diff --git a/NAMESPACE b/NAMESPACE index e6b81df..6beef13 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -83,6 +83,7 @@ importFrom(ggplot2,geom_smooth) importFrom(ggplot2,geom_text) importFrom(ggplot2,ggplot) importFrom(ggplot2,ggtitle) +importFrom(ggplot2,labs) importFrom(ggplot2,theme) importFrom(ggplot2,theme_bw) importFrom(ggplot2,theme_minimal) diff --git a/R/plotting.R b/R/plotting.R index a304093..227c7e4 100644 --- a/R/plotting.R +++ b/R/plotting.R @@ -397,7 +397,7 @@ plot_weather_compare <- function(meteo_lst1, meteo_lst2, par, period = "day", fn #' @param title character, information to be added in figure title #' @importFrom dplyr %>% filter select mutate bind_rows #' @importFrom tidyr pivot_longer -#' @importFrom ggplot2 ggplot geom_bar facet_wrap ggtitle aes theme_minimal +#' @importFrom ggplot2 ggplot geom_bar facet_wrap ggtitle aes theme_minimal labs #' @return ggplot figure of bar plots #' @export #' @@ -447,10 +447,10 @@ plot_wgn_comparison <- function(meteo_lst1, meteo_lst2, station1, station2, type mutate(source = type2) ##Bar plot - fig <- ggplot(bind_rows(df1,df2), aes(x = month, y = values, fill = source))+ + fig <- ggplot(bind_rows(df1,df2), aes(x = as.factor(month), y = values, fill = source))+ geom_bar(stat='identity', position = "dodge")+ facet_wrap(~par, scales = "free")+ - ggtitle(paste(type1, "vs.", type2, title))+ + labs(title=paste(type1, "vs.", type2, title), x ="Months", y = "Values")+ theme_minimal() return(fig) diff --git a/docs/404.html b/docs/404.html index bec9415..31bb017 100644 --- a/docs/404.html +++ b/docs/404.html @@ -51,6 +51,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 6c664fc..d1ddd02 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/articles/calibration.html b/docs/articles/calibration.html index 8d524f5..c2edd0b 100644 --- a/docs/articles/calibration.html +++ b/docs/articles/calibration.html @@ -53,6 +53,7 @@ Land use Soil parameters Weather data + Climate data
  • @@ -84,26 +85,26 @@ - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + +

    Calibration data

    @@ -195,14 +196,14 @@

    Timeseries
     plot_cal_data(cal_data$data, c("3","10"))

    -
    -

    For data rich monitoring station plot_cal_data function should +

    +

    For data rich monitoring station plot_cal_data function should be used only with single station selected. Such plotting allows better visualization.

     plot_cal_data(cal_data$data, c("4"))
    -
    - +
    +

    Monthly summary @@ -214,8 +215,8 @@

    Monthly summary 
     plot_monthly(cal_data$data, station = "4")
    -
    - +
    +

    Fractions @@ -257,8 +258,8 @@

    Maps reach <- st_transform(st_read(reach_path, quiet = TRUE), 4326) basin <-st_transform(st_read(basin_path, quiet = TRUE), 4326) plot_map(cal_data$data, cal_data$stations, reach, basin)

    -
    - +
    +
  • diff --git a/docs/articles/fig_basin.png b/docs/articles/fig_basin.png new file mode 100644 index 0000000..f0caca8 Binary files /dev/null and b/docs/articles/fig_basin.png differ diff --git a/vignettes/cloud.png b/docs/articles/fig_cloud.png similarity index 100% rename from vignettes/cloud.png rename to docs/articles/fig_cloud.png diff --git a/docs/articles/fig_temp.png b/docs/articles/fig_temp.png new file mode 100644 index 0000000..b4f7e75 Binary files /dev/null and b/docs/articles/fig_temp.png differ diff --git a/docs/articles/fig_wng.png b/docs/articles/fig_wng.png new file mode 100644 index 0000000..3aa46f1 Binary files /dev/null and b/docs/articles/fig_wng.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index 7e37345..28a9e33 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • @@ -67,6 +68,8 @@

    All vignettes

    Weather data
    +
    Climate data
    +
    diff --git a/docs/articles/land_use.html b/docs/articles/land_use.html index 3141227..5db4ea5 100644 --- a/docs/articles/land_use.html +++ b/docs/articles/land_use.html @@ -53,6 +53,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/articles/land_use_files/figure-html/lu_pts-1.png b/docs/articles/land_use_files/figure-html/lu_pts-1.png index 87032dc..a166f82 100644 Binary files a/docs/articles/land_use_files/figure-html/lu_pts-1.png and b/docs/articles/land_use_files/figure-html/lu_pts-1.png differ diff --git a/docs/articles/soil.html b/docs/articles/soil.html index 1a93db4..1076a5c 100644 --- a/docs/articles/soil.html +++ b/docs/articles/soil.html @@ -53,6 +53,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/articles/weather.html b/docs/articles/weather.html index 721d84b..00bd480 100644 --- a/docs/articles/weather.html +++ b/docs/articles/weather.html @@ -53,6 +53,7 @@ Land use Soil parameters Weather data + Climate data
  • @@ -147,8 +148,8 @@

    Plotting 
     plot_weather(met_lst, "PCP", "month", "sum")
    -
    -

    Another function plot_weather_compare +

    +

    Another function plot_weather_compare allows comparing two meteorological datasets of same time scale. Potential examples of its usage could be different. One could be comparing different datasets in order to decide, which one should be @@ -177,8 +178,8 @@

    Plotting## [1] "Loading of data is finished." 
     plot_weather_compare(met_lst, met_lst2, "PCP", "month", "mean", "clean", "raw")
    -
    - +
    +

    Interpolating @@ -203,7 +204,7 @@

    Interpolatingbasin <-st_transform(st_read(basin_path), 4326) %>% mutate(NAME = "Basin")

    ## Reading layer `basin' from data source 
-##   `C:\Users\laptop\AppData\Local\Temp\Rtmpa2dTAZ\temp_libpath1bf81946cd3\svatools\extdata\GIS\basin.shp' 
+##   `C:\Users\laptop\AppData\Local\R\win-library\4.2\svatools\extdata\GIS\basin.shp' 
 ##   using driver `ESRI Shapefile'
 ## Simple feature collection with 1 feature and 1 field
 ## Geometry type: POLYGON
@@ -213,8 +214,8 @@ 
    Interpolating
     stations <- st_transform(met_lst$stations, 4326)
 mapview(stations) + mapview(basin)
    
-
    
-
    For such need function interpolate was prepared. It
+
    
+
    For such need function interpolate was prepared. It
 allows to do interpolation between data in presented stations.
 Function’s grid_spacing parameter defines, how dense spatial
 interpolation points will be. Interpolate function creates virtual
diff --git a/docs/authors.html b/docs/authors.html
index c992f60..ba12e5a 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -29,6 +29,7 @@
     Land use
     Soil parameters
     Weather data
+    Climate data

  • diff --git a/docs/index.html b/docs/index.html index 5df26ae..fdd82d6 100644 --- a/docs/index.html +++ b/docs/index.html @@ -53,6 +53,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/news/index.html b/docs/news/index.html index 66db17a..fa28165 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index c3f3867..bca56d2 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -7,5 +7,6 @@ articles: land_use: land_use.html soil: soil.html weather: weather.html -last_built: 2023-04-12T12:58Z + climate: climate.html +last_built: 2023-04-13T13:20Z diff --git a/docs/reference/add_atmo_dep.html b/docs/reference/add_atmo_dep.html index 67ac845..7034c50 100644 --- a/docs/reference/add_atmo_dep.html +++ b/docs/reference/add_atmo_dep.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/add_missing_pcp_zero.html b/docs/reference/add_missing_pcp_zero.html index ecdf332..1298f26 100644 --- a/docs/reference/add_missing_pcp_zero.html +++ b/docs/reference/add_missing_pcp_zero.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/add_weather.html b/docs/reference/add_weather.html index 42812ca..72f08f2 100644 --- a/docs/reference/add_weather.html +++ b/docs/reference/add_weather.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/clean_outliers.html b/docs/reference/clean_outliers.html index e81a277..d29ba05 100644 --- a/docs/reference/clean_outliers.html +++ b/docs/reference/clean_outliers.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/clean_wq.html b/docs/reference/clean_wq.html index 6dcea3d..0c53dbb 100644 --- a/docs/reference/clean_wq.html +++ b/docs/reference/clean_wq.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/df_t.html b/docs/reference/df_t.html index 9a8dc37..e0ea8f2 100644 --- a/docs/reference/df_t.html +++ b/docs/reference/df_t.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/df_to_txt.html b/docs/reference/df_to_txt.html index c4b6369..53911e2 100644 --- a/docs/reference/df_to_txt.html +++ b/docs/reference/df_to_txt.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/extract_rotation.html b/docs/reference/extract_rotation.html index e6fe374..8a6e4be 100644 --- a/docs/reference/extract_rotation.html +++ b/docs/reference/extract_rotation.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/fill_with_closest.html b/docs/reference/fill_with_closest.html index cf83a9d..57e0d03 100644 --- a/docs/reference/fill_with_closest.html +++ b/docs/reference/fill_with_closest.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_atmo_dep.html b/docs/reference/get_atmo_dep.html index bb0a286..04f3932 100644 --- a/docs/reference/get_atmo_dep.html +++ b/docs/reference/get_atmo_dep.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_data_to_interpolate.html b/docs/reference/get_data_to_interpolate.html index b7534da..73c9a5c 100644 --- a/docs/reference/get_data_to_interpolate.html +++ b/docs/reference/get_data_to_interpolate.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_dates.html b/docs/reference/get_dates.html index 4d0a146..3c10643 100644 --- a/docs/reference/get_dates.html +++ b/docs/reference/get_dates.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_grid.html b/docs/reference/get_grid.html index 6e5d233..60d9d22 100644 --- a/docs/reference/get_grid.html +++ b/docs/reference/get_grid.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_hsg.html b/docs/reference/get_hsg.html index dce3bc3..612415c 100644 --- a/docs/reference/get_hsg.html +++ b/docs/reference/get_hsg.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_interpolated_data.html b/docs/reference/get_interpolated_data.html index 24b331e..0f7e937 100644 --- a/docs/reference/get_interpolated_data.html +++ b/docs/reference/get_interpolated_data.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_interpolation.html b/docs/reference/get_interpolation.html index 5fb6266..501f4ba 100644 --- a/docs/reference/get_interpolation.html +++ b/docs/reference/get_interpolation.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_lu_points.html b/docs/reference/get_lu_points.html index d642d9a..97d8df0 100644 --- a/docs/reference/get_lu_points.html +++ b/docs/reference/get_lu_points.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_nb_st_with_data.html b/docs/reference/get_nb_st_with_data.html index 48950db..c796300 100644 --- a/docs/reference/get_nb_st_with_data.html +++ b/docs/reference/get_nb_st_with_data.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_soil_parameters.html b/docs/reference/get_soil_parameters.html index 64b0250..9ea52ca 100644 --- a/docs/reference/get_soil_parameters.html +++ b/docs/reference/get_soil_parameters.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/get_usersoil_table.html b/docs/reference/get_usersoil_table.html index 24f3144..9987c60 100644 --- a/docs/reference/get_usersoil_table.html +++ b/docs/reference/get_usersoil_table.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/hide_show.html b/docs/reference/hide_show.html index 9282b10..5f4d89f 100644 --- a/docs/reference/hide_show.html +++ b/docs/reference/hide_show.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/index.html b/docs/reference/index.html index 741e3f2..eefbfbf 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/interpolate.html b/docs/reference/interpolate.html index da6ffd3..c41993b 100644 --- a/docs/reference/interpolate.html +++ b/docs/reference/interpolate.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/libs/image-Stations-0.0.1/file52f4769e32a5.png b/docs/reference/libs/image-Stations-0.0.1/file24d412101ce4.png similarity index 100% rename from docs/reference/libs/image-Stations-0.0.1/file52f4769e32a5.png rename to docs/reference/libs/image-Stations-0.0.1/file24d412101ce4.png diff --git a/docs/reference/libs/image-Stations-0.0.1/file52f4101367d7.png b/docs/reference/libs/image-Stations-0.0.1/file24d415d317da.png similarity index 100% rename from docs/reference/libs/image-Stations-0.0.1/file52f4101367d7.png rename to docs/reference/libs/image-Stations-0.0.1/file24d415d317da.png diff --git a/docs/reference/libs/image-Stations-0.0.1/file52f46df1bdc.png b/docs/reference/libs/image-Stations-0.0.1/file24d4160749f5.png similarity index 100% rename from docs/reference/libs/image-Stations-0.0.1/file52f46df1bdc.png rename to docs/reference/libs/image-Stations-0.0.1/file24d4160749f5.png diff --git a/docs/reference/libs/image-Stations-0.0.1/file52f4183b749e.png 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  • diff --git a/docs/reference/load_climate.html b/docs/reference/load_climate.html index 7dbc8f8..0e61620 100644 --- a/docs/reference/load_climate.html +++ b/docs/reference/load_climate.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/load_climate_lst.html b/docs/reference/load_climate_lst.html index 580e6be..bd9134e 100644 --- a/docs/reference/load_climate_lst.html +++ b/docs/reference/load_climate_lst.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/load_stations.html b/docs/reference/load_stations.html index 205b037..8198738 100644 --- a/docs/reference/load_stations.html +++ b/docs/reference/load_stations.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/load_template.html b/docs/reference/load_template.html index 6f843b6..8e7d8fe 100644 --- a/docs/reference/load_template.html +++ b/docs/reference/load_template.html @@ -33,6 +33,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/min_ks.html b/docs/reference/min_ks.html index 8074e15..54aeba0 100644 --- a/docs/reference/min_ks.html +++ b/docs/reference/min_ks.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/my.check.pwd.html b/docs/reference/my.check.pwd.html index 80dd4ef..4fcd813 100644 --- a/docs/reference/my.check.pwd.html +++ b/docs/reference/my.check.pwd.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/my.check.pww.html b/docs/reference/my.check.pww.html index 8a1baec..af05184 100644 --- a/docs/reference/my.check.pww.html +++ b/docs/reference/my.check.pww.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/my.pcpd.html b/docs/reference/my.pcpd.html index ed0581d..623de26 100644 --- a/docs/reference/my.pcpd.html +++ b/docs/reference/my.pcpd.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/my.pcpmhhr.html b/docs/reference/my.pcpmhhr.html index bd13a95..7bb8ed2 100644 --- a/docs/reference/my.pcpmhhr.html +++ b/docs/reference/my.pcpmhhr.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/my.pwd.html b/docs/reference/my.pwd.html index 09fd72d..b169f3c 100644 --- a/docs/reference/my.pwd.html +++ b/docs/reference/my.pwd.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/my.pww.html b/docs/reference/my.pww.html index d9f5788..5f72650 100644 --- a/docs/reference/my.pww.html +++ b/docs/reference/my.pww.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/my.skew.html b/docs/reference/my.skew.html index 71bf14a..6560179 100644 --- a/docs/reference/my.skew.html +++ b/docs/reference/my.skew.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/nyears.html b/docs/reference/nyears.html index 3b78c00..729d6f8 100644 --- a/docs/reference/nyears.html +++ b/docs/reference/nyears.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/plot_cal_data.html b/docs/reference/plot_cal_data.html index 96f4a22..d709e5e 100644 --- a/docs/reference/plot_cal_data.html +++ b/docs/reference/plot_cal_data.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • @@ -93,8 +94,8 @@

    Examples#> [1] "Loading of data is finished." plot_cal_data(cal_data$data, stations = c("1", "2", "3","10"), variables = c("PT", "Q")) -
    -     +
    + diff --git a/docs/reference/plot_fractions.html b/docs/reference/plot_fractions.html index 6cf3058..d5a6544 100644 --- a/docs/reference/plot_fractions.html +++ b/docs/reference/plot_fractions.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data

  • diff --git a/docs/reference/plot_map.html b/docs/reference/plot_map.html index 91264f5..2de9c06 100644 --- a/docs/reference/plot_map.html +++ b/docs/reference/plot_map.html @@ -2,7 +2,7 @@ Preparing interactive map of monitoring points — plot_map • svatools + Skip to contents @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • @@ -101,7 +102,7 @@

    Examples#> [1] "Loading of data is finished." reach <- st_transform(st_read(reach_path), 4326) #> Reading layer `reaches' from data source -#> `C:\Users\laptop\AppData\Local\Temp\Rtmpa2dTAZ\temp_libpath1bf81946cd3\svatools\extdata\GIS\reaches.shp' +#> `C:\Users\laptop\AppData\Local\R\win-library\4.2\svatools\extdata\GIS\reaches.shp' #> using driver `ESRI Shapefile' #> Simple feature collection with 125 features and 2 fields #> Geometry type: LINESTRING @@ -110,7 +111,7 @@

    Examples#> Projected CRS: ETRF2000-PL / CS92 basin <-st_transform(st_read(basin_path), 4326) #> Reading layer `basin' from data source -#> `C:\Users\laptop\AppData\Local\Temp\Rtmpa2dTAZ\temp_libpath1bf81946cd3\svatools\extdata\GIS\basin.shp' +#> `C:\Users\laptop\AppData\Local\R\win-library\4.2\svatools\extdata\GIS\basin.shp' #> using driver `ESRI Shapefile' #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON @@ -118,8 +119,8 @@

    Examples#> Bounding box: xmin: 4898668 ymin: 3304508 xmax: 4915266 ymax: 3319644 #> Projected CRS: ETRS89-extended / LAEA Europe plot_map(cal_data$data, cal_data$stations, reach, basin) -
    - +
    + diff --git a/docs/reference/plot_monthly.html b/docs/reference/plot_monthly.html index e2f42f3..40ecae1 100644 --- a/docs/reference/plot_monthly.html +++ b/docs/reference/plot_monthly.html @@ -29,6 +29,7 @@     Land use Soil parameters Weather data + Climate data

  • @@ -92,8 +93,8 @@

    Examples#> [1] "Loading data from template." #> [1] "Loading of data is finished." plot_monthly(cal_data$data, station = "4") -
    - +
    +

  • diff --git a/docs/reference/plot_weather.html b/docs/reference/plot_weather.html index c19fba2..c06e2c6 100644 --- a/docs/reference/plot_weather.html +++ b/docs/reference/plot_weather.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • @@ -117,8 +118,8 @@

    Examples#> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors -
    - +
    + diff --git a/docs/reference/plot_weather_compare.html b/docs/reference/plot_weather_compare.html index da4a3ac..b10f58d 100644 --- a/docs/reference/plot_weather_compare.html +++ b/docs/reference/plot_weather_compare.html @@ -29,6 +29,7 @@     Land use Soil parameters Weather data + Climate data

  • @@ -168,8 +169,8 @@

    Examples#> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors -
    - +
    + diff --git a/docs/reference/plot_wgn_comparison.html b/docs/reference/plot_wgn_comparison.html index 6e96cc6..cedd3d0 100644 --- a/docs/reference/plot_wgn_comparison.html +++ b/docs/reference/plot_wgn_comparison.html @@ -29,6 +29,7 @@     Land use Soil parameters Weather data + Climate data

  • diff --git a/docs/reference/prepare_climate.html b/docs/reference/prepare_climate.html index 6fe1f20..992f33b 100644 --- a/docs/reference/prepare_climate.html +++ b/docs/reference/prepare_climate.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/prepare_wgn.html b/docs/reference/prepare_wgn.html index a34f811..39e5b19 100644 --- a/docs/reference/prepare_wgn.html +++ b/docs/reference/prepare_wgn.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/read_tbl.html b/docs/reference/read_tbl.html index 81f58e9..bf10aba 100644 --- a/docs/reference/read_tbl.html +++ b/docs/reference/read_tbl.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/sp.na.omit.html b/docs/reference/sp.na.omit.html index 8bb76a3..233f808 100644 --- a/docs/reference/sp.na.omit.html +++ b/docs/reference/sp.na.omit.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/transform_to_list.html b/docs/reference/transform_to_list.html index 0f1683e..867d9ab 100644 --- a/docs/reference/transform_to_list.html +++ b/docs/reference/transform_to_list.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/update_wst_id.html b/docs/reference/update_wst_id.html index ddc9fa7..cbadeac 100644 --- a/docs/reference/update_wst_id.html +++ b/docs/reference/update_wst_id.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/update_wst_txt.html b/docs/reference/update_wst_txt.html index 533b6b4..77a3a2e 100644 --- a/docs/reference/update_wst_txt.html +++ b/docs/reference/update_wst_txt.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/usersoil_to_sol.html b/docs/reference/usersoil_to_sol.html index 6fa22f3..c8c319e 100644 --- a/docs/reference/usersoil_to_sol.html +++ b/docs/reference/usersoil_to_sol.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/reference/write_weather_template.html b/docs/reference/write_weather_template.html index 1aa0237..4e1e23b 100644 --- a/docs/reference/write_weather_template.html +++ b/docs/reference/write_weather_template.html @@ -29,6 +29,7 @@ Land use Soil parameters Weather data + Climate data
  • diff --git a/docs/search.json b/docs/search.json index f745b89..ca04f8a 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Svajunas Plunge. Maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Plunge, Svajunas Rets, Ekaterina (2023), R package aid SWAT+ preparation R package. URL https://biopsichas.github.io/svatools/ 10.5281/zenodo.7296034.","code":"@Manual{, title = {svatools - tools for SWAT+ input data preparation, visualization and model output assessment}, author = {Svajunas Plunge and Ekaterina Rets}, year = {2023}, note = {R package}, url = {https://biopsichas.github.io/svatools/}, doi = {10.5281/zenodo.7296034}, }"},{"path":[]},{"path":"/index.html","id":"svatools","dir":"","previous_headings":"","what":"svatools","title":"SWAT+ input data preparation tool","text":"goal svatools help SWAT+ model input data preparation. mostly functions, developed implementation modeling tasks OPTAIN project. tools intended fill gaps SWAT+ workflow along side main tools developed Christoph Schuerz. Therefore, highly recommend trying using tools: SWATfarmR - R tool preparing management schedules SWAT model; SWATplusR - R tool sensitivity analyse, model calibration validation; SWATbuildR1 - R tool building SWAT+ setups; SWATdoctR2 - collection functions R routines SWAT model calibration model diagnostics. Detailed information packages, workflow steps, input data, SWAT+ parameters, model calibration, validation, etc., found SWAT+ modeling protocol.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"SWAT+ input data preparation tool","text":"can install development version svatools GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"biopsichas/svatools\") # euptf2 package is needed and used for soil parameter functions devtools::install_github(\"tkdweber/euptf2\")"},{"path":"/index.html","id":"data","dir":"","previous_headings":"","what":"Data","title":"SWAT+ input data preparation tool","text":"data required run test package installed package extdata folder. Exact location computer found running lines . Please run system get .","code":"library(svatools) temp_path <- system.file(\"extdata\", package = \"svatools\") print(temp_path) #> [1] \"C:/Users/laptop/AppData/Local/R/win-library/4.2/svatools/extdata\""},{"path":"/index.html","id":"templates","dir":"","previous_headings":"","what":"Templates","title":"SWAT+ input data preparation tool","text":"order use svatools package functions data prepare data inline templates provided extdata folder. : calibration_data.xlsx - template loading calibration (water flow water quality variables) data. weather_data.xlsx - template loading weather variables. soil_lookup.xlsx - example loading soil parameters dataset. GIS/ - folder GIS layers needed run functions. CORDEX-BC/ - folder example climate data. Data prepared according templates can directly loaded R functions applied described.","code":""},{"path":"/articles/calibration.html","id":"loading","dir":"Articles","previous_headings":"","what":"Loading","title":"Calibration data","text":"data prepared according given templates, can loaded directly package function load_template function. Two data types loaded. First station information (location, name, ID). Second, values monitoring values IDs relate station, variable names, dates values.","code":"library(svatools) temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path, 4326) ## [1] \"Loading data from template.\" ## [1] \"Loading of data is finished.\" str(cal_data) ## List of 2 ## $ stations: sf [20 × 6] (S3: sf/tbl_df/tbl/data.frame) ## ..$ ID : chr [1:20] \"5\" \"8\" \"4\" \"2\" ... ## ..$ Name : chr [1:20] \"Zgłowiączka-Strózewo-Parcele\" \"DopZStarRadziejewa - Witowo\" \"Zgłąwiączka-ponizej. Osiecin,Samszyce\" \"Zgłowiączka-pow.Osiecin. Piołunowo\" ... ## ..$ Description: chr [1:20] \"powyżej jez. Głuszyńskiego (60 km)\" \"Kolonia Witowo\" \"Samszyce - poniżej Osięcin (67,8 km)\" \"powyżej Osięcin (75,2 km)\" ... ## ..$ geometry :sfc_POINT of length 20; first list element: 'XY' num [1:2] 18.7 52.6 ## ..$ Long : num [1:20] 18.7 18.7 18.7 18.7 18.7 ... ## ..$ Lat : num [1:20] 52.6 52.6 52.6 52.6 52.6 ... ## ..- attr(*, \"sf_column\")= chr \"geometry\" ## ..- attr(*, \"agr\")= Factor w/ 3 levels \"constant\",\"aggregate\",..: NA NA NA NA NA ## .. ..- attr(*, \"names\")= chr [1:5] \"ID\" \"Name\" \"Description\" \"Long\" ... ## $ data : tibble [22,306 × 5] (S3: tbl_df/tbl/data.frame) ## ..$ Station : chr [1:22306] \"1\" \"1\" \"1\" \"1\" ... ## ..$ DATE : POSIXct[1:22306], format: \"2008-03-11\" \"2008-03-25\" ... ## ..$ Variables: chr [1:22306] \"N-NH4\" \"N-NH4\" \"N-NH4\" \"N-NH4\" ... ## ..$ Values : num [1:22306] 0.05 0.05 0.05 0.05 0.05 0.05 0.83 0.25 0.51 1.89 ... ## ..$ Source : chr [1:22306] \"grab sample\" \"grab sample\" \"grab sample\" \"grab sample\" ..."},{"path":"/articles/calibration.html","id":"cleaning","dir":"Articles","previous_headings":"","what":"Cleaning","title":"Calibration data","text":"Two functions applied data cleaning. First clean_wq function, applied fixing common water data issues fixing data formats, units (e.g. NO3, N-NO3), instead LOD/LOQ values using LOD LOQ divided 2, replacing zeros water quality variables minimum value (multiplied selected coefficient) variable. Second function clean_outliers allows removal suspicious values defined outside selected range (mean - standard deviation; mean + standard deviation).","code":"##Zeros is replaced with min(Value)/2 cal_data$data <- clean_wq(cal_data$data, 0.5) lst <- clean_outliers(cal_data$data) ##Looking at data to be removed print(head(lst$dropped)) ## # A tibble: 6 × 5 ## Station DATE Variables Values Source ## ## 1 10 2012-12-11 00:00:00 N-NH4 16.8 grab sample ## 2 10 2016-02-04 00:00:00 N-NH4 6 grab sample ## 3 10 2016-03-07 00:00:00 N-NH4 4.67 grab sample ## 4 10 2016-04-04 00:00:00 N-NH4 11.1 grab sample ## 5 10 2020-01-27 00:00:00 N-NH4 20.1 grab sample ## 6 10 2013-10-07 00:00:00 N-NO2 0.837 grab sample ##Updating data cal_data$data <- lst$newdf"},{"path":"/articles/calibration.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"Calibration data","text":"Plotting valuable tool examine data identify potential problems.","code":""},{"path":"/articles/calibration.html","id":"timeseries","dir":"Articles","previous_headings":"Plotting","what":"Timeseries","title":"Calibration data","text":"several ways package used plot loaded calibration data. Data multiple stations plotted using plot_cal_data function. way function used stations relatively data points screen data coverage potential problems. data rich monitoring station plot_cal_data function used single station selected. plotting allows better visualization.","code":"plot_cal_data(cal_data$data, c(\"3\",\"10\")) plot_cal_data(cal_data$data, c(\"4\"))"},{"path":"/articles/calibration.html","id":"monthly-summary","dir":"Articles","previous_headings":"Plotting","what":"Monthly summary","title":"Calibration data","text":"Sometimes evaluation data quality monthly plots can useful. plots allow see monitoring results corresponds data sources processes, taking place monitored catchment. plot_monthly function can used plot monthly aggregates.","code":"plot_monthly(cal_data$data, station = \"4\")"},{"path":"/articles/calibration.html","id":"fractions","dir":"Articles","previous_headings":"Plotting","what":"Fractions","title":"Calibration data","text":"Possible problems observed plotting mineral total parts nutrients changes. function . plot_fractions used nitrogen phosphorus. Function provides monthly regression monthly fraction figures. Example function use nitrogen. Example function use nitrogen.","code":"plot_fractions(cal_data$data, station = c(\"4\"), c(\"NT\"), c(\"N-NO3\", \"N-NH4\", \"N-NO2\")) ## $regression ## ## $fraction plot_fractions(cal_data$data, station = c(\"4\"), c(\"PT\"), c(\"P-PO4\")) ## $regression ## ## $fraction"},{"path":"/articles/calibration.html","id":"maps","dir":"Articles","previous_headings":"Plotting","what":"Maps","title":"Calibration data","text":"last function package plotting calibration data plot_map. function allows plot catchment boundary, monitoring stations monitoring data within station (press monitoring station). allow examination spatial temporal dimensions existing data time.","code":"library(sf) ##Loading and converting coordinate system of GIS data. EPSG 4326 coordinate system should be used to get right plot. reach_path <- system.file(\"extdata\", \"GIS/reaches.shp\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") reach <- st_transform(st_read(reach_path, quiet = TRUE), 4326) basin <-st_transform(st_read(basin_path, quiet = TRUE), 4326) plot_map(cal_data$data, cal_data$stations, reach, basin)"},{"path":"/articles/deposition.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Atmospheric deposition","text":"According Grizzetti et al. (2005) study done three catchments Europe atmospheric deposition constituted 3 16% total nitrogen inputs. Helsinki Commission (2018) report indicates around 30% nitrogen load Baltic Sea comes atmospheric deposition, contributes 1 10% nitrogen load inland surface waters sea surrounding countries. Therefore setting atmospheric deposition appropriate level help better constrain parameters responsible nitrogen transport processes catchment. SWAT+ considers nitrogen deposition (NH4 NO3), phosphorous deposition. wet dry deposition taken account. Wet deposition absorption compounds rain snow fall expressed \\(mg/l\\), whereas dry deposition direct adsorption compounds water land surfaces, expressed \\(kg/ha/year\\). SWAT+ allows constant, annual monthly input data atmospheric deposition. choice controlled atmo parameter ‘codes.bsn’ file.","code":""},{"path":"/articles/deposition.html","id":"emep-data","dir":"Articles","previous_headings":"","what":"EMEP data","title":"Atmospheric deposition","text":"Nitrogen atmospheric obtained local sources. However, local data available parameters available, deposition data can extracted European Monitoring Evaluation Programme (EMEP) provided MSC-W model data files. EMEP portal provides gridded 0.1\\(^{\\circ}\\) resolution netCDF data Europe. MSC-W model grid covers territory -30 90 longitude 30 82 latitude 50x50 km2 grid cell. parameters, needed SWAT+ model : DDEP_OXN_m2GridDry - deposition oxidized nitrogen per m2 grid (\\(mgN/m^2\\)); WDEP_OXN - wet deposition oxidized nitrogen (\\(mgN/m^2\\)); DDEP_RDN_m2Grid - dry deposition reduced nitrogen per m2 grid (\\(mgN/m^2\\)); WDEP_RDN - wet deposition reduced nitrogen (\\(mgN/m^2\\)). WDEP_PREC - precipitation (\\(mm\\)). parameters can used prepare following SWAT+ parameters required ‘atmo.cli’ input file: NH4_RF - ammonia rainfall (\\(mg/l\\)); NO3_RF - nitrate rainfall (\\(mg/l\\)); NH4_DRY - ammonia dry deposition (\\(kg/ha/yr\\)); NO3_DRY - nitrate dry deposition (\\(kg/ha/yr\\)). EMEP provides access yearly, monthly, daily hourly data. Data selected catchment required time step extracted directly EMEP website get_atmo_dep function. Function requires just path catchment boundary shape file internet connection. Depending data time step selected (yearly monthly), function need less time run (less “year” selected, “month”). information inclusion atmospheric deposition data SWAT+ modeling presented section 3.10 SWAT+ modelling protocol.","code":"library(svatools) library(tidyverse) basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") df <- get_atmo_dep(basin_path) ##Plot results ggplot(pivot_longer(df, !DATE, names_to = \"par\", values_to = \"values\"), aes(x = DATE, y = values))+ geom_line()+ facet_wrap(~par, scales = \"free_y\")+ theme_bw()"},{"path":"/articles/deposition.html","id":"swat-input","dir":"Articles","previous_headings":"","what":"SWAT+ input","title":"Atmospheric deposition","text":"Atmospheric deposition data SWAT+ model can included via ‘atmodep.cli’ file. add_atmo_dep function designed. write yearly, monthly whole modeled period averages. Function requires get_atmo_dep function output yearly monthly data. SWAT+ model setup .sqlite database updated . Thus, function updates also required tables. case average yearly atmospheric deposition data used, t_ext function parameter provided. Please read add_atmo_dep.","code":"db_path <- \"./output/test/project.sqlite\" add_atmo_dep(df, db_path)"},{"path":"/articles/land_use.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Land use","text":"Preparation land use data SWATbuildR1 among difficult tasks requires collection combination multiple GIS datasets (crop land, tile drain extent, Natural/Small Water Retention Measures (NSWRMs) installation sites, etc.). Moreover, also requires putting lot efforts cleaning topological issues prepared layer. However, even difficult task prepare land use time series data required preparation management schedules necessary SWAT+ model. page provide tools (links ) workflow help task. Particular focus paid preparing land use time series data (local data available) remote sensing sources required SWATfarmR tool.","code":""},{"path":"/articles/land_use.html","id":"land-use-layer","dir":"Articles","previous_headings":"","what":"Land use layer","title":"Land use","text":"Merging different data sets, cleaning topological errors preparing final layer directly usable SWATbuildR quite time consuming task. can done Python script prepared task. script example data available zenodo page. arcpy library required ArcGIS Advanced license. order run scrip following GIS datasets collected: General land cover covering area (raster); Catchment boundary (polygon); Crop fields (polygon); River channels (line); Tile drain extent (polygon); Measure extent (polygon)2. GIS layers adjusted fit structure example data script adjusted fit input data structure. successfully running script resulting data layer inline SWATbuildR requirements (please see SWAT+ modeling protocol sections 2.2 2.3). example data structure provided .","code":"##Loading libraries library(svatools) library(tidyverse) library(sf) ##Loading data lu_path <- system.file(\"extdata\", \"GIS/lu_layer.shp\", package = \"svatools\") lu <- st_read(lu_path, quiet = TRUE) ##Adding IDs to land use field data and selected required columns lu <- mutate(lu, id = row_number()) %>% select(id, type, drainage, measures) ##Printing structure of the data str(lu) ## Classes 'sf' and 'data.frame': 10442 obs. of 5 variables: ## $ id : int 1 2 3 4 5 6 7 8 9 10 ... ## $ type : chr \"wwht\" \"wwht\" \"corn\" \"wwht\" ... ## $ drainage: int 0 0 0 0 0 0 0 0 0 0 ... ## $ measures: chr NA NA NA NA ... ## $ geometry:sfc_POLYGON of length 10442; first list element: List of 1 ## ..$ : num [1:16, 1:2] 479720 479710 479697 479698 479669 ... ## ..- attr(*, \"class\")= chr [1:3] \"XY\" \"POLYGON\" \"sfg\" ## - attr(*, \"sf_column\")= chr \"geometry\" ## - attr(*, \"agr\")= Factor w/ 3 levels \"constant\",\"aggregate\",..: NA NA NA NA ## ..- attr(*, \"names\")= chr [1:4] \"id\" \"type\" \"drainage\" \"measures\""},{"path":"/articles/land_use.html","id":"remote-sensing","dir":"Articles","previous_headings":"","what":"Remote sensing","title":"Land use","text":"SWAT+ model important capabilities simulate impacts agriculture water flows water quality water bodies. However, requires information agricultural activities selected area. One important crop rotation data. Yet, information rarely available freely accessible. cases remote sensing data used generate information crops selected time period. information provided SWAT+ modeling protocol section 4.1.1).","code":""},{"path":"/articles/land_use.html","id":"google-earth-script","dir":"Articles","previous_headings":"Remote sensing","what":"Google Earth script","title":"Land use","text":"framework OPTAIN project deliverable 3.2 (Szabó et al. 2022), Google Earth Engine-based script (Mészáros Szabó 2022) developed predict crop types random forest method based time series reflectance data Sentinel 1A 1B satellite radar images. Output script series crop maps starting year 2015 latest available year. Required input data: Catchment boundary; Training points; Parcel/field boundaries.","code":""},{"path":"/articles/land_use.html","id":"input-data","dir":"Articles","previous_headings":"Remote sensing","what":"Input data","title":"Land use","text":"Training data necessary classification algorithm. case crop types data available LUCAS: Land Use Coverage Area frame Survey data can used. case local data year 2021. used preparation training set. However, generating training set remote sensing algorithm good practice examine input data. example , areas land use/crop type calculated provided figure. general recommendation land use/crop types small areas eliminated workflow. provided example crop types areas occupying less 0.1% assigned general code “agrl”. Thus eliminated workflow.","code":"##Calculating areas in ha and % for land uses/crops lu_area <- lu[\"type\"] %>% mutate(area = st_area(geometry)) %>% st_drop_geometry() %>% group_by(type) %>% summarise_all(sum) %>% mutate(area = as.numeric(area/10000)) lu_area[\"Sum\"] <- sum(lu_area$area) lu_area[\"Area [%]\"] <- round(100*lu_area$area/lu_area$Sum, 2) ##Printing figure ggplot(lu_area, aes(x = reorder(type, `Area [%]`), y = `Area [%]`)) + geom_bar(stat=\"identity\", mapping = aes(fill = type))+ geom_text(aes(x = type, label = `Area [%]`), hjust = -0.1, size = 3)+ labs(y = \"area in %\", x = \"crops/land use\")+ ylim(0, 25) + coord_flip()+ theme_bw()+ theme(axis.text.y = element_text(size = 8)) ##Eliminating very small types lu$type[lu$type %in% c(\"past\", \"herb\", \"csil\")] <- \"agrl\""},{"path":"/articles/land_use.html","id":"training-data","dir":"Articles","previous_headings":"Remote sensing","what":"Training data","title":"Land use","text":"Remote sensing script requires training points information year land use/crop type. data can prepared get_lu_points function. function requires preparation lookup table land use assigned numeric code. Moreover, land use type forest, water, urban land uses also participating rotation. set aside. GIS layers prepared saved .shp files transferred Google Earth Engine. files can directly used classification script.","code":"##Preparation of lookup table lookup <- data.frame(lc1 = seq(1:length(unique(c(lu$type)))), type = unique(c(lu$type))) ##Setting, which land uses will not change lu_constant <- c(\"fesc\", \"orcd\", \"frst\", \"frse\", \"frsd\", \"urld\", \"urhd\", \"wetl\", \"watr\", \"agrl\") ##Obtaining training data pts <- get_lu_points(lu, 2021, lookup, lu_constant) ##Plotting points to be used in remote sensing algorithm training ggplot(pts %>% left_join(lookup, by = \"lc1\")) + geom_sf(mapping = aes(col = type)) + scale_color_brewer(palette = \"Paired\", name = \"Crop type\")+ theme_void() ##Preparing just the boundary data for fields parcels <- lu[\"id\"] %>% st_transform(4326) ##Preparing basin data output_path <- \"../output/\" basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") catchment <- st_read(basin_path, quiet = TRUE) %>% select() %>% mutate(name = \"Upper_Zglowiaczka\") %>% st_transform(4326) ##Writing all the necessary training data for remote sensing algorithm. st_write(pts, paste0(output_path, \"pts.shp\"), append = F) st_write(parcels, paste0(output_path, \"lu_prcl.shp\"), append = F) st_write(catchment, paste0(output_path, \"catchment.shp\"), append = F)"},{"path":"/articles/land_use.html","id":"rotation-extraction","dir":"Articles","previous_headings":"Remote sensing","what":"Rotation extraction","title":"Land use","text":"Running Google Earth Engine-based script prepared Mészáros Szabó (2022) generates .tif raster file bands year land use/crop types (represented numeric codes). One year example classification results provided . .tif file extract_rotation function can applied extract crop rotation information. information delivered format directly usable SWATfarmR input data preparation scripts, applied generate SWAT+ management crop rotation schedules. example complex rotations period 2015-2022 extracted field remote sensing classification results. Results saved new shape file. Attribute table example printed . Example prepared attribute table provided . fulfills requirements set SWAT+ modeling protocol section 4.1.1. Prepared attribute table can looped, longer time series crop rotation data needed. Following lines provides example time series extended. Additionally, write Final lines write input SWATbuildR.","code":"##Loading results for accuracy lu_rot <- extract_rotation(lu, 2015, \"cropmaps2015_2022.tif\", \"../output/\", lookup, lu_constant) ##Overwriting 2021 year classification results since actual data were available. ##Also removing results were land use should be constant lu_rot <- lu_rot %>% mutate(y_2021 = ifelse(!is.na(y_2021), type, NA)) %>% select(-type) %>% mutate_at(vars(starts_with(\"y_\")), ~ifelse(lu %in% lu_constant,NA,.)) ##Writing results for SWATfarmR input preparation functions st_write(lu_rot , paste0(output_path, \"lu_rot.shp\"), append = F) # write.csv(lu_rot %>% st_drop_geometry(), \"lu_rot.csv\", row.names = F) lu_rot_df <- read.csv(system.file(\"extdata\", \"lu_rot.csv\", package = \"svatools\")) print(head(lu_rot_df, 10)) ## lu y_2015 y_2016 y_2017 y_2018 y_2019 y_2020 y_2021 y_2022 ## 1 field_1 trit trit trit trit corn corn wwht wwht ## 2 field_2 wwht wwht wwht wwht wwht wwht wwht corn ## 3 field_3 canp canp corn alfa corn alfa corn corn ## 4 field_4 trit alfa corn corn mint trit wwht corn ## 5 field_5 alfa alfa alfa alfa mint mint wwht alfa ## 6 field_6 barl alfa corn onio barl corn corn trit ## 7 field_7 sgbt canp sgbt lett lett lett corn corn ## 8 field_8 wwht barl sgbt sgbt sgbt sgbt wwht barl ## 9 field_9 barl corn barl wwht mint mint mint mint ## 10 field_10 sgbt corn mint mint barl wwht mint mint i=1 df <- lu_rot ##Loop to extend time series while(i != 5){ df <- bind_cols(df, lu_rot_df[,c(2:9)]) i = i +1 } ##Write the column names and the final file colnames(df) <- c(\"lu\", paste0(\"y_\", seq(1983,2022)), \"geometry\") ##To get 33 years exact df <- df[-c(2:8)] st_write(df, paste0(output_path, \"lu_rot_final.shp\"), append = F) lu$type <- df$lu lu[lu$drainage == 0, \"drainage\"] <- NA ##Write land use input file for SWATbuildR st_write(st_make_valid(lu), paste0(output_path, \"land.shp\"), append = F)"},{"path":"/articles/NEWS.html","id":"svatools-0-0-2","dir":"Articles","previous_headings":"","what":"svatools 0.0.2","title":"","text":"add_weather(), clean_wq(), clean_outliers() get_hsg() functions added.","code":""},{"path":"/articles/NEWS.html","id":"svatools-0-0-1","dir":"Articles","previous_headings":"","what":"svatools 0.0.1","title":"","text":"first release svatools.","code":""},{"path":"/articles/soil.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Soil parameters","text":"Obtaining soil parameters SWAT+ model can difficult. Therefore package functions allows automatic preparation soil parameters. Important theoretical documentation equations presented section 3.4 SWAT+ modelling protocol. populating SWAT+ usersoil parameter table SOL_Z (depth layer), CLAY (percentage clay defined particles <2 \\(\\mu m\\)), SAND (percentage sand - 50–2000 \\(\\mu m\\)), SILT (percentage silt - 2–50 \\(\\mu m\\)) OC (SOL_CBN, soil organic carbon content %) parameters collected per separate soil type profile’s layer. parameters filled get_usersoil_table. assigning Hydrologic Soil Groups tile drainage, water level impervious layer depth data needed. preparation soil data parameters done using following workflow. However, depending available types data, different steps might needed. example Polish case study Upper Zglowiaczka OPTAIN project.","code":""},{"path":"/articles/soil.html","id":"loading-libraries-and-paths-to-data","dir":"Articles","previous_headings":"","what":"Loading libraries and paths to data","title":"Soil parameters","text":"Workflow presented needs 6 R packages. However, svatools euptf2 libraries required population soil parameter’s table get_usersoil_table presented Adding soil parameters user table step. Input step prepared various ways. presented just one way understand better work process develop soil parameters table.","code":"##Required library(svatools) library(euptf2) ##Optional library(sf) library(readxl) library(tidyverse) library(stars) ##Required ##Path to soil distribution shape file soil_path <- system.file(\"extdata\", \"GIS/soils.shp\", package = \"svatools\") ##Path to soil lookup initial parameters with SOL_Z, CLAY, SILT, OC parameters filled. lookup_path <- system.file(\"extdata\", \"soil_lookup.xlsx\", package = \"svatools\") ##Path to tile drainage shape file drainage_path <- system.file(\"extdata\", \"GIS/drained.shp\", package = \"svatools\") ##Optional ##Path to water level depth shape file water_l_path <- system.file(\"extdata\", \"GIS/water_level.shp\", package = \"svatools\") ##Path to DEM raster file DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") ##Path to catchment boundary shape file (which in this case used as impervious layer data) basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") ##Path to soil hydro groups (if available) hsg_path <- system.file(\"extdata\", \"GIS/hydgrp.shp\", package = \"svatools\")"},{"path":"/articles/soil.html","id":"preparing-input-data","dir":"Articles","previous_headings":"","what":"Preparing input data","title":"Soil parameters","text":"Lookup table contains two sheets. One granulometry (SAND, SILT, CLAY content) soil texture class. Second humus content different humus class soils (distributed layers), easily recalculated organic carbon content (division 1.72 coefficient). required GIS input data presented maps . Polygons impervious layer one three possible categories: “<50cm”, “50-100cm” “>100cm”. information available impervious layer depth assumed depth last soil layer reclassified one three categories. Water level depth also three categories: “<60cm”, “60-100cm” “>100cm”. information available soil types land use serve good proxy information. instance wet areas wetlands probably assigned “<60cm” class, areas dominant organic soils “60-100cm”, etc. Drainage status divided two: “Y” drained areas, “N” areas without working tile drains. database available, information existence drains might available drainage installation plans.","code":"##Reading granulometry lookup information for soil types into dataframe gran <- read_excel(lookup_path, sheet = \"SandSiltClay\") ##Print example of gran table print(head(gran, 3)) ## # A tibble: 3 × 4 ## `Texture EN` SAND SILT CLAY ## ## 1 CL 32.0 42.9 25.1 ## 2 L 52.3 29.4 18.2 ## 3 LS1 87.2 10.3 2.46 ##Reading humus lookup information in to dataframe and converting it into organic carbon %. humus <- read_excel(lookup_path, sheet = \"Humus\") %>% mutate(OC1 = `Humus 1st layer [%]`/1.72, OC2 = `Humus 2nd layer [%]`/1.72, OC3 = `Humus 3rd layer [%]`/1.72) %>% select(Humus, starts_with(\"OC\")) ##Print example of humus table print(head(humus, 3)) ## # A tibble: 3 × 4 ## Humus OC1 OC2 OC3 ## ## 1 1 1.88 0.942 0.174 ## 2 2 1.63 0.814 0.151 ## 3 3 1.26 0.628 0.116 ##Reading GIS layers for basin boundary, water level, drainage status, soils (with \"Code\" column here representing soil types) and soil hydrological groups. ##All data should be in one coordinate system. ##Impervious layer is prepared from basin layer as it is below 1 m whole catchment falls into one category. impervious<- st_read(basin_path, quiet = TRUE) %>% mutate(Impervious = \">100cm\") %>% st_transform(3035) water_level <- st_read(water_l_path, quiet = TRUE) %>% st_transform(3035) drainage <- st_read(drainage_path, quiet = TRUE) %>% st_transform(3035) soils <- st_read(soil_path, quiet = TRUE) %>% st_transform(3035) hsg <- st_read(hsg_path, quiet = TRUE) %>% st_transform(3035) ##Preparing maps for each GIS data p1 <- ggplot(impervious) + geom_sf(mapping = aes(fill = Impervious)) + theme_void() p2 <- ggplot(water_level) + geom_sf(mapping = aes(fill = Depth)) + theme_void() p3 <- ggplot(drainage) + geom_sf(mapping = aes(fill = Drained)) + theme_void() p4 <- ggplot(soils) + geom_sf(mapping = aes(fill = Code), show.legend = F) + theme_void() ##Combining into one cowplot::plot_grid(p1, p2, p3, p4, ncol = 2, labels = c('Impervious depth', 'Water level', 'Drainage status', 'Soil types'), label_x = 0, label_y = 0, hjust = -0.1, vjust = -0.5, label_size = 10)"},{"path":"/articles/soil.html","id":"preparing-final-soil-layer","dir":"Articles","previous_headings":"","what":"Preparing final soil layer","title":"Soil parameters","text":"final soil layer information added GIS layers soil types. instance example initial soil type code 4 elements separated “_“. looks S3_S3_SL3_3. First three shows texture class soil granulometry three layers last number humus class. intersection GIS layers information added soil code. Thus generating new soil type, information water, impervious layer depth, drainage status well. instance new code S3_S3_SL3_3_>100cm_60-100cm_Y. first four elements stay last three added depth impervious layers, depth water level drainage status classes. important point intersection GIS layers example increased number unique soil types 171 542 new soil type distribution GIS data created.","code":"##Printing example of soil codes before intersection print(head(soils %>% st_drop_geometry(), 3)) ## Code ## 1 S3_S3_SL3_3 ## 2 SL2_S3_SL3_3 ## 3 SL3_SL3_SL3_1 ##Counting number of unique soil types in input soil data soils_n1 <- dim(soils %>% st_drop_geometry() %>% select(Code) %>% unique)[1] soils <- soils %>% st_intersection(impervious) %>% st_intersection(water_level) %>% st_intersection(drainage) %>% mutate(SNAM = paste0(Code, \"_\", Impervious, \"_\", Depth, \"_\", Drained)) %>% select(SNAM) ##Counting number of unique soil types in generated soil data soils_n2 <- dim(soils %>% st_drop_geometry() %>% select(SNAM) %>% unique)[1] ##Example of new soil codes print(head(soils %>% st_drop_geometry(), 3)) ## SNAM ## 3 SL3_SL3_SL3_1_>100cm_>100cm_N ## 5 SL4_SL4_SL4_3_>100cm_>100cm_N ## 6 SL3_SL3_SL3_2_>100cm_>100cm_N ##Praparing figure of new soil data p5 <- ggplot(soils) + geom_sf(mapping = aes(fill = SNAM), show.legend = F) + theme_void() cowplot::plot_grid(p4, p5, ncol = 2, labels = c('Original soil map', 'New soil map'), label_x = 0, label_y = 0, hjust = -0.1, vjust = -0.5, label_size = 10)"},{"path":"/articles/soil.html","id":"preparing-initial-user-soil-table","dir":"Articles","previous_headings":"","what":"Preparing initial user soil table","title":"Soil parameters","text":"part prepares input following parameter generation part, actually uses svatools package functions populate usersoil table. Workflow provided done form input usertable, used get_usersoil_table function. However, information prepared means R, might convenient user loaded R environment. requirements table parameters filled: SNAM soil type text last three parts separated **_** representing impervious layer, water level drainage classes; NLAYERS numeric value soil type representing number soil layers; SOL_Z numeric value soil layer represent max depth soil layer; SAND numeric sand content %; SILT numeric silt content %; CLAY numeric clay content %; SOL_CBN numeric organic carbon content %. example demonstrates data lookup tables used populate initial usertable look generating parameters. case soil types three layers depth. information added mutate function. according textural classes lookup information added table left_join functions.","code":"usertable <- soils %>% st_drop_geometry() %>% distinct() %>% separate(SNAM, c(\"Lyr1\", \"Lyr2\", \"Lyr3\", \"Humus\", \"Impervious\", \"Depth\", \"Drained\"), \"_\", remove = FALSE) %>% mutate(NLAYERS = 3, SOL_Z1 = 250, SOL_Z2 = 750, SOL_Z3 = 1500, Humus = as.numeric(Humus)) %>% left_join(gran %>% rename_with( ~ paste0(.x, \"1\")), by = c(\"Lyr1\" = \"Texture EN1\")) %>% left_join(gran %>% rename_with( ~ paste0(.x, \"2\")), by = c(\"Lyr2\" = \"Texture EN2\")) %>% left_join(gran %>% rename_with( ~ paste0(.x, \"3\")), by = c(\"Lyr3\" = \"Texture EN3\")) %>% left_join(humus, by = c(\"Humus\")) %>% mutate(Impervious = str_split(SNAM, \"_\")[[1]][5], Depth = str_split(SNAM, \"_\")[[1]][6], Drained = str_split(SNAM, \"_\")[[1]][7]) %>% select(SNAM, NLAYERS, Impervious, Depth, Drained, ends_with(\"1\"), ends_with(\"2\"), ends_with(\"3\"), -starts_with(\"Lyr\")) ##Renaming OC column to SOL_CBN sol_oc <- names(usertable)[grep(\"OC>*\", names(usertable))] usertable <- rename_at(usertable, vars(all_of(sol_oc)), ~sub(\"OC\", \"SOL_CBN\", sol_oc)) ##Saving and printing structure example of the required table write.csv(usertable, \"../output/usersoils.csv\", row.names=FALSE, quote=FALSE) str(usertable) ## 'data.frame': 542 obs. of 20 variables: ## $ SNAM : chr \"SL3_SL3_SL3_1_>100cm_>100cm_N\" \"SL4_SL4_SL4_3_>100cm_>100cm_N\" \"SL3_SL3_SL3_2_>100cm_>100cm_N\" \"SL2_SL4_SL4_2_>100cm_>100cm_N\" ... ## $ NLAYERS : num 3 3 3 3 3 3 3 3 3 3 ... ## $ Impervious: chr \">100cm\" \">100cm\" \">100cm\" \">100cm\" ... ## $ Depth : chr \">100cm\" \">100cm\" \">100cm\" \">100cm\" ... ## $ Drained : chr \"N\" \"N\" \"N\" \"N\" ... ## $ SOL_Z1 : num 250 250 250 250 250 250 250 250 250 250 ... ## $ SAND1 : num 62.5 60.5 62.5 71.7 80.7 ... ## $ SILT1 : num 25.4 27.8 25.4 22.1 14.8 ... ## $ CLAY1 : num 12.07 11.69 12.07 6.11 4.45 ... ## $ SOL_CBN1 : num 1.88 1.26 1.63 1.63 1.63 ... ## $ SOL_Z2 : num 750 750 750 750 750 750 750 750 750 750 ... ## $ SAND2 : num 62.5 60.5 62.5 60.5 80.7 ... ## $ SILT2 : num 25.4 27.8 25.4 27.8 14.8 ... ## $ CLAY2 : num 12.07 11.69 12.07 11.69 4.45 ... ## $ SOL_CBN2 : num 0.942 0.628 0.814 0.814 0.814 ... ## $ SOL_Z3 : num 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 ... ## $ SAND3 : num 62.5 60.5 62.5 60.5 62.5 ... ## $ SILT3 : num 25.4 27.8 25.4 27.8 25.4 ... ## $ CLAY3 : num 12.1 11.7 12.1 11.7 12.1 ... ## $ SOL_CBN3 : num 0.174 0.116 0.151 0.151 0.151 ..."},{"path":"/articles/soil.html","id":"par_make","dir":"Articles","previous_headings":"","what":"Adding soil parameters to user table","title":"Soil parameters","text":"section required part essence workflow. get_usersoil_table function filling required SWAT+ parameters. input data formatted correctly, function straightforward. requires prepared csv file.","code":"usertable <- get_usersoil_table(\"../output/usersoils.csv\", hsg = TRUE, keep_values = FALSE, nb_lyr = 10) ## [1] \"Soil hydrological groups were calculated.\" ## [1] \"Adding additional 7 layer(s) added.\""},{"path":"/articles/soil.html","id":"overwritting-with-measured-parameters","dir":"Articles","previous_headings":"","what":"Overwritting with measured parameters","title":"Soil parameters","text":"case soil types required soil parameters get_usersoil_table function results can overwritten. Example provides example data code used replacing parameters generated user table. However, achieved keep_values = TRUE get_usersoil_table function, data provided ‘usersoils.csv’ file used input function. case parameters available GIS data (.e. field measurements local databases), can used overwriting generated parameters usertable. example map soil hydrological groups. used overwrite results get_soil_parameters function.","code":"##Example data loaded to be used for overwriting parameters in the user table rep_pars_path <- system.file(\"extdata\", \"soil_parameters.xlsx\", package = \"svatools\") replace_pars <- read_excel(rep_pars_path) ##Extracting soil type codes for each soil layer usertable <- separate(usertable, SNAM, c(\"Lyr1\", \"Lyr2\", \"Lyr3\", \"LyrX\"), \"_\", extra = \"merge\", remove = FALSE) ##Replacing values for selected soil types for(p in c(\"Emt\", \"Mpl\", \"Tn\")){ ##Selected soil layers for (l in seq(1, 3)){ ##Selected parameters mp <- paste0(c(\"SOL_Z\", \"SOL_BD\", \"SOL_AWC\", \"SOL_K\", \"SOL_CBN\", \"CLAY\", \"SILT\", \"SAND\", \"ROCK\", \"SOL_ALB\", \"USLE_K\", \"SOL_EC\"),l) usertable[usertable[paste0(\"Lyr\",l)] == p, mp] <- replace_pars[replace_pars[\"SNAM\"] == p, mp] } } ##Removing columns with soil types for each layer usertable <- select(usertable, -starts_with(\"Lyr\")) ##Joining GIS data to generated usertable soilp <- soils %>% left_join(usertable, by = \"SNAM\") ##Plotting soil hydrologic groups obtained from get_usersoil_table function plot(soilp[\"HYDGRP\"], main=\"HYDGRP obtained with get_usersoil_table function\") ##Plotting soil hydrologic groups obtained local hydgrp map plot(hsg[\"hsg\"], main=\"HYDGRP from field data\") ##Overwriting generated data with values from field data source soilp <- st_join(soilp, hsg, join = st_overlaps, left = TRUE, largest = TRUE) %>% mutate(HYDGRP = hsg) %>% select(-hsg) ##Plotting updated data result plot(soilp[\"HYDGRP\"])"},{"path":"/articles/soil.html","id":"checking-results-on-a-map","dir":"Articles","previous_headings":"","what":"Checking results on a map","title":"Soil parameters","text":"generation required soil parameters checking results map good practice, help spot various issues validate results. just examples.","code":"##Plot soil sol_k for the top layer plot(soilp[\"SOL_K1\"]) ##Plot soil sol_k for the top layer plot(soilp[\"USLE_K1\"]) ##Plot soil sol_awc for the top layer plot(soilp[\"SOL_AWC1\"])"},{"path":"/articles/soil.html","id":"write-inputs-for-swat","dir":"Articles","previous_headings":"","what":"Write inputs for SWAT+","title":"Soil parameters","text":"last step write SWAT+ input data: raster layer (case named ‘SoilmapSWAT.tif’), linkage table raster codes soil type codes (‘Soil_SWAT_cod.csv’) usersoil table (‘usersoil_lrew.csv’). DEM data step used help rastarize soil types distribution grid, used setup preparation algorithms. write_stars function stars package used write raster file write.csv R base function write .csv files.","code":"##Reading catchment DEM raster data dem <- read_stars(DEM_path) plot(dem) ##Merging unique soil types into one row and multipolygon soils_dis <- soils %>% group_by(SNAM) %>% dplyr::summarise() %>% mutate(SOIL_ID = rownames(.), SNAME = SNAM) ##Rasterizing soil type data soils_raster <- soils_dis %>% st_rasterize(dem) plot(soils_raster) ##Preparing linkage table soil_link_table <- soils_dis %>% st_drop_geometry() %>% select(SOIL_ID, SNAME) print(head(soil_link_table, 3)) ## # A tibble: 3 × 2 ## SOIL_ID SNAME ## ## 1 1 Emt_Emt_Emt_1_>100cm_<60cm_N ## 2 2 Emt_Emt_Emt_1_>100cm_<60cm_Y ## 3 3 Emt_Emt_Emt_1_>100cm_>100cm_N ##Writing files write_stars(soils_raster , \"../output/SoilmapSWAT.tif\") write.csv(soil_link_table, \"../output/Soil_SWAT_cod.csv\", row.names=FALSE, quote=FALSE) write.csv(usertable, \"../output/usersoil_lrew.csv\", row.names=FALSE, quote=FALSE)"},{"path":"/articles/weather.html","id":"load_weather","dir":"Articles","previous_headings":"","what":"Loading","title":"Weather data","text":"Loading weather data templates done load_template function loading calibration data. However, data meteorological station separated different sheets. data loaded nested list list format, used run functions.","code":"library(svatools) temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) ## [1] \"Loading data from template.\" ## [1] \"Reading station ID9 data.\" ## [1] \"Reading station ID3 data.\" ## [1] \"Reading station ID10 data.\" ## [1] \"Reading station ID11 data.\" ## [1] \"Reading station ID12 data.\" ## [1] \"Reading station ID13 data.\" ## [1] \"Reading station ID1 data.\" ## [1] \"Reading station ID8 data.\" ## [1] \"Reading station ID2 data.\" ## [1] \"Reading station ID4 data.\" ## [1] \"Reading station ID5 data.\" ## [1] \"Reading station ID6 data.\" ## [1] \"Reading station ID7 data.\" ## [1] \"Reading station ID14 data.\" ## [1] \"Loading of data is finished.\""},{"path":"/articles/weather.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"Weather data","text":"Function plot_weather allows plot meteorological data selecting variables, time period way aggregating . Time period selected one “second”,“minute”,“hour”,“day”, “week”, “month”, “year”. aggregation functions selected dplyr package summarise function available options. provided meteorological stations` data displayed. Relevant stations selected interactively generated plot selecting deselecting stations legend. show hide buttons allows select deselect stations. Press Shift + left mouse bottom drag zoom specific area, press left mouse button grad explore plot, press Shift + left right mouse buttons time zoom . Toolbox top right corner figure used operations well. Another function plot_weather_compare allows comparing two meteorological datasets time scale. Potential examples usage different. One comparing different datasets order decide, one used modeling. Another example might examine cleaning operations improved original data. Moreover, function helpful examining climate data downscaling bias-correction results comparing observed data. options aggregation plot_weather_compare function can used.","code":"plot_weather(met_lst, \"PCP\", \"month\", \"sum\") temp_path2 <- system.file(\"extdata\", \"weather_data_raw.xlsx\", package = \"svatools\") met_lst2 <- load_template(temp_path2, 3035) ## [1] \"Loading data from template.\" ## [1] \"Reading station ID9 data.\" ## [1] \"Reading station ID3 data.\" ## [1] \"Reading station ID10 data.\" ## [1] \"Reading station ID11 data.\" ## [1] \"Reading station ID12 data.\" ## [1] \"Reading station ID13 data.\" ## [1] \"Reading station ID1 data.\" ## [1] \"Reading station ID8 data.\" ## [1] \"Reading station ID2 data.\" ## [1] \"Reading station ID4 data.\" ## [1] \"Reading station ID5 data.\" ## [1] \"Reading station ID6 data.\" ## [1] \"Reading station ID7 data.\" ## [1] \"Loading of data is finished.\" plot_weather_compare(met_lst, met_lst2, \"PCP\", \"month\", \"mean\", \"clean\", \"raw\")"},{"path":"/articles/weather.html","id":"interpolating","dir":"Articles","previous_headings":"","what":"Interpolating","title":"Weather data","text":"cases stations available selected catchment huge data gaps exists, interpolation applied stations cover gaps. Example loads basin boundary used modeling adds already loaded meteorological stations map. seen basin just one station inside . However, case station observations huge gaps. Therefore, highly recommended cases collect data nearby stations. data can help fill gaps also used generate spatial distribution variables, employed propagate time series data number virtual stations. Therefore, number meteorological stations prepared full time series data. need function interpolate prepared. allows interpolation data presented stations. Function’s grid_spacing parameter defines, dense spatial interpolation points . Interpolate function creates virtual stations. Data needed function loaded stations observations, basin boundary shape path, DEM raster path. important, GIS data used function coordinate system. Function parameters grid_spacing, p_vector - character vector representing weather variables interpolate (optional, default variables selected c(“PCP”, “SLR”, “RELHUM”, “WNDSPD”, “TMP_MAX”, “TMP_MIN” ), idw_exponent - numeric value exponent parameter used interpolation (optional, default value 2). running interpolate function transform_to_list function used transform interpolation results list list format, used functions (loading weather data template). functions can applied. Transformation function requires dates beginning end time series. info can extracted loaded weather data (used interpolation) internal function svatools:::get_dates.","code":"library(mapview) library(sf) library(dplyr) basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") basin <-st_transform(st_read(basin_path), 4326) %>% mutate(NAME = \"Basin\") ## Reading layer `basin' from data source ## `C:\\Users\\laptop\\AppData\\Local\\Temp\\Rtmpa2dTAZ\\temp_libpath1bf81946cd3\\svatools\\extdata\\GIS\\basin.shp' ## using driver `ESRI Shapefile' ## Simple feature collection with 1 feature and 1 field ## Geometry type: POLYGON ## Dimension: XY ## Bounding box: xmin: 4898668 ymin: 3304508 xmax: 4915266 ymax: 3319644 ## Projected CRS: ETRS89-extended / LAEA Europe stations <- st_transform(met_lst$stations, 4326) mapview(stations) + mapview(basin) DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") result <- svatools::interpolate(met_lst, basin_path, DEM_path, 2000) ##To transform interpolation results to list of lists, which could be used with other weather functions. start_date <- svatools:::get_dates(met_lst)$min_date end_date <- svatools:::get_dates(met_lst)$max_date met_lst_int <- transform_to_list(result, start_date, end_date)"},{"path":"/articles/weather.html","id":"wgn-input-function","dir":"Articles","previous_headings":"Interpolating","what":"WGN input function","title":"Weather data","text":"Weather generator inputs also can directly prepared prepare_wgn function package.","code":"wgn <- prepare_wgn(met_lst, TMP_MAX = met_lst$data$ID10$TMP_MAX, TMP_MIN = met_lst$data$ID10$TMP_MIN, PCP = met_lst$data$ID9$PCP, RELHUM = met_lst$data$ID9$RELHUM, WNDSPD = met_lst$data$ID10$WNDSPD, MAXHHR = met_lst$data$ID11$MAXHHR, SLR = met_lst$data$ID9$SLR) write.csv(wgn$wgn_st, \"wgn_st.csv\", row.names = FALSE, quote = FALSE) write.csv(wgn$wgn_data, \"wgn_data.csv\", row.names = FALSE, quote = FALSE)"},{"path":[]},{"path":"/articles/weather.html","id":"swat-input-and-updating--sqlite-database","dir":"Articles","previous_headings":"Writing","what":"SWAT+ input and updating .sqlite database","title":"Weather data","text":"add_weather function can used write weather files project’s folder update tables project’s sqlite database weather, weather generator relation weather station information.","code":"##Path to .sqlite db_path <- \"./output/test/project.sqlite\" add_weather(db_path, met_lst, wgn)"},{"path":"/reference/add_atmo_dep.html","id":null,"dir":"Reference","previous_headings":"","what":"Update sqlite database with atmospheric deposition data — add_atmo_dep","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"Update sqlite database atmospheric deposition data","code":""},{"path":"/reference/add_atmo_dep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"","code":"add_atmo_dep(df, db_path, t_ext = \"year\")"},{"path":"/reference/add_atmo_dep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"df dafaframe \"DATE\", \"NH4_RF\", \"NO3_RF\" , \"NH4_DRY\" \"NO3_DRY\" columns obtained get_atmo_dep) function. db_path character sqlite database (example \"./output/project.sqlite\") t_ext string, 'year' yearly averages, 'month' - monthly averages 'annual' average period. Optional (default - \"year\").","code":""},{"path":"/reference/add_atmo_dep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"write data 'atmodep.cli' file updated sqlite database codes connection atmospheric deposition data","code":""},{"path":"/reference/add_atmo_dep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"","code":"if (FALSE) { basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") df <- get_atmo_dep(basin_path) db_path <- \"./output/test/project.sqlite\" add_atmo_dep(df, db_path) }"},{"path":"/reference/add_weather.html","id":null,"dir":"Reference","previous_headings":"","what":"Update sqlite database with weather data — add_weather","title":"Update sqlite database with weather data — add_weather","text":"Update sqlite database weather data","code":""},{"path":"/reference/add_weather.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update sqlite database with weather data — add_weather","text":"","code":"add_weather(db_path, meteo_lst, wgn_lst, fill_missing = TRUE)"},{"path":"/reference/add_weather.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update sqlite database with weather data — add_weather","text":"db_path character sqlite database (example \"./output/project.sqlite\") meteo_lst meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). wgn_lst list two dataframes: wgn_st - wgn station data, wgn_data - wgn data (prepared prepare_wgn() function). fill_missing Boolean, TRUE - fill data missing stations data closest stations available data. FALSE - leave stations without data. Weather generator used fill missing variables model. Optional (Default = TRUE).","code":""},{"path":"/reference/add_weather.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update sqlite database with weather data — add_weather","text":"updated sqlite database weather data","code":""},{"path":"/reference/add_weather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update sqlite database with weather data — add_weather","text":"","code":"if (FALSE) { ##Getting meteo data from template met_lst <- load_template(temp_path, 3035) ##Calculating wgn parameters wgn <- prepare_wgn(met_lst, MAXHHR = met_lst$data$ID11$MAXHHR) ##Writing weather input into model database db_path <- \"./output/test/project.sqlite\" add_weather(db_path, met_lst, wgn) }"},{"path":"/reference/clean_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to clean outliers outside by some standard deviations — clean_outliers","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"Function clean outliers outside standard deviations","code":""},{"path":"/reference/clean_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"","code":"clean_outliers(df, times_sd = 3)"},{"path":"/reference/clean_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"df dataframe columns c(\"Station\", \"DATE\", \"Variables\", \"Values\", \"Source\"). times_sd numeric value representing multiplication factor standard deviation. Values outside mean - sd X times_sd, mean + sd X times_sd identified outliers. Optional default value 3.","code":""},{"path":"/reference/clean_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"list two dataframes. newdf dataframe contains dataframe cleaned outliers. dropped dataframe contains data, removed newdf dataframe.","code":""},{"path":"/reference/clean_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" lst <- clean_outliers(cal_data$data) ##Looking at data to be removed print(head(lst$dropped)) #> # A tibble: 6 × 5 #> Station DATE Variables Values Source #> #> 1 10 2012-12-11 00:00:00 N-NH4 16.8 grab sample #> 2 10 2016-02-04 00:00:00 N-NH4 6 grab sample #> 3 10 2016-03-07 00:00:00 N-NH4 4.67 grab sample #> 4 10 2016-04-04 00:00:00 N-NH4 11.1 grab sample #> 5 10 2020-01-27 00:00:00 N-NH4 20.1 grab sample #> 6 10 2013-10-07 00:00:00 N-NO2 0.837 grab sample ##Updating data cal_data$data <- lst$newdf"},{"path":"/reference/clean_wq.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean water quality data from most typical issues — clean_wq","title":"Clean water quality data from most typical issues — clean_wq","text":"Clean water quality data typical issues","code":""},{"path":"/reference/clean_wq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean water quality data from most typical issues — clean_wq","text":"","code":"clean_wq(df, zero_to_min = 1)"},{"path":"/reference/clean_wq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean water quality data from most typical issues — clean_wq","text":"df dataframe water quality data columns c(\"Station\", \"DATE\", \"Variables\", \"Values\", \"Source\"). zero_to_min numeric coefficient zeros min variable value X zero_to_min. Optional, default 1.","code":""},{"path":"/reference/clean_wq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean water quality data from most typical issues — clean_wq","text":"cleaned dataframe","code":""},{"path":"/reference/clean_wq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clean water quality data from most typical issues — clean_wq","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" cal_data$data <- clean_wq(cal_data$data)"},{"path":"/reference/df_t.html","id":null,"dir":"Reference","previous_headings":"","what":"Transforming sp dataframe to dataframe — df_t","title":"Transforming sp dataframe to dataframe — df_t","text":"Transforming sp dataframe dataframe","code":""},{"path":"/reference/df_t.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transforming sp dataframe to dataframe — df_t","text":"","code":"df_t(sp_df)"},{"path":"/reference/df_t.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transforming sp dataframe to dataframe — df_t","text":"sp_df sp dataframe","code":""},{"path":"/reference/df_t.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transforming sp dataframe to dataframe — df_t","text":"Dataframe prepared written .","code":""},{"path":"/reference/df_t.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transforming sp dataframe to dataframe — df_t","text":"","code":"if (FALSE) { df_t(sp_df) }"},{"path":"/reference/extract_rotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract rotation information from raster file — extract_rotation","title":"Extract rotation information from raster file — extract_rotation","text":"Extract rotation information raster file","code":""},{"path":"/reference/extract_rotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract rotation information from raster file — extract_rotation","text":"","code":"extract_rotation(df, start_year, tif_name, r_path, lookup, lu_constant = c())"},{"path":"/reference/extract_rotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract rotation information from raster file — extract_rotation","text":"df sf data.frame land use. \"id\" \"type\" columns present. start_year numeric, representing year data begins. tif_name string name .tif raster file. r_path string path .tif file. lookup dataframe \"lc1\" column numeric codes \"type\" column text. lu_constant vector strings land uses kept constant land use (.e. water, urban areas, etc.)","code":""},{"path":"/reference/extract_rotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract rotation information from raster file — extract_rotation","text":"sf data.frame land use amended crop rotation information","code":""},{"path":"/reference/extract_rotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract rotation information from raster file — extract_rotation","text":"","code":"if (FALSE) { library(sf) ##Loading land use/crop layer lu_path <- system.file(\"extdata\", \"GIS/lu_layer.shp\", package = \"svatools\") lu <- st_read(lu_path, quiet = TRUE) %>% mutate(id = row_number()) ##Preparing lookup table lookup <- data.frame(lc1 = seq(1:length(unique(c(lu$type)))), type = unique(c(lu$type))) lu_constant <- c(\"fesc\", \"orch\", \"frst\", \"frse\", \"frsd\", \"urld\", \"urhd\", \"wetl\", \"past\", \"watr\", \"agrl\") ##Extracting rotation information from raster ##Raster information should have been prepared with remote sensing lu_rot <- extract_rotation(lu, 2015, \"cropmaps.tif\", \"./output/\", lookup, lu_constant) }"},{"path":"/reference/get_atmo_dep.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"Extract EMEP atmospheric deposition data catchment","code":""},{"path":"/reference/get_atmo_dep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"","code":"get_atmo_dep( catchment_boundary_path, t_ext = \"year\", start_year = 1990, end_year = 2020 )"},{"path":"/reference/get_atmo_dep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"catchment_boundary_path path basin boundary shape file. t_ext string, EMEP data access 'year' yearly averages, 'month' - monthly averages. Optional (default - 'year'). start_year integer year start data extraction. Optional (default - 1990). end_year integer year end data extraction. Optional (default - 2020).","code":""},{"path":"/reference/get_atmo_dep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"dafaframe \"DATE\", \"NH4_RF\", \"NO3_RF\" , \"NH4_DRY\" \"NO3_DRY\" columns. Values SWAT+ units. \"NH4_RF\" - ammonia rainfall (mg/l), \"NO3_RF\" - nitrate rainfall (mg/l), NH4_RF - ammonia deposition (kg/ha/yr), \"NO3_DRY\" - nitrate dry deposition (kg/ha/yr)","code":""},{"path":"/reference/get_atmo_dep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"","code":"if (FALSE) { basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") df <- get_atmo_dep(basin_path) ##Plot results ggplot(pivot_longer(df, !DATE, names_to = \"par\", values_to = \"values\"), aes(x = DATE, y = values))+ geom_line()+ facet_wrap(~par, scales = \"free_y\")+ theme_bw() }"},{"path":"/reference/get_data_to_interpolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to get data to be used in the interpolation — get_data_to_interpolate","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"Function get data used interpolation","code":""},{"path":"/reference/get_data_to_interpolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"","code":"get_data_to_interpolate(meteo_lst, par)"},{"path":"/reference/get_data_to_interpolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par weather variable extract (.e. \"PCP\", \"SLR\", etc)","code":""},{"path":"/reference/get_data_to_interpolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"SpatialPointsDataFrame columns days rows stations.","code":""},{"path":"/reference/get_data_to_interpolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"","code":"##get_data_to_interpolate (meteo_lst, \"PCP\")"},{"path":"/reference/get_dates.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"Function get maximum min max dates available time series data stations parameters.","code":""},{"path":"/reference/get_dates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"","code":"get_dates(meteo_lst)"},{"path":"/reference/get_dates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER).","code":""},{"path":"/reference/get_dates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"list 2 values: minimum maximum value available data.","code":""},{"path":"/reference/get_dates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"","code":"if (FALSE) { get_dates(meteo_lst) }"},{"path":"/reference/get_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare grid for interpolation — get_grid","title":"Prepare grid for interpolation — get_grid","text":"Prepare grid interpolation","code":""},{"path":"/reference/get_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare grid for interpolation — get_grid","text":"","code":"get_grid(b, grid_spacing, wkt_str)"},{"path":"/reference/get_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare grid for interpolation — get_grid","text":"b vector border c(xmin, ymin, xmax, ymax) coordinates define extent grid. grid_spacing Grid spacing. Depending coordinate system meters, degrees. wkt_str projection string","code":""},{"path":"/reference/get_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare grid for interpolation — get_grid","text":"Grid needed interpolation.","code":""},{"path":"/reference/get_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare grid for interpolation — get_grid","text":"","code":"if (FALSE) { get_grid(shp, 2000, wkt_str) }"},{"path":"/reference/get_hsg.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to get soil hydrologic groups — get_hsg","title":"Function to get soil hydrologic groups — get_hsg","text":"Function get soil hydrologic groups","code":""},{"path":"/reference/get_hsg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to get soil hydrologic groups — get_hsg","text":"","code":"get_hsg(d_imp, d_wtr, drn, t)"},{"path":"/reference/get_hsg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to get soil hydrologic groups — get_hsg","text":"d_imp character depth impervious layer. three entry options possible: \"<50cm\", \"50-100cm\", \">100cm\". d_wtr character water table high. three entry options possible: \"<60cm\", \"60-100cm\", \">100cm\". drn character tile drainage status. two entry options possible: \"Y\" drained areas, \"N\" areas without working tile drains. t one row dataframe SOL_Z SOL_K values soil type.","code":""},{"path":"/reference/get_hsg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to get soil hydrologic groups — get_hsg","text":"character soil hydrologic group , B, C D","code":""},{"path":"/reference/get_hsg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to get soil hydrologic groups — get_hsg","text":"","code":"df <- data.frame(SOL_K1 = 10, SOL_K2 = 1, SOL_K3 = 2, SOL_Z1 = 250, SOL_Z2 = 700, SOL_Z3 = 1000) get_hsg(d_imp = \">100cm\", d_wtr = \"<60cm\", drn = \"Y\", df) #> Error in get_hsg(d_imp = \">100cm\", d_wtr = \"<60cm\", drn = \"Y\", df): could not find function \"get_hsg\""},{"path":"/reference/get_interpolated_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Main interpolation function — get_interpolated_data","title":"Main interpolation function — get_interpolated_data","text":"Main interpolation function","code":""},{"path":"/reference/get_interpolated_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Main interpolation function — get_interpolated_data","text":"","code":"get_interpolated_data(meteo_lst, grd, par, shp, dem_data_path, idw_exponent)"},{"path":"/reference/get_interpolated_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Main interpolation function — get_interpolated_data","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). grd sp SpatialGrid grid interpolation. par character representing weather variable extract (.e. \"PCP\", \"SLR\", etc). shp sf dataframe defining basin boundary shape. dem_data_path path DEM raster data projection weather station. idw_exponent numeric value exponent parameter used interpolation. (optional, default value 2).","code":""},{"path":"/reference/get_interpolated_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Main interpolation function — get_interpolated_data","text":"SpatialPointsDataFrame interpolated data.","code":""},{"path":"/reference/get_interpolated_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Main interpolation function — get_interpolated_data","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) get_interpolated_data(met_lst, grd, \"PCP\", read_sf(basin_path), DEM_path, 2) }"},{"path":"/reference/get_interpolation.html","id":null,"dir":"Reference","previous_headings":"","what":"Get interpolation values for catchment — get_interpolation","title":"Get interpolation values for catchment — get_interpolation","text":"Get interpolation values catchment","code":""},{"path":"/reference/get_interpolation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get interpolation values for catchment — get_interpolation","text":"","code":"get_interpolation(sp_df, st, grd, i, idw_exponent)"},{"path":"/reference/get_interpolation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get interpolation values for catchment — get_interpolation","text":"sp_df SpatialPointsDataFrame one column one day rows stations. st SpatialPointsDataFrame virtual stations grid. contain dataframe data station elevation DEM grd Grid interpolation. integer representing day number. idw_exponent numeric value exponent parameter used interpolation.","code":""},{"path":"/reference/get_interpolation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get interpolation values for catchment — get_interpolation","text":"SpatialPointsDataFrame virtual stations interpolated data data dataframe","code":""},{"path":"/reference/get_interpolation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get interpolation values for catchment — get_interpolation","text":"","code":"if (FALSE) { get_interpolation(sp_df, meteo_pts, 2, 2) }"},{"path":"/reference/get_lu_points.html","id":null,"dir":"Reference","previous_headings":"","what":"Preparing training points for remote sensing algorithm — get_lu_points","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"Preparing training points remote sensing algorithm","code":""},{"path":"/reference/get_lu_points.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"","code":"get_lu_points( df, year, lookup, lu_constant = c(), nb_pts = 100, col_name = \"type\" )"},{"path":"/reference/get_lu_points.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"df sf data.frame land use. \"type\" column present. year numeric value, year land use. lookup dataframe \"lc1\" column numeric codes \"type\" column text. lu_constant vector strings land uses kept constant land use (.e. water, urban areas, etc.) nb_pts numeric, number points per land use/crop class. Optional, default 100. col_name string name column used representing type crops/land use. Optional, default \"type\".","code":""},{"path":"/reference/get_lu_points.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"sf data.frame point input remote sensing training algorithm.","code":""},{"path":"/reference/get_lu_points.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"","code":"if (FALSE) { library(sf) ##Loading land use/crop layer lu_path <- system.file(\"extdata\", \"GIS/lu_layer.shp\", package = \"svatools\") lu <- st_read(lu_path, quiet = TRUE) ##Preparing lookup table lookup <- data.frame(lc1 = seq(1:length(unique(c(lu$type)))), type = unique(c(lu$type))) lu_constant <- c(\"fesc\", \"orch\", \"frst\", \"frse\", \"frsd\", \"urld\", \"urhd\", \"wetl\", \"past\", \"watr\", \"agrl\") ##Getting training points pts <- get_lu_points(lu, 2021, lookup, lu_constant) }"},{"path":"/reference/get_nb_st_with_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Getting number of stations with data for selected parameter — get_nb_st_with_data","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"Getting number stations data selected parameter","code":""},{"path":"/reference/get_nb_st_with_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"","code":"get_nb_st_with_data(meteo_lst, par)"},{"path":"/reference/get_nb_st_with_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par weather variable extract (.e. \"PCP\", \"SLR\", etc)","code":""},{"path":"/reference/get_nb_st_with_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"number stations data parameter.","code":""},{"path":"/reference/get_nb_st_with_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"","code":"if (FALSE) { get_nb_st_with_data(meteo_lst, \"PCP\") }"},{"path":"/reference/get_soil_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to fill soil parameters — get_soil_parameters","title":"Function to fill soil parameters — get_soil_parameters","text":"Function fill soil parameters","code":""},{"path":"/reference/get_soil_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to fill soil parameters — get_soil_parameters","text":"","code":"get_soil_parameters(soilp)"},{"path":"/reference/get_soil_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to fill soil parameters — get_soil_parameters","text":"soilp dataframe SNAM, NLAYERS columns SOL_Z, CLAY, SILT, SAND, OC columns available soil layer.","code":""},{"path":"/reference/get_soil_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to fill soil parameters — get_soil_parameters","text":"dataframe fully formatted filled table soil parameters SWAT model.","code":""},{"path":"/reference/get_soil_parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to fill soil parameters — get_soil_parameters","text":"","code":"if (FALSE) { soil <- get_soil_parameters(temp_path) }"},{"path":"/reference/hide_show.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"Function put option remove hide show lines chart function also print chart.","code":""},{"path":"/reference/hide_show.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"","code":"hide_show(graph)"},{"path":"/reference/hide_show.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"graph plotly graph object","code":""},{"path":"/reference/hide_show.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"plotly graph object option remove show lines","code":""},{"path":"/reference/hide_show.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"","code":"if (FALSE) { hide_show(fig) }"},{"path":"/reference/interpolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpolating and writing results into model input files — interpolate","title":"Interpolating and writing results into model input files — interpolate","text":"Interpolating writing results model input files","code":""},{"path":"/reference/interpolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpolating and writing results into model input files — interpolate","text":"","code":"interpolate( meteo_lst, catchment_boundary_path, dem_data_path, grid_spacing, p_vector = c(\"PCP\", \"SLR\", \"RELHUM\", \"WNDSPD\", \"TMP_MAX\", \"TMP_MIN\"), idw_exponent = 2 )"},{"path":"/reference/interpolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpolating and writing results into model input files — interpolate","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). catchment_boundary_path path basin boundary shape file. dem_data_path path DEM raster data projection weather station. grid_spacing numeric value distance grid. Units coordinate system used. p_vector character vector representing weather variables interpolate (optional, default variables selected c(\"PCP\", \"SLR\", \"RELHUM\", \"WNDSPD\", \"TMP_MAX\", \"TMP_MIN\" ). idw_exponent numeric value exponent parameter used interpolation (optional, default value 2).","code":""},{"path":"/reference/interpolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpolating and writing results into model input files — interpolate","text":"nested list lists dataframes interpolation results. Nested structure lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Function also writes SWAT weather text input files interpolation results.","code":""},{"path":"/reference/interpolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interpolating and writing results into model input files — interpolate","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) interpolate(met_lst, basin_path, DEM_path, 2000) }"},{"path":"/reference/list_to_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Transforming list to dataframe — list_to_df","title":"Transforming list to dataframe — list_to_df","text":"Transforming list dataframe","code":""},{"path":"/reference/list_to_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transforming list to dataframe — list_to_df","text":"","code":"list_to_df(lst)"},{"path":"/reference/list_to_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transforming list to dataframe — list_to_df","text":"lst nested list dataframes (weather variable)","code":""},{"path":"/reference/list_to_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transforming list to dataframe — list_to_df","text":"dataframe collected dataframes single dataframe.","code":""},{"path":"/reference/list_to_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transforming list to dataframe — list_to_df","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) df <- list_to_df(met_lst$data$ID1) }"},{"path":"/reference/load_climate.html","id":null,"dir":"Reference","previous_headings":"","what":"Function loading climate data csv files — load_climate","title":"Function loading climate data csv files — load_climate","text":"Function loading climate data csv files","code":""},{"path":"/reference/load_climate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function loading climate data csv files — load_climate","text":"","code":"load_climate( f_path, f_lst = list(PCP = \"prec-1\", SLR = \"solarRad-1\", RELHUM = \"relHum-1\", TMP_MAX = \"Tmax-1\", TMP_MIN = \"Tmin-1\", WNDSPD = \"windSpeed-1\") )"},{"path":"/reference/load_climate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function loading climate data csv files — load_climate","text":"f_path character providing path climate date folder (.e. \"inst/extdata/CORDEX-BC\") f_lst list providing file name weather parameter (default list(\"PCP\" = \"prec-1\", \"SLR\" = \"solarRad-1\", \"RELHUM\" = \"relHum-1\", \"TMP_MAX\" = \"Tmax-1\", \"TMP_MIN\" = \"Tmin-1\", \"WNDSPD\" = \"windSpeed-1\"))","code":""},{"path":"/reference/load_climate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function loading climate data csv files — load_climate","text":"nested list lists dataframes. Nested structure meteo_lst -> RCM_MOD-> Parameter -> Dataframe (DATE, PARAMETER).","code":""},{"path":"/reference/load_climate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function loading climate data csv files — load_climate","text":"","code":"if (FALSE) { temp_path <- paste0(system.file(\"extdata\", package = \"svatools\"), \"/CORDEX-BC\") cli_lst <- load_climate(temp_path) str(cli_lst) }"},{"path":"/reference/load_stations.html","id":null,"dir":"Reference","previous_headings":"","what":"Reading station data from excel templates — load_stations","title":"Reading station data from excel templates — load_stations","text":"Reading station data excel templates","code":""},{"path":"/reference/load_stations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reading station data from excel templates — load_stations","text":"","code":"load_stations(template_path, epsg_code)"},{"path":"/reference/load_stations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reading station data from excel templates — load_stations","text":"template_path path *.xlsx file. epsg_code EPSG code station coordinates system.","code":""},{"path":"/reference/load_stations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reading station data from excel templates — load_stations","text":"sf dataframe station information.","code":""},{"path":"/reference/load_stations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reading station data from excel templates — load_stations","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") stations <- load_stations(temp_path, 3035) str(stations) library(sf) library(mapview) mapview(st_transform(stations, 4326)) }"},{"path":"/reference/load_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"Function providing loading data templates (single multiple data sheets) (data cleaned ).","code":""},{"path":"/reference/load_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"","code":"load_template(template_path, epsg_code = 4326)"},{"path":"/reference/load_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"template_path path *.xlsx file. epsg_code EPSG code station coordinates (default 4326 WGS 84 coordinate system) system","code":""},{"path":"/reference/load_template.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"list two: stations dataframe, contains station information, second member list contains contains measurement data dataframe (data one sheet) nested list dataframes parameter separate dataframe.","code":""},{"path":"/reference/load_template.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"","code":"##Two types of templates could be used # 1) Example of template for weather data temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) #> [1] \"Loading data from template.\" #> [1] \"Reading station ID9 data.\" #> [1] \"Reading station ID3 data.\" #> [1] \"Reading station ID10 data.\" #> [1] \"Reading station ID11 data.\" #> [1] \"Reading station ID12 data.\" #> [1] \"Reading station ID13 data.\" #> [1] \"Reading station ID1 data.\" #> [1] \"Reading station ID8 data.\" #> [1] \"Reading station ID2 data.\" #> [1] \"Reading station ID4 data.\" #> [1] \"Reading station ID5 data.\" #> [1] \"Reading station ID6 data.\" #> [1] \"Reading station ID7 data.\" #> [1] \"Reading station ID14 data.\" #> [1] \"Loading of data is finished.\" str(met_lst) #> List of 2 #> $ stations: sf [14 × 7] (S3: sf/tbl_df/tbl/data.frame) #> ..$ ID : chr [1:14] \"ID1\" \"ID2\" \"ID3\" \"ID4\" ... #> ..$ Name : chr [1:14] \"RUDZK DUZY\" \"GLEBOKIE\" \"GLODOWO\" \"SOBIESIERNIE\" ... #> ..$ Elevation: chr [1:14] \"98.7\" \"85.0\" \"99.0\" \"80.0\" ... #> ..$ Source : chr [1:14] \"ODR\" \"IMGW\" \"IMGW\" \"IMGW\" ... #> ..$ geometry :sfc_POINT of length 14; first list element: 'XY' num [1:2] 4892472 3300305 #> ..$ Long : num [1:14] 4892472 4891414 4941373 4890114 4899376 ... #> ..$ Lat : num [1:14] 3300305 3315522 3342216 3326585 3286548 ... #> ..- attr(*, \"sf_column\")= chr \"geometry\" #> ..- attr(*, \"agr\")= Factor w/ 3 levels \"constant\",\"aggregate\",..: NA NA NA NA NA NA #> .. ..- attr(*, \"names\")= chr [1:6] \"ID\" \"Name\" \"Elevation\" \"Source\" ... #> $ data :List of 14 #> ..$ ID9 :List of 6 #> .. ..$ SLR : tibble [2,952 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:2952], format: \"2014-01-01\" ... #> .. .. ..$ SLR : num [1:2952] 1.32 2.83 2.73 2.08 2.25 ... #> .. ..$ RELHUM : tibble [3,317 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:3317], format: \"2014-01-01\" ... #> .. .. ..$ RELHUM: num [1:3317] 0.885 0.873 0.881 0.887 0.888 ... #> .. ..$ TMP_MAX: tibble [3,317 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:3317], format: \"2014-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:3317] 3.4 4.2 6.4 6.6 9.3 7.2 9.6 9.3 9.6 9.4 ... #> .. ..$ TMP_MIN: tibble [3,306 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:3306], format: \"2014-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:3306] 0.4 -1.6 0.3 2.4 4.1 1.4 4.2 4.4 3.1 5.1 ... #> .. ..$ PCP : tibble [3,197 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:3197], format: \"2014-05-01\" ... #> .. .. ..$ PCP : num [1:3197] 0 1 0 0.2 0 0.2 1 0 0.8 0.2 ... #> .. ..$ WNDSPD : tibble [760 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:760], format: \"2021-01-01\" ... #> .. .. ..$ WNDSPD: num [1:760] 1.89 1.1 2.68 2.36 1.85 ... #> ..$ ID3 :List of 3 #> .. ..$ TMP_MAX: tibble [6,726 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:6726], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:6726] 6.6 4.4 7.5 6.2 6.6 6.2 5 7 4.6 3.5 ... #> .. ..$ TMP_MIN: tibble [6,726 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:6726], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:6726] 1 1.6 1.5 3.3 2.5 2.5 2.8 3.5 1.6 1.1 ... #> .. ..$ RELHUM : tibble [1,154 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1154], format: \"1998-01-01\" ... #> .. .. ..$ RELHUM: num [1:1154] 0.91 0.903 0.86 0.828 0.823 0.92 0.923 0.895 0.943 0.96 ... #> ..$ ID10:List of 4 #> .. ..$ TMP_MAX: tibble [1,948 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1948], format: \"2016-06-01\" ... #> .. .. ..$ TMP_MAX: num [1:1948] 25.3 23.8 23.5 26 23.6 19 21.4 26.7 20 21.3 ... #> .. ..$ TMP_MIN: tibble [1,948 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1948], format: \"2016-06-01\" ... #> .. .. ..$ TMP_MIN: num [1:1948] 14.3 14.5 13 11.8 10.7 8.9 6.1 8 10.1 8.5 ... #> .. ..$ RELHUM : tibble [1,947 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1947], format: \"2016-06-01\" ... #> .. .. ..$ RELHUM: num [1:1947] 0.787 0.806 0.646 0.646 0.551 0.541 0.486 0.546 0.601 0.62 ... #> .. ..$ WNDSPD : tibble [1,932 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1932], format: \"2016-06-01\" ... #> .. .. ..$ WNDSPD: num [1:1932] 3.4 3.2 3.6 1.5 3.1 3.4 1.6 2.6 2.3 4 ... #> ..$ ID11:List of 3 #> .. ..$ SLR : tibble [6,940 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:6940], format: \"2000-01-01\" ... #> .. .. ..$ SLR : num [1:6940] 1.16 0.85 0.77 0.55 0.82 1.02 0.77 2.72 0.54 0.99 ... #> .. ..$ MAXHHR: tibble [4,040 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:4040], format: \"2008-06-06\" ... #> .. .. ..$ MAXHHR: num [1:4040] 0 0 0 0 0 0 0 0.9 0.1 0.2 ... #> .. ..$ PCP : tibble [4,040 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:4040], format: \"2008-06-06\" ... #> .. .. ..$ PCP : num [1:4040] 0 0 0 0 0 0 0 2.8 0.1 0.3 ... #> ..$ ID12:List of 6 #> .. ..$ TMP_MAX: tibble [8,571 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8571], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:8571] 6.8 4.7 8.1 6.6 7.4 5.6 6.6 8.1 5.5 6 ... #> .. ..$ TMP_MIN: tibble [8,571 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8571], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:8571] 1.1 0.4 1.1 3.2 1.6 2.2 2.7 4.7 1.7 1.7 ... #> .. ..$ RELHUM : tibble [6,574 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:6574], format: \"1998-01-01\" ... #> .. .. ..$ RELHUM: num [1:6574] 0.903 0.94 0.83 0.825 0.755 0.908 0.935 0.9 0.975 0.983 ... #> .. ..$ WNDSPD : tibble [6,574 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:6574], format: \"1998-01-01\" ... #> .. .. ..$ WNDSPD: num [1:6574] 2 1.7 3.7 4.3 7.3 3.7 3 5 2 1.7 ... #> .. ..$ MAXHHR : tibble [3,187 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:3187], format: \"2012-06-13\" ... #> .. .. ..$ MAXHHR: num [1:3187] 6.6 8.5 0.2 3.8 4.4 0.1 0 9.9 2.9 0.5 ... #> .. ..$ PCP : tibble [3,187 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:3187], format: \"2012-06-13\" ... #> .. .. ..$ PCP : num [1:3187] 13 21.3 0.3 10.9 7.8 0.1 0 34.1 21.1 0.9 ... #> ..$ ID13:List of 5 #> .. ..$ TMP_MAX: tibble [8,379 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8379], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:8379] 7.5 5.5 8.7 7.2 8.5 5.7 6.4 9 5.8 4.8 ... #> .. ..$ TMP_MIN: tibble [8,380 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8380], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:8380] 1.1 0.5 1.5 3.5 2.2 2.7 2.7 4.5 2.6 1.9 ... #> .. ..$ SLR : tibble [6,915 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:6915], format: \"2000-01-01\" ... #> .. .. ..$ SLR : num [1:6915] 1.45 0.97 0.55 0.31 1.15 0.96 0.71 2.6 2.52 1.06 ... #> .. ..$ RELHUM : tibble [8,358 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8358], format: \"1998-01-01\" ... #> .. .. ..$ RELHUM: num [1:8358] 0.949 0.923 0.826 0.809 0.706 0.928 0.911 0.873 0.973 0.994 ... #> .. ..$ WNDSPD : tibble [8,401 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8401], format: \"1998-01-01\" ... #> .. .. ..$ WNDSPD: num [1:8401] 2.5 3.5 4.9 5.5 7.5 6.4 5.1 6.5 2.5 2 ... #> ..$ ID1 :List of 5 #> .. ..$ TMP_MAX: tibble [930 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:930], format: \"2018-10-27\" ... #> .. .. ..$ TMP_MAX: num [1:930] 11.2 7.9 10.1 19.6 15.4 ... #> .. ..$ TMP_MIN: tibble [868 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:868], format: \"2018-10-27\" ... #> .. .. ..$ TMP_MIN: num [1:868] 4.67 0.64 1.8 10.09 4.57 ... #> .. ..$ RELHUM : tibble [565 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:565], format: \"2018-10-27\" ... #> .. .. ..$ RELHUM: num [1:565] 0.716 0.877 0.983 0.81 0.712 ... #> .. ..$ WNDSPD : tibble [930 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:930], format: \"2018-10-27\" ... #> .. .. ..$ WNDSPD: num [1:930] 1.09 1.47 3.04 2.97 1 ... #> .. ..$ PCP : tibble [384 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:384], format: \"2019-01-01\" ... #> .. .. ..$ PCP : num [1:384] 4.3 1.1 1.8 2.5 5.9 1.2 0.7 1.5 3.3 0.1 ... #> ..$ ID8 :List of 4 #> .. ..$ TMP_MAX: tibble [1,171 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1171], format: \"2018-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:1171] 9.68 3.79 4.13 7.41 7.63 7.54 4.05 1.45 2.14 1.27 ... #> .. ..$ TMP_MIN: tibble [1,165 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1165], format: \"2018-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:1165] 2.59 0.36 0.21 3.95 3.47 2 -2.74 -6.5 -4.29 -0.33 ... #> .. ..$ RELHUM : tibble [1,171 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1171], format: \"2018-01-01\" ... #> .. .. ..$ RELHUM: num [1:1171] 0.875 0.969 0.974 0.897 0.903 ... #> .. ..$ PCP : tibble [402 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:402], format: \"2018-04-01\" ... #> .. .. ..$ PCP : num [1:402] 6.8 1.6 7 8.4 3.6 4.2 0.6 2.4 27.2 0.2 ... #> ..$ ID2 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0 0 0 0 0 0 0 0 0 0 ... #> ..$ ID4 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0 0 2.2 0 3.5 1.3 9.2 6.3 0.4 0 ... #> ..$ ID5 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0 0 2.1 0.3 4.7 0.8 2.5 13.5 0.6 0.7 ... #> ..$ ID6 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0.5 0 2.2 0 3.6 1.2 3.2 7 0 0 ... #> ..$ ID7 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0 0 2.8 0.3 3.1 2.1 1.5 11.6 1 0.2 ... #> ..$ ID14:List of 2 #> .. ..$ SLR : tibble [365 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:365], format: \"2022-01-01\" ... #> .. .. ..$ SLR : num [1:365] 0.683 0.798 0.671 1.203 1.064 ... #> .. ..$ RELHUM: tibble [365 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:365], format: \"2022-01-01\" ... #> .. .. ..$ RELHUM: num [1:365] 1 1 1 1 1 1 1 1 1 1 ... ## 2) Example of template for calibration data temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" str(cal_data) #> List of 2 #> $ stations: sf [20 × 6] (S3: sf/tbl_df/tbl/data.frame) #> ..$ ID : chr [1:20] \"5\" \"8\" \"4\" \"2\" ... #> ..$ Name : chr [1:20] \"Zgłowiączka-Strózewo-Parcele\" \"DopZStarRadziejewa - Witowo\" \"Zgłąwiączka-ponizej. Osiecin,Samszyce\" \"Zgłowiączka-pow.Osiecin. Piołunowo\" ... #> ..$ Description: chr [1:20] \"powyżej jez. Głuszyńskiego (60 km)\" \"Kolonia Witowo\" \"Samszyce - poniżej Osięcin (67,8 km)\" \"powyżej Osięcin (75,2 km)\" ... #> ..$ geometry :sfc_POINT of length 20; first list element: 'XY' num [1:2] 18.7 52.6 #> ..$ Long : num [1:20] 18.7 18.7 18.7 18.7 18.7 ... #> ..$ Lat : num [1:20] 52.6 52.6 52.6 52.6 52.6 ... #> ..- attr(*, \"sf_column\")= chr \"geometry\" #> ..- attr(*, \"agr\")= Factor w/ 3 levels \"constant\",\"aggregate\",..: NA NA NA NA NA #> .. ..- attr(*, \"names\")= chr [1:5] \"ID\" \"Name\" \"Description\" \"Long\" ... #> $ data : tibble [22,306 × 5] (S3: tbl_df/tbl/data.frame) #> ..$ Station : chr [1:22306] \"1\" \"1\" \"1\" \"1\" ... #> ..$ DATE : POSIXct[1:22306], format: \"2008-03-11\" \"2008-03-25\" ... #> ..$ Variables: chr [1:22306] \"N-NH4\" \"N-NH4\" \"N-NH4\" \"N-NH4\" ... #> ..$ Values : num [1:22306] 0.05 0.05 0.05 0.05 0.05 0.05 0.83 0.25 0.51 1.89 ... #> ..$ Source : chr [1:22306] \"grab sample\" \"grab sample\" \"grab sample\" \"grab sample\" ..."},{"path":"/reference/min_ks.html","id":null,"dir":"Reference","previous_headings":"","what":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"Get min Ks within depth range SOL_K SOL_Z","code":""},{"path":"/reference/min_ks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"","code":"min_ks(df, max_depth)"},{"path":"/reference/min_ks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"df one row dataframe soil parameters SOL_K SOL_Z columns max_depth numeric value depth range 0.","code":""},{"path":"/reference/min_ks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"numeric min Ks parameter value within depth range","code":""},{"path":"/reference/min_ks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"","code":"if (FALSE) { df <- data.frame(SOL_K1 = 10, SOL_K2 = 1, SOL_K3 = 2, SOL_Z1 = 250, SOL_Z2 = 700, SOL_Z3 = 1000) min_ks(df, 800) }"},{"path":"/reference/my.check.pwd.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for my.pwd function — my.check.pwd","title":"Helper for my.pwd function — my.check.pwd","text":"Helper .pwd function","code":""},{"path":"/reference/my.check.pwd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper for my.pwd function — my.check.pwd","text":"","code":"my.check.pwd(x)"},{"path":"/reference/my.check.pwd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for my.pwd function — my.check.pwd","text":"x vector numbers","code":""},{"path":"/reference/my.check.pwd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper for my.pwd function — my.check.pwd","text":"logical, wet day dry","code":""},{"path":"/reference/my.check.pwd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper for my.pwd function — my.check.pwd","text":"","code":"if (FALSE) { my.check.pwd(x) }"},{"path":"/reference/my.check.pww.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for my.pww function — my.check.pww","title":"Helper for my.pww function — my.check.pww","text":"Helper .pww function","code":""},{"path":"/reference/my.check.pww.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper for my.pww function — my.check.pww","text":"","code":"my.check.pww(x)"},{"path":"/reference/my.check.pww.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for my.pww function — my.check.pww","text":"x vector numbers","code":""},{"path":"/reference/my.check.pww.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper for my.pww function — my.check.pww","text":"logical, wet day wet","code":""},{"path":"/reference/my.check.pww.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper for my.pww function — my.check.pww","text":"","code":"if (FALSE) { my.check.pww(x) }"},{"path":"/reference/my.pcpd.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate pcp_days parameter — my.pcpd","title":"Function to calculate pcp_days parameter — my.pcpd","text":"Function calculate pcp_days parameter","code":""},{"path":"/reference/my.pcpd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate pcp_days parameter — my.pcpd","text":"","code":"my.pcpd(x, nyears, na.rm = FALSE)"},{"path":"/reference/my.pcpd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate pcp_days parameter — my.pcpd","text":"x vector numbers nyears numeric number years available PCP variable na.rm logical, NA cleaned","code":""},{"path":"/reference/my.pcpd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate pcp_days parameter — my.pcpd","text":"numeric result","code":""},{"path":"/reference/my.pcpd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate pcp_days parameter — my.pcpd","text":"","code":"if (FALSE) { my.pcpd(x) }"},{"path":"/reference/my.pwd.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate wet_dry parameter — my.pwd","title":"Function to calculate wet_dry parameter — my.pwd","text":"Function calculate wet_dry parameter","code":""},{"path":"/reference/my.pwd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate wet_dry parameter — my.pwd","text":"","code":"my.pwd(x, na.rm = FALSE)"},{"path":"/reference/my.pwd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate wet_dry parameter — my.pwd","text":"x vector numbers na.rm logical, NA cleaned","code":""},{"path":"/reference/my.pwd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate wet_dry parameter — my.pwd","text":"numeric result","code":""},{"path":"/reference/my.pwd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate wet_dry parameter — my.pwd","text":"","code":"if (FALSE) { my.pwd(x) }"},{"path":"/reference/my.pww.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate wet_wet parameter — my.pww","title":"Function to calculate wet_wet parameter — my.pww","text":"Function calculate wet_wet parameter","code":""},{"path":"/reference/my.pww.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate wet_wet parameter — my.pww","text":"","code":"my.pww(x, na.rm = FALSE)"},{"path":"/reference/my.pww.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate wet_wet parameter — my.pww","text":"x vector numbers na.rm logical, NA cleaned","code":""},{"path":"/reference/my.pww.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate wet_wet parameter — my.pww","text":"numeric result","code":""},{"path":"/reference/my.pww.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate wet_wet parameter — my.pww","text":"","code":"if (FALSE) { my.pww(x) }"},{"path":"/reference/my.skew.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate pcp_skew parameter — my.skew","title":"Function to calculate pcp_skew parameter — my.skew","text":"Function calculate pcp_skew parameter","code":""},{"path":"/reference/my.skew.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate pcp_skew parameter — my.skew","text":"","code":"my.skew(x, na.rm = FALSE)"},{"path":"/reference/my.skew.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate pcp_skew parameter — my.skew","text":"x vector numbers na.rm logical, NA cleaned","code":""},{"path":"/reference/my.skew.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate pcp_skew parameter — my.skew","text":"numeric result","code":""},{"path":"/reference/my.skew.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate pcp_skew parameter — my.skew","text":"","code":"if (FALSE) { my.skew(x) }"},{"path":"/reference/nyears.html","id":null,"dir":"Reference","previous_headings":"","what":"Count number of years data available for variable — nyears","title":"Count number of years data available for variable — nyears","text":"Count number years data available variable","code":""},{"path":"/reference/nyears.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count number of years data available for variable — nyears","text":"","code":"nyears(df, col)"},{"path":"/reference/nyears.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count number of years data available for variable — nyears","text":"df dataframe DATE column least one variable column. col variable column name","code":""},{"path":"/reference/nyears.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count number of years data available for variable — nyears","text":"numeric number year data available.","code":""},{"path":"/reference/nyears.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count number of years data available for variable — nyears","text":"","code":"if (FALSE) { nyears(df, \"PCP\") }"},{"path":"/reference/plot_cal_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting calibration data — plot_cal_data","title":"Plotting calibration data — plot_cal_data","text":"Plotting calibration data","code":""},{"path":"/reference/plot_cal_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting calibration data — plot_cal_data","text":"","code":"plot_cal_data(df, stations, variables = levels(as.factor(df$Variables)))"},{"path":"/reference/plot_cal_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting calibration data — plot_cal_data","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) stations character vector listing stations, selected figure. variables optional parameter character vector, parameters figure.","code":""},{"path":"/reference/plot_cal_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting calibration data — plot_cal_data","text":"plotly object interactive figure.","code":""},{"path":"/reference/plot_cal_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting calibration data — plot_cal_data","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_cal_data(cal_data$data, stations = c(\"1\", \"2\", \"3\",\"10\"), variables = c(\"PT\", \"Q\")) 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each month between parts of nutrient and total — plot_fractions","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"Plot regression fractions month parts nutrient total","code":""},{"path":"/reference/plot_fractions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"","code":"plot_fractions(df, station, total_var, min_vars)"},{"path":"/reference/plot_fractions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed). station character vector indicating station/s, selected figure. total_var character vector variable selected represent total certain variable. min_vars character vector variable/s selected represent mineral organic certain variable.","code":""},{"path":"/reference/plot_fractions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"list two ggplot objects: regression - monthly regression plots total vs part (.e. mineral organic), fraction - monthly fraction values.","code":""},{"path":"/reference/plot_fractions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_fractions(cal_data$data, c(\"4\"), c(\"NT\"), c(\"N-NO3\", \"N-NH4\", \"N-NO2\")) #> $regression #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()` #> Caused by error in `check_output()`: #> ! out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()` #> Caused by error in `check_output()`: #> ! out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> #> $fraction #> plot_fractions(cal_data$data, c(\"4\"), c(\"PT\"), c(\"P-PO4\")) #> $regression #> #> $fraction #>"},{"path":"/reference/plot_many.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting stations with few data points — plot_many","title":"Plotting stations with few data points — plot_many","text":"Plotting stations data points","code":""},{"path":"/reference/plot_many.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting stations with few data points — plot_many","text":"","code":"plot_many(df, drop_st = c())"},{"path":"/reference/plot_many.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting stations with few data points — plot_many","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) drop_st optional parameter character vector, stations excluded figure. Stations lot data plotted plot_one function.","code":""},{"path":"/reference/plot_many.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting stations with few data points — plot_many","text":"plotly object interactive figure","code":""},{"path":"/reference/plot_many.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting stations with few data points — plot_many","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_many(cal_data$data, drop_st = c(\"4\")) {\"x\":{\"data\":[{\"x\":[1205193600,1206403200,1208217600,1209340800,1210550400,1211500800,1226880000,1228089600,1229299200,1236556800,1237161600,1238371200,1267401600,1269820800,1270512000,1271030400,1274659200,1275264000,1289174400,1290384000,1367193600,1387152000,1388361600,1389571200,1395619200,1395878400,1396828800,1511481600,1513641600,1515024000,1515628800,1517270400],\"y\":[0.05,0.05,0.05,0.05,0.05,0.05,0.83,0.25,0.51,1.89,0.25,0.25,0.25,0.05,0.05,0.05,0.53,0.64,0.4,0.05,0.25,0.25,0.92,0.25,0.25,0.25,0.25,0.25,0.25,0.25,0.25,0.25],\"text\":[\"DATE: 2008-03-11
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points — plot_map","title":"Preparing interactive map of monitoring points — plot_map","text":"Preparing interactive map monitoring points","code":""},{"path":"/reference/plot_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preparing interactive map of monitoring points — plot_map","text":"","code":"plot_map(df, df_station, rch, shp)"},{"path":"/reference/plot_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preparing interactive map of monitoring points — plot_map","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed). df_station dataframe formatted station data (ID, geometry columns needed). rch sf dataframe reaches (id, geometry columns needed). shp sf dataframe basin (name, geometry columns needed).","code":""},{"path":"/reference/plot_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preparing interactive map of monitoring points — plot_map","text":"leaflet object interactive map monitoring data opening pressing points.","code":""},{"path":"/reference/plot_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preparing interactive map of monitoring points — plot_map","text":"","code":"library(sf) #> Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") reach_path <- system.file(\"extdata\", \"GIS/reaches.shp\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") cal_data <- load_template(temp_path, 4326) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" reach <- st_transform(st_read(reach_path), 4326) #> Reading layer `reaches' from data source #> `C:\\Users\\laptop\\AppData\\Local\\Temp\\Rtmpa2dTAZ\\temp_libpath1bf81946cd3\\svatools\\extdata\\GIS\\reaches.shp' #> using driver `ESRI Shapefile' #> Simple feature collection with 125 features and 2 fields #> Geometry type: LINESTRING #> Dimension: XY #> Bounding box: xmin: 469806.3 ymin: 519299 xmax: 484124.6 ymax: 533957.5 #> Projected CRS: ETRF2000-PL / CS92 basin <-st_transform(st_read(basin_path), 4326) #> Reading layer `basin' from data source #> `C:\\Users\\laptop\\AppData\\Local\\Temp\\Rtmpa2dTAZ\\temp_libpath1bf81946cd3\\svatools\\extdata\\GIS\\basin.shp' #> using driver `ESRI Shapefile' #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension: XY #> Bounding box: xmin: 4898668 ymin: 3304508 xmax: 4915266 ymax: 3319644 #> Projected CRS: ETRS89-extended / LAEA Europe plot_map(cal_data$data, cal_data$stations, reach, basin) {\"x\":{\"options\":{\"crs\":{\"crsClass\":\"L.CRS.EPSG3857\",\"code\":null,\"proj4def\":null,\"projectedBounds\":null,\"options\":{}}},\"calls\":[{\"method\":\"addProviderTiles\",\"args\":[\"OpenStreetMap\",null,\"OSM\",{\"errorTileUrl\":\"\",\"noWrap\":false,\"detectRetina\":false}]},{\"method\":\"addProviderTiles\",\"args\":[\"Esri.WorldImagery\",null,\"Imagery\",{\"errorTileUrl\":\"\",\"noWrap\":false,\"detectRetina\":false}]},{\"method\":\"addProviderTiles\",\"args\":[\"OpenTopoMap\",null,\"Topography\",{\"errorTileUrl\":\"\",\"noWrap\":false,\"detectRetina\":false}]},{\"method\":\"addPolygons\",\"args\":[[[[{\"lng\":[18.7502099240057,18.7504926933119,18.7506871710007,18.7509986800875,18.7512071166416,18.7508115137972,18.7505916199336,18.7505197040129,18.7506401597032,18.7508813711807,18.7507345763972,18.7501611148954,18.7502514424212,18.7506534536152,18.7513363458376,18.7514593333211,18.7516379752494,18.7521121762843,18.7523785823232,18.752837084082,18.753563554398,18.7543039355989,18.7545425346942,18.7546205610143,18.7555547559262,18.7557183852302,18.755808114847,18.7559436334374,18.7562407226947,18.7569373483367,18.7572775706597,18.7583271966211,18.7587571304625,18.758846349337,18.7590546897903,18.7594258430317,18.7594422986243,18.7592984080839,18.7592111677966,18.7590807784513,18.7590103444975,18.759070592959,18.7589271605303,18.7591508518052,18.7597425012178,18.7603033153805,18.7619294488274,18.762772651462,18.7640736140535,18.7643260406867,18.7643416876192,18.7643431366321,18.7640635755685,18.7631646363843,18.7631519465912,18.7635974855205,18.7638787299317,18.7657705493919,18.7659483988432,18.7660681952442,18.7662609148815,18.7665567348396,18.7670889798359,18.7678583626752,18.7685976169044,18.7687462352002,18.7689247840753,18.7692221090683,18.76930128557,18.7696732799711,18.7695566794069,18.7692764145533,18.7686412992974,18.7683758127983,18.7679035647087,18.7670185594167,18.7665017056602,18.7659998683725,18.7648771307765,18.7642279800905,18.7635338790701,18.763342430576,18.7631961336791,18.7633450492251,18.7639236967941,18.7640724614786,18.763896130436,18.7627886019696,18.7617253545133,18.7598504868083,18.7592753604772,18.7585673676424,18.7568833723323,18.7567658741343,18.7567088201532,18.7565629722895,18.7568461234213,18.7564946391979,18.7562885539669,18.7561263416647,18.7558313157327,18.7552428718211,18.755155283476,18.75489146842,18.7546573050474,18.7545560038527,18.754719564037,18.7553416888213,18.7561257577379,18.7566897715909,18.756706737367,18.7562497337737,18.7556151629093,18.755365282292,18.7553819589672,18.755663384716,18.756284471264,18.7565813629803,18.7567597753474,18.7567759923388,18.7566741830516,18.7570005097336,18.7576958295215,18.758006681697,18.7583924796056,18.7596370404868,18.7601399077081,18.7609076281592,18.7617209832863,18.7624165023874,18.7628604665398,18.7621975146983,18.7613722381999,18.7604742979646,18.7595021830991,18.7589728097015,18.7582209012121,18.7573053846903,18.75618233592,18.7554585855524,18.7545579421626,18.753673333048,18.7531441303318,18.7527030013065,18.7523519695625,18.7524592617534,18.7527883945441,18.753237639499,18.7536407400864,18.7537921074785,18.7538247697243,18.7532953336809,18.7528853669791,18.7526951052413,18.7519467699306,18.7520671947247,18.7524530737031,18.7522630768024,18.7520285278717,18.7517632285112,18.7511132741856,18.7500047204835,18.7499160153213,18.7494884865192,18.749298362125,18.7489600064725,18.7485020264981,18.7480148730685,18.7471145060678,18.746420774244,18.7459776644383,18.7455336165549,18.7451494678098,18.744765161691,18.7443080665634,18.7438218072708,18.7433954597056,18.7432051743331,18.7431480007496,18.7433121142697,18.7436973535552,18.7443777054692,18.7448512719434,18.7453399656794,18.7458583422864,18.7460218609611,18.7460230047748,18.7459220356903,18.7456886460065,18.7456604683802,18.7456628112005,18.7456494515216,18.745443570406,18.7451484209989,18.7448539509131,18.7445893791049,18.7444141327971,18.7443273217981,18.7442986816499,18.7442844925026,18.7441672114052,18.7441682063312,18.7443321886255,18.7445844427194,18.7449994172933,18.7456064283742,18.7460648008676,18.746582643235,18.7468934190588,18.7473962831945,18.7476186762919,18.7476194511092,18.7473396050638,18.7470012739343,18.7466482859084,18.7465466502053,18.7465330794998,18.7464602984919,18.7462695128279,18.7461673622238,18.7461543130422,18.7462140940279,18.7462439324602,18.7464966223444,18.7469415411975,18.7470023543017,18.7468287721994,18.7464915605989,18.7460222713869,18.7451106440782,18.7448180603188,18.7445695596948,18.744188259059,18.7434820498631,18.7431736819231,18.7427632500902,18.7425592754916,18.742545409654,18.7425614877859,18.7428149543502,18.7432172861389,18.7436780717777,18.7438567720383,18.7440935696852,18.7446251584884,18.7452304448501,18.7457915531925,18.7463227763357,18.7464992285063,18.7466747487117,18.7468806212503,18.7471012040726,18.7474110767007,18.7477509872302,18.7482540371705,18.7485206039952,18.7490232354614,18.7496430774134,18.7499972292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values of single station to interactive monthly box plots — plot_monthly","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"Aggregating values single station interactive monthly box plots","code":""},{"path":"/reference/plot_monthly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"","code":"plot_monthly(df, station, variables = levels(as.factor(df$Variables)))"},{"path":"/reference/plot_monthly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character indicating station, selected figure. variables optional parameter character vector, parameters figure.","code":""},{"path":"/reference/plot_monthly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"plotly object interactive figure","code":""},{"path":"/reference/plot_monthly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_monthly(cal_data$data, station = \"4\") 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figure.","code":""},{"path":"/reference/plot_monthly_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly_one","text":"plotly object interactive figure","code":""},{"path":"/reference/plot_monthly_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly_one","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_monthly_one(cal_data$data, station = \"4\", drop_variables = c(\"Q\", \"Q1h\", \"Q2h\")) 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N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"Plot N min NT regression month fraction Nmin NT","code":""},{"path":"/reference/plot_NminTN.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"","code":"plot_NminTN(df, station)"},{"path":"/reference/plot_NminTN.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character vector indicating station/s, selected figure.","code":""},{"path":"/reference/plot_NminTN.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"list two ggplot objects: regression - monthly regression plots NT vs Nmin, fraction - monthly fraction values Nmin/NT","code":""},{"path":"/reference/plot_NminTN.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_NminTN(cal_data$data, station = c(\"4\")) #> $regretion #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()`: #> out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()`: #> out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> #> $fraction #>"},{"path":"/reference/plot_Nmin_NT.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"Plot N min NT regression month fraction Nmin NT","code":""},{"path":"/reference/plot_Nmin_NT.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"","code":"plot_Nmin_NT(df, station)"},{"path":"/reference/plot_Nmin_NT.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character vector indicating station/s, selected figure.","code":""},{"path":"/reference/plot_Nmin_NT.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"list two ggplot objects: regression - monthly regression plots NT vs Nmin, fraction - monthly fraction values Nmin/NT","code":""},{"path":"/reference/plot_Nmin_NT.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_Nmin_NT(cal_data$data, station = c(\"4\")) #> $regretion #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()`: #> out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()`: #> out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> #> $fraction #>"},{"path":"/reference/plot_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting single, data rich station — plot_one","title":"Plotting single, data rich station — plot_one","text":"Plotting single, data rich station","code":""},{"path":"/reference/plot_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting single, data rich station — plot_one","text":"","code":"plot_one(df, station)"},{"path":"/reference/plot_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting single, data rich station — plot_one","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character indicating station, selected figure.","code":""},{"path":"/reference/plot_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting single, data rich station — plot_one","text":"plotly object interactive figure.","code":""},{"path":"/reference/plot_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting single, data rich station — plot_one","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_one(cal_data$data, station = \"4\") #> Warning: The `keep` argument of `group_map()` is deprecated as of dplyr 1.0.0. #> Please use the `.keep` argument instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated. #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors {\"x\":{\"data\":[{\"x\":[\"2008-03-11 00:00:00.000000\",\"2008-03-25 00:00:00.000000\",\"2008-04-04 00:00:00.000000\",\"2008-04-15 00:00:00.000000\",\"2008-04-28 00:00:00.000000\",\"2008-05-12 00:00:00.000000\",\"2008-05-23 00:00:00.000000\",\"2008-06-04 00:00:00.000000\",\"2008-06-16 00:00:00.000000\",\"2008-07-01 00:00:00.000000\",\"2008-07-11 00:00:00.000000\",\"2008-07-15 00:00:00.000000\",\"2008-07-28 00:00:00.000000\",\"2008-08-08 00:00:00.000000\",\"2008-08-25 00:00:00.000000\",\"2008-09-10 00:00:00.000000\",\"2008-09-22 00:00:00.000000\",\"2008-10-06 00:00:00.000000\",\"2008-10-20 00:00:00.000000\",\"2008-11-03 00:00:00.000000\",\"2008-11-17 00:00:00.000000\",\"2008-12-01 00:00:00.000000\",\"2008-12-15 00:00:00.000000\",\"2009-01-21 00:00:00.000000\",\"2009-02-02 00:00:00.000000\",\"2009-02-16 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plot","code":""},{"path":"/reference/plot_PminTP.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Pmin fraction of PT — plot_PminTP","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_PminTP(cal_data$data, station = unique(cal_data$stations$ID))"},{"path":"/reference/plot_Pmin_PT.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Pmin fraction of PT — plot_Pmin_PT","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"Plot Pmin fraction PT","code":""},{"path":"/reference/plot_Pmin_PT.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"","code":"plot_Pmin_PT(df, station)"},{"path":"/reference/plot_Pmin_PT.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character vector indicating station/s, selected figure.","code":""},{"path":"/reference/plot_Pmin_PT.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"ggplot object monthly Pmin/PT fraction box plot","code":""},{"path":"/reference/plot_Pmin_PT.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_Pmin_PT(cal_data$data, station = unique(station$ID)) #> Error in filter(., Station %in% station & Variables %in% c(\"P-PO4\", \"PT\")): Problem while computing `..1 = Station %in% station & Variables %in% #> c(\"P-PO4\", \"PT\")`. #> Caused by error in `unique()`: #> ! object 'station' not found"},{"path":"/reference/plot_ts_fig.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to prepare static time series figure for single station — plot_ts_fig","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"Function prepare static time series figure single station","code":""},{"path":"/reference/plot_ts_fig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"","code":"plot_ts_fig(station, df)"},{"path":"/reference/plot_ts_fig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"station character indicating station, selected figure. df dataframe formatted data (Station, DATE, Variables Values columns needed)","code":""},{"path":"/reference/plot_ts_fig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"ggplot object static figure.","code":""},{"path":"/reference/plot_ts_fig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) plot_ts_fig(\"4\", cal_data$data) }"},{"path":"/reference/plot_weather.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare the plotly figure for weather data — plot_weather","title":"Prepare the plotly figure for weather data — plot_weather","text":"Prepare plotly figure weather data","code":""},{"path":"/reference/plot_weather.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare the plotly figure for weather data — plot_weather","text":"","code":"plot_weather(meteo_lst, par, period = \"day\", fn_summarize = \"mean\")"},{"path":"/reference/plot_weather.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare the plotly figure for weather data — plot_weather","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par character marking weather variable extract (.e. \"PCP\", \"SLR\", etc). period character describing, time interval display (default \"day\", examples \"week\", \"month\", etc). fn_summarize function recalculate time interval (default \"mean\", examples \"median\", \"sum\", etc).","code":""},{"path":"/reference/plot_weather.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare the plotly figure for weather data — plot_weather","text":"plotly figure object displayed weather data","code":""},{"path":"/reference/plot_weather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare the plotly figure for weather data — plot_weather","text":"","code":"temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 4326) #> [1] \"Loading data from template.\" 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plot_weather_compare","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"Plotting figure comparing two datasets weather data","code":""},{"path":"/reference/plot_weather_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"","code":"plot_weather_compare( meteo_lst1, meteo_lst2, par, period = \"day\", fn_summarize = \"mean\", name_set1 = \"dataset 1\", name_set2 = \"dataset 2\" )"},{"path":"/reference/plot_weather_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"meteo_lst1 first nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). meteo_lst2 second nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par character marking weather variable extract (.e. \"PCP\", \"SLR\", etc). period character describing, time interval display (default \"day\", examples \"week\", \"month\", etc). fn_summarize function recalculate time interval (default \"mean\", examples \"median\", \"sum\", etc). name_set1 character name first dataset. name_set2 character name second dataset.","code":""},{"path":"/reference/plot_weather_compare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"plotly figure object displayed weather data two datasets.","code":""},{"path":"/reference/plot_weather_compare.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"","code":"temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst1 <- load_template(temp_path, 4326) #> [1] \"Loading data from template.\" #> [1] \"Reading station ID9 data.\" #> [1] \"Reading station ID3 data.\" #> [1] \"Reading station ID10 data.\" #> [1] \"Reading station ID11 data.\" #> [1] \"Reading station ID12 data.\" #> [1] \"Reading station ID13 data.\" #> [1] \"Reading station ID1 data.\" #> [1] \"Reading station ID8 data.\" #> [1] \"Reading station ID2 data.\" #> [1] \"Reading station ID4 data.\" #> [1] \"Reading station ID5 data.\" #> [1] \"Reading station ID6 data.\" #> [1] \"Reading station ID7 data.\" #> [1] \"Reading station ID14 data.\" #> [1] \"Loading of data is finished.\" temp_path <- system.file(\"extdata\", \"weather_data_raw.xlsx\", package = \"svatools\") met_lst2 <- load_template(temp_path, 4326) #> [1] \"Loading data from template.\" #> [1] \"Reading station ID9 data.\" #> Warning: RELHUM variable values at station ID9 should be between 0 and 1. Please check, correct your input data and reload template!!! #> Warning: PCP variable values at station ID9 should be between 0 and 1825 mm/d (maximum 1-day recorded rainfall). Please check, correct your input data and reload template!!! #> [1] \"Reading station ID3 data.\" #> Warning: RELHUM variable values at station ID3 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID10 data.\" #> Warning: RELHUM variable values at station ID10 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID11 data.\" #> [1] \"Reading station ID12 data.\" #> Warning: RELHUM variable values at station ID12 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID13 data.\" #> Warning: RELHUM variable values at station ID13 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID1 data.\" #> Warning: RELHUM variable values at station ID1 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID8 data.\" #> Warning: RELHUM variable values at station ID8 should be between 0 and 1. Please check, correct your input data and reload template!!! #> Warning: WNDSPD variable values at station ID8 should be between 0 and 104 m/s (the highest recorded natural surface wind velocity). Please check, correct your input data and reload template!!! #> [1] \"Reading station ID2 data.\" #> [1] \"Reading station ID4 data.\" #> [1] \"Reading station ID5 data.\" #> [1] \"Reading station ID6 data.\" #> [1] \"Reading station ID7 data.\" #> [1] \"Loading of data is finished.\" plot_weather_compare(met_lst1, met_lst2, \"PCP\", \"month\", \"mean\", \"clean\", \"raw\") #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors {\"x\":{\"data\":[{\"x\":[\"2018-04-01 00:00:00.000000\",\"2018-05-01 00:00:00.000000\",\"2018-06-01 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the plotly figure for weather data — plot_weather_fig","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"Prepare plotly figure weather data","code":""},{"path":"/reference/plot_weather_fig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"","code":"plot_weather_fig(meteo_lst, par, period = \"day\", fn_summarize = \"mean\")"},{"path":"/reference/plot_weather_fig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par character marking weather variable extract (.e. \"PCP\", \"SLR\", etc). period character describing, time interval display (default \"day\", examples \"week\", \"month\", etc). fn_summarize function recalculate time interval (default \"mean\", examples \"median\", \"sum\", etc).","code":""},{"path":"/reference/plot_weather_fig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"plotly figure object displayed weather data","code":""},{"path":"/reference/plot_weather_fig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"","code":"##get_weather_fig(meteo_lst, \"PCP\", \"month\", \"sum\")"},{"path":"/reference/plot_weather_fig_compare.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"Plotting figure comparing two datasets weather data","code":""},{"path":"/reference/plot_weather_fig_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"","code":"plot_weather_fig_compare( meteo_lst1, meteo_lst2, par, period = \"day\", fn_summarize = \"mean\", name_set1 = \"raw\", name_set2 = \"clean\" )"},{"path":"/reference/plot_weather_fig_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"meteo_lst1 first nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). meteo_lst2 second nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par character marking weather variable extract (.e. \"PCP\", \"SLR\", etc). period character describing, time interval display (default \"day\", examples \"week\", \"month\", etc). fn_summarize function recalculate time interval (default \"mean\", examples \"median\", \"sum\", etc). name_set1 character name first dataset. name_set2 character name second dataset.","code":""},{"path":"/reference/plot_weather_fig_compare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"plotly figure object displayed weather data two datasets.","code":""},{"path":"/reference/plot_weather_fig_compare.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"","code":"##plot_weather_fig_compare(meteo_lst1, meteo_lst2, \"PCP\", \"month\", \"mean\", \"raw\", \"clean\")"},{"path":"/reference/prepare_wgn.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to generate wgn data for the model — prepare_wgn","title":"Function to generate wgn data for the model — prepare_wgn","text":"Function generate wgn data model","code":""},{"path":"/reference/prepare_wgn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to generate wgn data for the model — prepare_wgn","text":"","code":"prepare_wgn( meteo_lst, TMP_MAX = NULL, TMP_MIN = NULL, PCP = NULL, RELHUM = NULL, WNDSPD = NULL, MAXHHR = NULL, SLR = NULL )"},{"path":"/reference/prepare_wgn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to generate wgn data for the model — prepare_wgn","text":"meteo_lst meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). TMP_MAX dataframe two columns: DATE : POSIXct, TMP_MAX : num. Optional (default NULL). parameter refers data, used instead, TMP_MAX variable missing station. TMP_MIN dataframe two columns: DATE : POSIXct, TMP_MIN : num. Optional (default NULL). parameter refers data, used instead, TMP_MIN missing station. PCP dataframe two columns: DATE : POSIXct, PCP : num. Optional (default NULL). parameter refers data, used instead, PCP missing station. RELHUM dataframe two columns: DATE : POSIXct, RELHUM : num. Optional (default NULL). parameter refers data, used instead, RELHUM missing station. WNDSPD dataframe two columns: DATE : POSIXct, WNDSPD : num. Optional (default NULL). parameter refers data, used instead, WNDSPD missing station. MAXHHR dataframe two columns: DATE : POSIXct, MAXHHR : num. Optional (default NULL). parameter refers data, used instead, MAXHHR missing station. SLR dataframe two columns: DATE : POSIXct, SLR : num. Optional (default NULL). parameter refers data, used instead, SLR missing station.","code":""},{"path":"/reference/prepare_wgn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to generate wgn data for the model — prepare_wgn","text":"list two dataframes: wgn_st - wgn station data, wgn_data - wgn data","code":""},{"path":"/reference/prepare_wgn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to generate wgn data for the model — prepare_wgn","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) TMP_MAX <- met_lst$data$ID10$TMP_MAX TMP_MIN <- met_lst$data$ID10$TMP_MIN PCP <- met_lst$data$ID9$PCP RELHUM = met_lst$data$ID9$RELHUM WNDSPD <- met_lst$data$ID12$WNDSPD MAXHHR <- met_lst$data$ID11$MAXHHR SLR <- met_lst$data$ID9$SLR ##Does the thing wgn <- prepare_wgn(met_lst, TMP_MAX, TMP_MIN, PCP, RELHUM, WNDSPD, MAXHHR, SLR) }"},{"path":"/reference/rch.html","id":null,"dir":"Reference","previous_headings":"","what":"River data — rch","title":"River data — rch","text":"Example GIS river dataset Polish case study","code":""},{"path":"/reference/rch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"River data — rch","text":"","code":"rch"},{"path":"/reference/rch.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"River data — rch","text":"sf data.frame 2 variables: id id reach. geometry Geometry objects.","code":""},{"path":"/reference/shp.html","id":null,"dir":"Reference","previous_headings":"","what":"Basin boundary data — shp","title":"Basin boundary data — shp","text":"Example basin GIS dataset Polish case study","code":""},{"path":"/reference/shp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basin boundary data — shp","text":"","code":"shp"},{"path":"/reference/shp.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Basin boundary data — shp","text":"data.frame 2 variables: name Name catchment. geometry Geometry objects.","code":""},{"path":"/reference/sp.na.omit.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to remove NAs in sp dataframe — sp.na.omit","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"Function remove NAs sp dataframe","code":""},{"path":"/reference/sp.na.omit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"","code":"sp.na.omit(x, margin = 1)"},{"path":"/reference/sp.na.omit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"x sp dataframe margin numeric. Optional, default 1 remove rows, 2 removing columns.","code":""},{"path":"/reference/sp.na.omit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"sp dataframe without NAs data","code":""},{"path":"/reference/sp.na.omit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"","code":"if (FALSE) { sp_df <- sp.na.omit(sp_df) }"},{"path":"/reference/transform_to_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Transforming list of sp dataframes to list of list — transform_to_list","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"Transforming list sp dataframes list list","code":""},{"path":"/reference/transform_to_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"","code":"transform_to_list(list_sp, start_date, end_date)"},{"path":"/reference/transform_to_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"list_sp list SpatialPointsDataFrames (weather variable) prepared interpolate function. start_date time series starting date string. Example \"1998-01-01\". end_date time series ending date string. Example \"2021-12-31\".","code":""},{"path":"/reference/transform_to_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat).","code":""},{"path":"/reference/transform_to_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"","code":"if (FALSE) { ###First step to get interpolation results temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) result <- interpolate(met_lst, \"./output/\", basin_path, DEM_path, 2000) ###Second step converting interpolation results to list of list format. start_date <- svatools:::get_dates(met_lst)$min_date end_date <- svatools:::get_dates(met_lst)$max_date int_met_lst <- transform_to_list(result, start_date, end_date) }"},{"path":"/reference/update_wst_id.html","id":null,"dir":"Reference","previous_headings":"","what":"Updating sqlite database table with wst_id information — update_wst_id","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"Updating sqlite database table wst_id information","code":""},{"path":"/reference/update_wst_id.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"","code":"update_wst_id(tname, db_path, wst_cli)"},{"path":"/reference/update_wst_id.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"tname character table name (example \"hru_con\") db_path character sqlite database (example \"./output/project.sqlite\") wst_cli data.frame weather_sta_cli table","code":""},{"path":"/reference/update_wst_id.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"updates sql database table amended wst_id (id closest weather station)","code":""},{"path":"/reference/update_wst_id.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"","code":"if (FALSE) { update_wst_id(\"hru_con\", \"./output/project.sqlite\", weather_sta_cli) }"},{"path":"/reference/write_atmo_cli.html","id":null,"dir":"Reference","previous_headings":"","what":"Write 'atmo.cli' file — write_atmo_cli","title":"Write 'atmo.cli' file — write_atmo_cli","text":"Write 'atmo.cli' file","code":""},{"path":"/reference/write_atmo_cli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write 'atmo.cli' file — write_atmo_cli","text":"","code":"write_atmo_cli(df, write_path, t_ext = \"year\")"},{"path":"/reference/write_atmo_cli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write 'atmo.cli' file — write_atmo_cli","text":"df dafaframe \"DATE\", \"NH4_RF\", \"NO3_RF\" , \"NH4_DRY\" \"NO3_DRY\" columns obtained get_atmo_dep) function. write_path path folder results written. t_ext string, prepare file: 'year' yearly averages, 'month' - monthly averages 'annual' average period. Optional (default - \"year\"). df monthly data, 'month' used.","code":""},{"path":"/reference/write_atmo_cli.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write 'atmo.cli' file — write_atmo_cli","text":"'atmo.cli' file one station catchment.","code":""},{"path":"/reference/write_atmo_cli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write 'atmo.cli' file — write_atmo_cli","text":"","code":"if (FALSE) { basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") df <- get_atmo_dep(basin_path) write_atmo_cli(df, \"./output/\") }"},{"path":"/reference/write_input_files.html","id":null,"dir":"Reference","previous_headings":"","what":"Writing input files (all except TMP) — write_input_files","title":"Writing input files (all except TMP) — write_input_files","text":"Writing input files (except TMP)","code":""},{"path":"/reference/write_input_files.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Writing input files (all except TMP) — write_input_files","text":"","code":"write_input_files(write_path, sp_df, meteo_lst, par)"},{"path":"/reference/write_input_files.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Writing input files (all except TMP) — write_input_files","text":"write_path path folder results written. sp_df SpatialPointsDataFrame resulting interpolated data. meteo_lst nested list lists dataframes. par weather variable (.e. \"PCP\", \"SLR\", etc).","code":""},{"path":"/reference/write_input_files.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Writing input files (all except TMP) — write_input_files","text":"weather data text files virtual stations (created interpolation) format usable SWAT model.","code":""},{"path":"/reference/write_input_files.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Writing input files (all except TMP) — write_input_files","text":"","code":"if (FALSE) { write_input_files(\"./output/\", sp_df, \"PCP\") }"},{"path":"/reference/write_input_files_tmp.html","id":null,"dir":"Reference","previous_headings":"","what":"Writing input files for TMP data — write_input_files_tmp","title":"Writing input files for TMP data — write_input_files_tmp","text":"Writing input files TMP data","code":""},{"path":"/reference/write_input_files_tmp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Writing input files for TMP data — write_input_files_tmp","text":"","code":"write_input_files_tmp(write_path, sp_df_mx, sp_df_mn, meteo_lst)"},{"path":"/reference/write_input_files_tmp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Writing input files for TMP data — write_input_files_tmp","text":"write_path path folder results written. sp_df_mx SpatialPointsDataFrame resulting interpolated data TMP_MAX variable. sp_df_mn SpatialPointsDataFrame resulting interpolated data TMP_MIN variable. meteo_lst nested list lists dataframes.","code":""},{"path":"/reference/write_input_files_tmp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Writing input files for TMP data — write_input_files_tmp","text":"temperature weather data text files virtual stations (created interpolation) format usable SWAT model.","code":""},{"path":"/reference/write_input_files_tmp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Writing input files for TMP data — write_input_files_tmp","text":"","code":"if (FALSE) { write_input_files_tmp(\"./output/\", sp_df_mx, sp_df_mn) }"},{"path":"/reference/write_ref_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Writing reference file for weather data — write_ref_file","title":"Writing reference file for weather data — write_ref_file","text":"Writing reference file weather data","code":""},{"path":"/reference/write_ref_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Writing reference file for weather data — write_ref_file","text":"","code":"write_ref_file(write_path, sp_df, par)"},{"path":"/reference/write_ref_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Writing reference file for weather data — write_ref_file","text":"write_path path folder results written. sp_df SpatialPointsDataFrame resulting interpolated data. par weather variable (.e. \"PCP\", \"SLR\", etc).","code":""},{"path":"/reference/write_ref_file.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Writing reference file for weather data — write_ref_file","text":"weather virtual stations (created interpolation) reference file format usable SWAT model.","code":""},{"path":"/reference/write_ref_file.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Writing reference file for weather data — write_ref_file","text":"","code":"if (FALSE) { write_ref_file(\"./output/\", sp_df, \"PCP\") }"},{"path":"/reference/write_wgnmaker_files.html","id":null,"dir":"Reference","previous_headings":"","what":"Preparing input files for WGNmaker. — write_wgnmaker_files","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"WGNmaker excel macro downloaded following link: https://swat.tamu.edu/media/41583/wgen-excel.zip","code":""},{"path":"/reference/write_wgnmaker_files.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"","code":"write_wgnmaker_files(write_path, meteo_lst, wgn_stations_lst, station_name)"},{"path":"/reference/write_wgnmaker_files.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"write_path path folder results written. meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). wgn_stations_lst List selected stations parameter used data preparation. Example list(PCP = \"ID12\", SLR = \"ID11\", RELHUM = \"ID12\", TMP_MAX = \"ID12\", TMP_MIN = \"ID12\", WNDSPD = \"ID12\", MAXHHR = \"ID12\") station_name string name station.","code":""},{"path":"/reference/write_wgnmaker_files.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"Excel files needed WGNmaker","code":""},{"path":"/reference/write_wgnmaker_files.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"","code":"if (FALSE) { write_wgnmaker_files(\"./wgn/\", meteo_lst, wgn_stations_lst, \"Station1\") }"},{"path":"/reference/add_missing_pcp_zero.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace empty PCP entry with 0 — add_missing_pcp_zero","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"Replace empty PCP entry 0","code":""},{"path":"/reference/add_missing_pcp_zero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"","code":"add_missing_pcp_zero(df_to_correct, df_valid = NULL, value_to_zero = 0.2)"},{"path":"/reference/add_missing_pcp_zero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"df_to_correct dataframe correct PCP variable \"DATE\" \"PCP\" columns. df_valid (optional) dataframe valid data PCP variable \"DATE\" \"PCP\" columns. Also overlap df_to_correct data. value_to_zero (optional) numeric value daily PCP. NA values can set 0 df_valid PCP values equal value_to_zero (default 0.2).","code":""},{"path":"/reference/add_missing_pcp_zero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"updated dataframe","code":""},{"path":"/reference/add_missing_pcp_zero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"","code":"if (FALSE) { met_lst$data$ID8$PCP <- add_missing_pcp_zero(met_lst$data$ID8$PCP, met_lst$data$ID9$PCP) }"},{"path":"/reference/write_weather_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Write weather meteo_lst into template .xlsx file — write_weather_template","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":"Write weather meteo_lst template .xlsx file","code":""},{"path":"/reference/write_weather_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":"","code":"write_weather_template( meteo_lst, write_path = \"\", f_name = \"weather_data.xlsx\" )"},{"path":"/reference/write_weather_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). write_path (optional) character path folder results written (default \"\"). f_name (optional) character name file (default 'weather_data.xlsx').","code":""},{"path":"/reference/write_weather_template.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":".xlsx file written template format, de read load_template function.","code":""},{"path":"/reference/write_weather_template.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":"","code":"if (FALSE) { write_weather_template(met_lst) }"},{"path":"/reference/my.pcpmhhr.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate hhr parameter — my.pcpmhhr","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"Function calculate hhr parameter","code":""},{"path":"/reference/my.pcpmhhr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"","code":"my.pcpmhhr(x, na.rm = FALSE)"},{"path":"/reference/my.pcpmhhr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"x vector numbers na.rm logical, NA cleaned","code":""},{"path":"/reference/my.pcpmhhr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"numeric result","code":""},{"path":"/reference/my.pcpmhhr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"","code":"if (FALSE) { my.pcpmhhr(x) }"},{"path":"/reference/fill_with_closest.html","id":null,"dir":"Reference","previous_headings":"","what":"Filling missing variables from the closest stations, which has data. — fill_with_closest","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"Filling missing variables closest stations, data.","code":""},{"path":"/reference/fill_with_closest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"","code":"fill_with_closest( meteo_lst, par_fill = c(\"TMP_MAX\", \"TMP_MIN\", \"PCP\", \"RELHUM\", \"WNDSPD\", \"SLR\") )"},{"path":"/reference/fill_with_closest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"meteo_lst meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). par_fill vector variables filled. Optional (default c(\"TMP_MAX\", \"TMP_MIN\",\"PCP\", \"RELHUM\", \"WNDSPD\", \"SLR\")).","code":""},{"path":"/reference/fill_with_closest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"list dataframe filled data. Returned list meteo_lst$data.","code":""},{"path":"/reference/fill_with_closest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) met_lst$data <- fill_with_closest(met_lst, c(\"TMP_MAX\", \"TMP_MIN\")) }"},{"path":"/reference/usersoil_to_sol.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert usersoil.csv to soils.sol — usersoil_to_sol","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"Convert usersoil.csv soils.sol","code":""},{"path":"/reference/usersoil_to_sol.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"","code":"usersoil_to_sol(csv_path, db_path = NULL)"},{"path":"/reference/usersoil_to_sol.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"csv_path character path csv file (example \"usersoil_lrew.csv\"\") db_path character sqlite project database (example \"output/project.sqlite\"). Optional, default NULL. used reduce soils.sol file, less soil types sqlite database usersoil csv file.","code":""},{"path":"/reference/usersoil_to_sol.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"soils.sol SWAT+ model input file","code":""},{"path":"/reference/usersoil_to_sol.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"","code":"if (FALSE) { usersoil_to_sol(\"output/usersoil_lrew.csv\") ##Or usersoil_to_sol(\"output/usersoil_lrew.csv\", \"output/project.sqlite\") }"},{"path":"/reference/get_usersoil_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to prepare user soil table for SWAT model — get_usersoil_table","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"Function prepare user soil table SWAT model","code":""},{"path":"/reference/get_usersoil_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"","code":"get_usersoil_table(csv_path, hsg = FALSE, keep_values = FALSE, nb_lyr = NA)"},{"path":"/reference/get_usersoil_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"csv_path character path csv file (example \"usersoil_lrew.csv\"). File comma separated minimum columns SNAM, NLAYERS SOL_Z, CLAY, SILT, SAND, SOL_CBN available soil layer (minimum 1). hsg Boolean, TRUE - prepare soil hydrological groups, FALSE - soil hydrological group preparation done. Optional (Default = FALSE). hsg = TRUE three additional columns input table: Impervious (allowed values \"<50cm\", \"50-100cm\", \">100cm\"), Depth (allowed values \"<60cm\", \"60-100cm\", \">100cm\") Drained (allowed values \"Y\" drained areas, \"N\" areas without working tile drains). information can found SWAT+ modeling protocol Table 3.3. keep_values Boolean character vector, TRUE - keep old values (new values left 0 NA values present input table), FALSE - keep new values. Character vector also used keep specified columns. instance, c(\"HYDGRP\", \"ROCK1\") keep values soil hydro groups rock data first layer, c(\"HYDGRP\", \"ROCK\") keep values rock data columns. Optional (Default = FALSE). nb_lyr integer, number layers resulting user soil data contain. Optional (Default = NA, stands number input data).","code":""},{"path":"/reference/get_usersoil_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"dataframe fully formatted filled table soil parameters SWAT model.","code":""},{"path":"/reference/get_usersoil_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"","code":"if (FALSE) { usersoils <- get_usersoil_table(\"table.csv\") write.csv(usersoils, \"usersoils.csv\", row.names=FALSE, quote=FALSE) }"},{"path":"/reference/plot_wgn_comparison.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot wgn parameters comparison — plot_wgn_comparison","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"Plot wgn parameters comparison","code":""},{"path":"/reference/plot_wgn_comparison.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"","code":"plot_wgn_comparison( meteo_lst1, meteo_lst2, station1, station2, type1 = \"set 1\", type2 = \"set 2\", title = \"comparison\" )"},{"path":"/reference/plot_wgn_comparison.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"meteo_lst1 first nested list lists dataframes. meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). meteo_lst2 second nested list lists dataframes. meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). station1 character, id one station first list selected comparison (example \"ID1\"). station2 character, id one station second list selected comparison (example \"ID1\"). type1 character, naming first dataset (example \"measured\"). type2 character, naming second dataset (example \"netCDF\"). title character, information added figure title","code":""},{"path":"/reference/plot_wgn_comparison.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"ggplot figure bar plots","code":""},{"path":"/reference/plot_wgn_comparison.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 4326) plot_wgn_comparison(met_lst, met_lst, \"ID9\", \"ID2\", \"Samszyce\", \"Glebokie\", \"comparison\") }"},{"path":"/reference/load_climate_lst.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract climate data into nested list of lists — load_climate_lst","title":"Extract climate data into nested list of lists — load_climate_lst","text":"Extract climate data nested list lists","code":""},{"path":"/reference/load_climate_lst.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract climate data into nested list of lists — load_climate_lst","text":"","code":"load_climate_lst(dir_path, location)"},{"path":"/reference/load_climate_lst.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract climate data into nested list of lists — load_climate_lst","text":"dir_path character, path CORDEX-BC folder (example climate/CORDEX-BC\"). location character list. case character, path provided catchment boundary file (example GIS/basin.shp). case list, nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). List list obtained using load_template() prepared excel template.","code":""},{"path":"/reference/load_climate_lst.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract climate data into nested list of lists — load_climate_lst","text":"Nested lists lists. First nesting level RCP, second RCM model numbers, rest meteo_lst. part used package functions (example plot_weather(result$rcp26$1), \"PCP\", \"month\", \"sum\").","code":""},{"path":"/reference/load_climate_lst.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract climate data into nested list of lists — load_climate_lst","text":"","code":"if (FALSE) { basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") data_path <- \"climate/CORDEX-BC\" result <- load_climate_lst(data_path, basin_path) }"},{"path":"/reference/update_wst_txt.html","id":null,"dir":"Reference","previous_headings":"","what":"Updating wst_id information in model text files — update_wst_txt","title":"Updating wst_id information in model text files — update_wst_txt","text":"Updating wst_id information model text files","code":""},{"path":"/reference/update_wst_txt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updating wst_id information in model text files — update_wst_txt","text":"","code":"update_wst_txt(fname, write_path, wst_sf, spacing, folder_to_save = \"temp\")"},{"path":"/reference/update_wst_txt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updating wst_id information in model text files — update_wst_txt","text":"fname character, model file name (example 'aquifer.con'). write_path character, path SWAT+ txtinout folder (example \"my_model\"). wst_sf sf dataframe weather station information. contain \"name\" \"geometry\" columns. spacing character, contain information spacing columns file. Example c('%8s', '%-12s', rep('%12s', 5), '%8s', '%16s', rep('%8s', 4)). folder_to_save character, folder save function results. Optional, default = \"temp\".","code":""},{"path":"/reference/update_wst_txt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updating wst_id information in model text files — update_wst_txt","text":"Updated text file, updated station names (nearest stations features).","code":""},{"path":"/reference/update_wst_txt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Updating wst_id information in model text files — update_wst_txt","text":"","code":"if (FALSE) { spacing <- c('%8s', '%-12s', rep('%12s', 5), '%8s', '%16s', rep('%8s', 4), '%12s', '%8s', rep('%12s', 2)) update_wst_txt(\"reservoir.con\", \"model_folder\", wst_sf, spacing) }"},{"path":"/reference/read_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Loading SWAT+ text files into dataframe — read_tbl","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"Loading SWAT+ text files dataframe","code":""},{"path":"/reference/read_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"","code":"read_tbl(tbl_name, proj_path, row_data_start = 3, row_col_names = 2)"},{"path":"/reference/read_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"tbl_name character, name file read eaxmple ('rout_unit.con'). proj_path character, path SWAT+ txtinout folder (example \"my_model\"). row_data_start numeric, row number data written. Optional, default = 3. row_col_names numeric, row nu,mber column names written. Optional, default = 2.","code":""},{"path":"/reference/read_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"dataframe information text file","code":""},{"path":"/reference/read_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"","code":"if (FALSE) { df <- read_tbl('rout_unit.con', 'model_folder', 3, 2) }"},{"path":"/reference/prepare_climate.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"Function prepare update climate data text input files SWAT+ model","code":""},{"path":"/reference/prepare_climate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"","code":"prepare_climate(meteo_lst, write_path, period_starts = NA, period_ends = NA)"},{"path":"/reference/prepare_climate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"meteo_lst meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). write_path character, path SWAT+ txtinout folder (example \"my_model\"). period_starts character, date string (example '1991-01-01'). Optional (default = NA, stands available data). period_ends character, date string (example '2020-12-31'). Optional (default = NA, stands available data).","code":""},{"path":"/reference/prepare_climate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"Fill update multiple weather related weather text files.","code":""},{"path":"/reference/prepare_climate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"","code":"if (FALSE) { prepare_climate(meteo_lst, \"output\", \"1991-01-01\", \"2020-12-31\") }"},{"path":"/reference/df_to_txt.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform dataframe into text and writes into existing file — df_to_txt","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"Transform dataframe text writes existing file","code":""},{"path":"/reference/df_to_txt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"","code":"df_to_txt(write_path, fname, df, spacing)"},{"path":"/reference/df_to_txt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"write_path character, path SWAT+ txtinout folder (example \"my_model\"). fname character, file name. df dataframe spacing character, example spacing <- c('%-30s', rep('%-13s', 2), '%-15s', '%-3s')","code":""},{"path":"/reference/df_to_txt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"updated text file","code":""},{"path":"/reference/df_to_txt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"","code":"if (FALSE) { spacing <- c(rep('%10s', 5)) df_to_txt(\"output\", \"time.sim\", df, spacing) }"},{"path":"/news/index.html","id":"svatools-010","dir":"Changelog","previous_headings":"","what":"svatools 0.1.0","title":"svatools 0.1.0","text":"new prepare_climate() function added.","code":""},{"path":"/news/index.html","id":"svatools-009","dir":"Changelog","previous_headings":"","what":"svatools 0.0.9","title":"svatools 0.0.9","text":"New functions get_usersoil_table(), load_climate_lst() plot_wgn_comparison() added.","code":""},{"path":"/news/index.html","id":"svatools-008","dir":"Changelog","previous_headings":"","what":"svatools 0.0.8","title":"svatools 0.0.8","text":"bugs fixed get_soil_parameters() get_atmo_dep().","code":""},{"path":"/news/index.html","id":"svatools-007","dir":"Changelog","previous_headings":"","what":"svatools 0.0.7","title":"svatools 0.0.7","text":"Fixed SOL_BD calculation get_soil_parameters(). Added function usersoil_to_sol().","code":""},{"path":"/news/index.html","id":"svatools-006","dir":"Changelog","previous_headings":"","what":"svatools 0.0.6","title":"svatools 0.0.6","text":"Updated functions interpolate(), prepare_wgn(), add_weather() load_template() deal different data. Added new function fill_with_closest(). Bug fixes.","code":""},{"path":"/news/index.html","id":"svatools-005","dir":"Changelog","previous_headings":"","what":"svatools 0.0.5","title":"svatools 0.0.5","text":"Different bugs various functions corrected. Plotting functions updated. Added function add_missing_pcp_zero().","code":""},{"path":"/news/index.html","id":"svatools-004","dir":"Changelog","previous_headings":"","what":"svatools 0.0.4","title":"svatools 0.0.4","text":"Land use data functions added get_lu_points() extract_rotation() well Land use article page explain use .","code":""},{"path":"/news/index.html","id":"svatools-003","dir":"Changelog","previous_headings":"","what":"svatools 0.0.3","title":"svatools 0.0.3","text":"Atmospheric deposition data functions added get_atmo_dep(), write_atmo_cli() add_atmo_dep() well Atmospheric deposition article page explain use .","code":""},{"path":"/news/index.html","id":"svatools-002","dir":"Changelog","previous_headings":"","what":"svatools 0.0.2","title":"svatools 0.0.2","text":"add_weather(), clean_wq(), clean_outliers() get_hsg() functions added.","code":""},{"path":"/news/index.html","id":"svatools-001","dir":"Changelog","previous_headings":"","what":"svatools 0.0.1","title":"svatools 0.0.1","text":"first release package svatools.","code":""}] +[{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Svajunas Plunge. Maintainer.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Plunge, Svajunas Rets, Ekaterina (2023), R package aid SWAT+ preparation R package. URL https://biopsichas.github.io/svatools/ 10.5281/zenodo.7296034.","code":"@Manual{, title = {svatools - tools for SWAT+ input data preparation, visualization and model output assessment}, author = {Svajunas Plunge and Ekaterina Rets}, year = {2023}, note = {R package}, url = {https://biopsichas.github.io/svatools/}, doi = {10.5281/zenodo.7296034}, }"},{"path":[]},{"path":"/index.html","id":"svatools","dir":"","previous_headings":"","what":"svatools","title":"SWAT+ input data preparation tool","text":"goal svatools help SWAT+ model input data preparation. mostly functions, developed implementation modeling tasks OPTAIN project. tools intended fill gaps SWAT+ workflow along side main tools developed Christoph Schuerz. Therefore, highly recommend trying using tools: SWATfarmR - R tool preparing management schedules SWAT model; SWATplusR - R tool sensitivity analyse, model calibration validation; SWATbuildR1 - R tool building SWAT+ setups; SWATdoctR2 - collection functions R routines SWAT model calibration model diagnostics. Detailed information packages, workflow steps, input data, SWAT+ parameters, model calibration, validation, etc., found SWAT+ modeling protocol.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"SWAT+ input data preparation tool","text":"can install development version svatools GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"biopsichas/svatools\") # euptf2 package is needed and used for soil parameter functions devtools::install_github(\"tkdweber/euptf2\")"},{"path":"/index.html","id":"data","dir":"","previous_headings":"","what":"Data","title":"SWAT+ input data preparation tool","text":"data required run test package installed package extdata folder. Exact location computer found running lines . Please run system get .","code":"library(svatools) temp_path <- system.file(\"extdata\", package = \"svatools\") print(temp_path) #> [1] \"C:/Users/laptop/AppData/Local/R/win-library/4.2/svatools/extdata\""},{"path":"/index.html","id":"templates","dir":"","previous_headings":"","what":"Templates","title":"SWAT+ input data preparation tool","text":"order use svatools package functions data prepare data inline templates provided extdata folder. : calibration_data.xlsx - template loading calibration (water flow water quality variables) data. weather_data.xlsx - template loading weather variables. soil_lookup.xlsx - example loading soil parameters dataset. GIS/ - folder GIS layers needed run functions. CORDEX-BC/ - folder example climate data. Data prepared according templates can directly loaded R functions applied described.","code":""},{"path":"/articles/calibration.html","id":"loading","dir":"Articles","previous_headings":"","what":"Loading","title":"Calibration data","text":"data prepared according given templates, can loaded directly package function load_template function. Two data types loaded. First station information (location, name, ID). Second, values monitoring values IDs relate station, variable names, dates values.","code":"library(svatools) temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path, 4326) ## [1] \"Loading data from template.\" ## [1] \"Loading of data is finished.\" str(cal_data) ## List of 2 ## $ stations: sf [20 × 6] (S3: sf/tbl_df/tbl/data.frame) ## ..$ ID : chr [1:20] \"5\" \"8\" \"4\" \"2\" ... ## ..$ Name : chr [1:20] \"Zgłowiączka-Strózewo-Parcele\" \"DopZStarRadziejewa - Witowo\" \"Zgłąwiączka-ponizej. Osiecin,Samszyce\" \"Zgłowiączka-pow.Osiecin. Piołunowo\" ... ## ..$ Description: chr [1:20] \"powyżej jez. Głuszyńskiego (60 km)\" \"Kolonia Witowo\" \"Samszyce - poniżej Osięcin (67,8 km)\" \"powyżej Osięcin (75,2 km)\" ... ## ..$ geometry :sfc_POINT of length 20; first list element: 'XY' num [1:2] 18.7 52.6 ## ..$ Long : num [1:20] 18.7 18.7 18.7 18.7 18.7 ... ## ..$ Lat : num [1:20] 52.6 52.6 52.6 52.6 52.6 ... ## ..- attr(*, \"sf_column\")= chr \"geometry\" ## ..- attr(*, \"agr\")= Factor w/ 3 levels \"constant\",\"aggregate\",..: NA NA NA NA NA ## .. ..- attr(*, \"names\")= chr [1:5] \"ID\" \"Name\" \"Description\" \"Long\" ... ## $ data : tibble [22,306 × 5] (S3: tbl_df/tbl/data.frame) ## ..$ Station : chr [1:22306] \"1\" \"1\" \"1\" \"1\" ... ## ..$ DATE : POSIXct[1:22306], format: \"2008-03-11\" \"2008-03-25\" ... ## ..$ Variables: chr [1:22306] \"N-NH4\" \"N-NH4\" \"N-NH4\" \"N-NH4\" ... ## ..$ Values : num [1:22306] 0.05 0.05 0.05 0.05 0.05 0.05 0.83 0.25 0.51 1.89 ... ## ..$ Source : chr [1:22306] \"grab sample\" \"grab sample\" \"grab sample\" \"grab sample\" ..."},{"path":"/articles/calibration.html","id":"cleaning","dir":"Articles","previous_headings":"","what":"Cleaning","title":"Calibration data","text":"Two functions applied data cleaning. First clean_wq function, applied fixing common water data issues fixing data formats, units (e.g. NO3, N-NO3), instead LOD/LOQ values using LOD LOQ divided 2, replacing zeros water quality variables minimum value (multiplied selected coefficient) variable. Second function clean_outliers allows removal suspicious values defined outside selected range (mean - standard deviation; mean + standard deviation).","code":"##Zeros is replaced with min(Value)/2 cal_data$data <- clean_wq(cal_data$data, 0.5) lst <- clean_outliers(cal_data$data) ##Looking at data to be removed print(head(lst$dropped)) ## # A tibble: 6 × 5 ## Station DATE Variables Values Source ## ## 1 10 2012-12-11 00:00:00 N-NH4 16.8 grab sample ## 2 10 2016-02-04 00:00:00 N-NH4 6 grab sample ## 3 10 2016-03-07 00:00:00 N-NH4 4.67 grab sample ## 4 10 2016-04-04 00:00:00 N-NH4 11.1 grab sample ## 5 10 2020-01-27 00:00:00 N-NH4 20.1 grab sample ## 6 10 2013-10-07 00:00:00 N-NO2 0.837 grab sample ##Updating data cal_data$data <- lst$newdf"},{"path":"/articles/calibration.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"Calibration data","text":"Plotting valuable tool examine data identify potential problems.","code":""},{"path":"/articles/calibration.html","id":"timeseries","dir":"Articles","previous_headings":"Plotting","what":"Timeseries","title":"Calibration data","text":"several ways package used plot loaded calibration data. Data multiple stations plotted using plot_cal_data function. way function used stations relatively data points screen data coverage potential problems. data rich monitoring station plot_cal_data function used single station selected. plotting allows better visualization.","code":"plot_cal_data(cal_data$data, c(\"3\",\"10\")) plot_cal_data(cal_data$data, c(\"4\"))"},{"path":"/articles/calibration.html","id":"monthly-summary","dir":"Articles","previous_headings":"Plotting","what":"Monthly summary","title":"Calibration data","text":"Sometimes evaluation data quality monthly plots can useful. plots allow see monitoring results corresponds data sources processes, taking place monitored catchment. plot_monthly function can used plot monthly aggregates.","code":"plot_monthly(cal_data$data, station = \"4\")"},{"path":"/articles/calibration.html","id":"fractions","dir":"Articles","previous_headings":"Plotting","what":"Fractions","title":"Calibration data","text":"Possible problems observed plotting mineral total parts nutrients changes. function . plot_fractions used nitrogen phosphorus. Function provides monthly regression monthly fraction figures. Example function use nitrogen. Example function use nitrogen.","code":"plot_fractions(cal_data$data, station = c(\"4\"), c(\"NT\"), c(\"N-NO3\", \"N-NH4\", \"N-NO2\")) ## $regression ## ## $fraction plot_fractions(cal_data$data, station = c(\"4\"), c(\"PT\"), c(\"P-PO4\")) ## $regression ## ## $fraction"},{"path":"/articles/calibration.html","id":"maps","dir":"Articles","previous_headings":"Plotting","what":"Maps","title":"Calibration data","text":"last function package plotting calibration data plot_map. function allows plot catchment boundary, monitoring stations monitoring data within station (press monitoring station). allow examination spatial temporal dimensions existing data time.","code":"library(sf) ##Loading and converting coordinate system of GIS data. EPSG 4326 coordinate system should be used to get right plot. reach_path <- system.file(\"extdata\", \"GIS/reaches.shp\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") reach <- st_transform(st_read(reach_path, quiet = TRUE), 4326) basin <-st_transform(st_read(basin_path, quiet = TRUE), 4326) plot_map(cal_data$data, cal_data$stations, reach, basin)"},{"path":"/articles/deposition.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Atmospheric deposition","text":"According Grizzetti et al. (2005) study done three catchments Europe atmospheric deposition constituted 3 16% total nitrogen inputs. Helsinki Commission (2018) report indicates around 30% nitrogen load Baltic Sea comes atmospheric deposition, contributes 1 10% nitrogen load inland surface waters sea surrounding countries. Therefore setting atmospheric deposition appropriate level help better constrain parameters responsible nitrogen transport processes catchment. SWAT+ considers nitrogen deposition (NH4 NO3), phosphorous deposition. wet dry deposition taken account. Wet deposition absorption compounds rain snow fall expressed \\(mg/l\\), whereas dry deposition direct adsorption compounds water land surfaces, expressed \\(kg/ha/year\\). SWAT+ allows constant, annual monthly input data atmospheric deposition. choice controlled atmo parameter ‘codes.bsn’ file.","code":""},{"path":"/articles/deposition.html","id":"emep-data","dir":"Articles","previous_headings":"","what":"EMEP data","title":"Atmospheric deposition","text":"Nitrogen atmospheric obtained local sources. However, local data available parameters available, deposition data can extracted European Monitoring Evaluation Programme (EMEP) provided MSC-W model data files. EMEP portal provides gridded 0.1\\(^{\\circ}\\) resolution netCDF data Europe. MSC-W model grid covers territory -30 90 longitude 30 82 latitude 50x50 km2 grid cell. parameters, needed SWAT+ model : DDEP_OXN_m2GridDry - deposition oxidized nitrogen per m2 grid (\\(mgN/m^2\\)); WDEP_OXN - wet deposition oxidized nitrogen (\\(mgN/m^2\\)); DDEP_RDN_m2Grid - dry deposition reduced nitrogen per m2 grid (\\(mgN/m^2\\)); WDEP_RDN - wet deposition reduced nitrogen (\\(mgN/m^2\\)). WDEP_PREC - precipitation (\\(mm\\)). parameters can used prepare following SWAT+ parameters required ‘atmo.cli’ input file: NH4_RF - ammonia rainfall (\\(mg/l\\)); NO3_RF - nitrate rainfall (\\(mg/l\\)); NH4_DRY - ammonia dry deposition (\\(kg/ha/yr\\)); NO3_DRY - nitrate dry deposition (\\(kg/ha/yr\\)). EMEP provides access yearly, monthly, daily hourly data. Data selected catchment required time step extracted directly EMEP website get_atmo_dep function. Function requires just path catchment boundary shape file internet connection. Depending data time step selected (yearly monthly), function need less time run (less “year” selected, “month”). information inclusion atmospheric deposition data SWAT+ modeling presented section 3.10 SWAT+ modelling protocol.","code":"library(svatools) library(tidyverse) basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") df <- get_atmo_dep(basin_path) ##Plot results ggplot(pivot_longer(df, !DATE, names_to = \"par\", values_to = \"values\"), aes(x = DATE, y = values))+ geom_line()+ facet_wrap(~par, scales = \"free_y\")+ theme_bw()"},{"path":"/articles/deposition.html","id":"swat-input","dir":"Articles","previous_headings":"","what":"SWAT+ input","title":"Atmospheric deposition","text":"Atmospheric deposition data SWAT+ model can included via ‘atmodep.cli’ file. add_atmo_dep function designed. write yearly, monthly whole modeled period averages. Function requires get_atmo_dep function output yearly monthly data. SWAT+ model setup .sqlite database updated . Thus, function updates also required tables. case average yearly atmospheric deposition data used, t_ext function parameter provided. Please read add_atmo_dep.","code":"db_path <- \"./output/test/project.sqlite\" add_atmo_dep(df, db_path)"},{"path":"/articles/land_use.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Land use","text":"Preparation land use data SWATbuildR1 among difficult tasks requires collection combination multiple GIS datasets (crop land, tile drain extent, Natural/Small Water Retention Measures (NSWRMs) installation sites, etc.). Moreover, also requires putting lot efforts cleaning topological issues prepared layer. However, even difficult task prepare land use time series data required preparation management schedules necessary SWAT+ model. page provide tools (links ) workflow help task. Particular focus paid preparing land use time series data (local data available) remote sensing sources required SWATfarmR tool.","code":""},{"path":"/articles/land_use.html","id":"land-use-layer","dir":"Articles","previous_headings":"","what":"Land use layer","title":"Land use","text":"Merging different data sets, cleaning topological errors preparing final layer directly usable SWATbuildR quite time consuming task. can done Python script prepared task. script example data available zenodo page. arcpy library required ArcGIS Advanced license. order run scrip following GIS datasets collected: General land cover covering area (raster); Catchment boundary (polygon); Crop fields (polygon); River channels (line); Tile drain extent (polygon); Measure extent (polygon)2. GIS layers adjusted fit structure example data script adjusted fit input data structure. successfully running script resulting data layer inline SWATbuildR requirements (please see SWAT+ modeling protocol sections 2.2 2.3). example data structure provided .","code":"##Loading libraries library(svatools) library(tidyverse) library(sf) ##Loading data lu_path <- system.file(\"extdata\", \"GIS/lu_layer.shp\", package = \"svatools\") lu <- st_read(lu_path, quiet = TRUE) ##Adding IDs to land use field data and selected required columns lu <- mutate(lu, id = row_number()) %>% select(id, type, drainage, measures) ##Printing structure of the data str(lu) ## Classes 'sf' and 'data.frame': 10442 obs. of 5 variables: ## $ id : int 1 2 3 4 5 6 7 8 9 10 ... ## $ type : chr \"wwht\" \"wwht\" \"corn\" \"wwht\" ... ## $ drainage: int 0 0 0 0 0 0 0 0 0 0 ... ## $ measures: chr NA NA NA NA ... ## $ geometry:sfc_POLYGON of length 10442; first list element: List of 1 ## ..$ : num [1:16, 1:2] 479720 479710 479697 479698 479669 ... ## ..- attr(*, \"class\")= chr [1:3] \"XY\" \"POLYGON\" \"sfg\" ## - attr(*, \"sf_column\")= chr \"geometry\" ## - attr(*, \"agr\")= Factor w/ 3 levels \"constant\",\"aggregate\",..: NA NA NA NA ## ..- attr(*, \"names\")= chr [1:4] \"id\" \"type\" \"drainage\" \"measures\""},{"path":"/articles/land_use.html","id":"remote-sensing","dir":"Articles","previous_headings":"","what":"Remote sensing","title":"Land use","text":"SWAT+ model important capabilities simulate impacts agriculture water flows water quality water bodies. However, requires information agricultural activities selected area. One important crop rotation data. Yet, information rarely available freely accessible. cases remote sensing data used generate information crops selected time period. information provided SWAT+ modeling protocol section 4.1.1).","code":""},{"path":"/articles/land_use.html","id":"google-earth-script","dir":"Articles","previous_headings":"Remote sensing","what":"Google Earth script","title":"Land use","text":"framework OPTAIN project deliverable 3.2 (Szabó et al. 2022), Google Earth Engine-based script (Mészáros Szabó 2022) developed predict crop types random forest method based time series reflectance data Sentinel 1A 1B satellite radar images. Output script series crop maps starting year 2015 latest available year. Required input data: Catchment boundary; Training points; Parcel/field boundaries.","code":""},{"path":"/articles/land_use.html","id":"input-data","dir":"Articles","previous_headings":"Remote sensing","what":"Input data","title":"Land use","text":"Training data necessary classification algorithm. case crop types data available LUCAS: Land Use Coverage Area frame Survey data can used. case local data year 2021. used preparation training set. However, generating training set remote sensing algorithm good practice examine input data. example , areas land use/crop type calculated provided figure. general recommendation land use/crop types small areas eliminated workflow. provided example crop types areas occupying less 0.1% assigned general code “agrl”. Thus eliminated workflow.","code":"##Calculating areas in ha and % for land uses/crops lu_area <- lu[\"type\"] %>% mutate(area = st_area(geometry)) %>% st_drop_geometry() %>% group_by(type) %>% summarise_all(sum) %>% mutate(area = as.numeric(area/10000)) lu_area[\"Sum\"] <- sum(lu_area$area) lu_area[\"Area [%]\"] <- round(100*lu_area$area/lu_area$Sum, 2) ##Printing figure ggplot(lu_area, aes(x = reorder(type, `Area [%]`), y = `Area [%]`)) + geom_bar(stat=\"identity\", mapping = aes(fill = type))+ geom_text(aes(x = type, label = `Area [%]`), hjust = -0.1, size = 3)+ labs(y = \"area in %\", x = \"crops/land use\")+ ylim(0, 25) + coord_flip()+ theme_bw()+ theme(axis.text.y = element_text(size = 8)) ##Eliminating very small types lu$type[lu$type %in% c(\"past\", \"herb\", \"csil\")] <- \"agrl\""},{"path":"/articles/land_use.html","id":"training-data","dir":"Articles","previous_headings":"Remote sensing","what":"Training data","title":"Land use","text":"Remote sensing script requires training points information year land use/crop type. data can prepared get_lu_points function. function requires preparation lookup table land use assigned numeric code. Moreover, land use type forest, water, urban land uses also participating rotation. set aside. GIS layers prepared saved .shp files transferred Google Earth Engine. files can directly used classification script.","code":"##Preparation of lookup table lookup <- data.frame(lc1 = seq(1:length(unique(c(lu$type)))), type = unique(c(lu$type))) ##Setting, which land uses will not change lu_constant <- c(\"fesc\", \"orcd\", \"frst\", \"frse\", \"frsd\", \"urld\", \"urhd\", \"wetl\", \"watr\", \"agrl\") ##Obtaining training data pts <- get_lu_points(lu, 2021, lookup, lu_constant) ##Plotting points to be used in remote sensing algorithm training ggplot(pts %>% left_join(lookup, by = \"lc1\")) + geom_sf(mapping = aes(col = type)) + scale_color_brewer(palette = \"Paired\", name = \"Crop type\")+ theme_void() ##Preparing just the boundary data for fields parcels <- lu[\"id\"] %>% st_transform(4326) ##Preparing basin data output_path <- \"../output/\" basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") catchment <- st_read(basin_path, quiet = TRUE) %>% select() %>% mutate(name = \"Upper_Zglowiaczka\") %>% st_transform(4326) ##Writing all the necessary training data for remote sensing algorithm. st_write(pts, paste0(output_path, \"pts.shp\"), append = F) st_write(parcels, paste0(output_path, \"lu_prcl.shp\"), append = F) st_write(catchment, paste0(output_path, \"catchment.shp\"), append = F)"},{"path":"/articles/land_use.html","id":"rotation-extraction","dir":"Articles","previous_headings":"Remote sensing","what":"Rotation extraction","title":"Land use","text":"Running Google Earth Engine-based script prepared Mészáros Szabó (2022) generates .tif raster file bands year land use/crop types (represented numeric codes). One year example classification results provided . .tif file extract_rotation function can applied extract crop rotation information. information delivered format directly usable SWATfarmR input data preparation scripts, applied generate SWAT+ management crop rotation schedules. example complex rotations period 2015-2022 extracted field remote sensing classification results. Results saved new shape file. Attribute table example printed . Example prepared attribute table provided . fulfills requirements set SWAT+ modeling protocol section 4.1.1. Prepared attribute table can looped, longer time series crop rotation data needed. Following lines provides example time series extended. Additionally, write Final lines write input SWATbuildR.","code":"##Loading results for accuracy lu_rot <- extract_rotation(lu, 2015, \"cropmaps2015_2022.tif\", \"../output/\", lookup, lu_constant) ##Overwriting 2021 year classification results since actual data were available. ##Also removing results were land use should be constant lu_rot <- lu_rot %>% mutate(y_2021 = ifelse(!is.na(y_2021), type, NA)) %>% select(-type) %>% mutate_at(vars(starts_with(\"y_\")), ~ifelse(lu %in% lu_constant,NA,.)) ##Writing results for SWATfarmR input preparation functions st_write(lu_rot , paste0(output_path, \"lu_rot.shp\"), append = F) # write.csv(lu_rot %>% st_drop_geometry(), \"lu_rot.csv\", row.names = F) lu_rot_df <- read.csv(system.file(\"extdata\", \"lu_rot.csv\", package = \"svatools\")) print(head(lu_rot_df, 10)) ## lu y_2015 y_2016 y_2017 y_2018 y_2019 y_2020 y_2021 y_2022 ## 1 field_1 trit trit trit trit corn corn wwht wwht ## 2 field_2 wwht wwht wwht wwht wwht wwht wwht corn ## 3 field_3 canp canp corn alfa corn alfa corn corn ## 4 field_4 trit alfa corn corn mint trit wwht corn ## 5 field_5 alfa alfa alfa alfa mint mint wwht alfa ## 6 field_6 barl alfa corn onio barl corn corn trit ## 7 field_7 sgbt canp sgbt lett lett lett corn corn ## 8 field_8 wwht barl sgbt sgbt sgbt sgbt wwht barl ## 9 field_9 barl corn barl wwht mint mint mint mint ## 10 field_10 sgbt corn mint mint barl wwht mint mint i=1 df <- lu_rot ##Loop to extend time series while(i != 5){ df <- bind_cols(df, lu_rot_df[,c(2:9)]) i = i +1 } ##Write the column names and the final file colnames(df) <- c(\"lu\", paste0(\"y_\", seq(1983,2022)), \"geometry\") ##To get 33 years exact df <- df[-c(2:8)] st_write(df, paste0(output_path, \"lu_rot_final.shp\"), append = F) lu$type <- df$lu lu[lu$drainage == 0, \"drainage\"] <- NA ##Write land use input file for SWATbuildR st_write(st_make_valid(lu), paste0(output_path, \"land.shp\"), append = F)"},{"path":"/articles/NEWS.html","id":"svatools-0-0-2","dir":"Articles","previous_headings":"","what":"svatools 0.0.2","title":"","text":"add_weather(), clean_wq(), clean_outliers() get_hsg() functions added.","code":""},{"path":"/articles/NEWS.html","id":"svatools-0-0-1","dir":"Articles","previous_headings":"","what":"svatools 0.0.1","title":"","text":"first release svatools.","code":""},{"path":"/articles/soil.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Soil parameters","text":"Obtaining soil parameters SWAT+ model can difficult. Therefore package functions allows automatic preparation soil parameters. Important theoretical documentation equations presented section 3.4 SWAT+ modelling protocol. populating SWAT+ usersoil parameter table SOL_Z (depth layer), CLAY (percentage clay defined particles <2 \\(\\mu m\\)), SAND (percentage sand - 50–2000 \\(\\mu m\\)), SILT (percentage silt - 2–50 \\(\\mu m\\)) OC (SOL_CBN, soil organic carbon content %) parameters collected per separate soil type profile’s layer. parameters filled get_usersoil_table. assigning Hydrologic Soil Groups tile drainage, water level impervious layer depth data needed. preparation soil data parameters done using following workflow. However, depending available types data, different steps might needed. example Polish case study Upper Zglowiaczka OPTAIN project.","code":""},{"path":"/articles/soil.html","id":"loading-libraries-and-paths-to-data","dir":"Articles","previous_headings":"","what":"Loading libraries and paths to data","title":"Soil parameters","text":"Workflow presented needs 6 R packages. However, svatools euptf2 libraries required population soil parameter’s table get_usersoil_table presented Adding soil parameters user table step. Input step prepared various ways. presented just one way understand better work process develop soil parameters table.","code":"##Required library(svatools) library(euptf2) ##Optional library(sf) library(readxl) library(tidyverse) library(stars) ##Required ##Path to soil distribution shape file soil_path <- system.file(\"extdata\", \"GIS/soils.shp\", package = \"svatools\") ##Path to soil lookup initial parameters with SOL_Z, CLAY, SILT, OC parameters filled. lookup_path <- system.file(\"extdata\", \"soil_lookup.xlsx\", package = \"svatools\") ##Path to tile drainage shape file drainage_path <- system.file(\"extdata\", \"GIS/drained.shp\", package = \"svatools\") ##Optional ##Path to water level depth shape file water_l_path <- system.file(\"extdata\", \"GIS/water_level.shp\", package = \"svatools\") ##Path to DEM raster file DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") ##Path to catchment boundary shape file (which in this case used as impervious layer data) basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") ##Path to soil hydro groups (if available) hsg_path <- system.file(\"extdata\", \"GIS/hydgrp.shp\", package = \"svatools\")"},{"path":"/articles/soil.html","id":"preparing-input-data","dir":"Articles","previous_headings":"","what":"Preparing input data","title":"Soil parameters","text":"Lookup table contains two sheets. One granulometry (SAND, SILT, CLAY content) soil texture class. Second humus content different humus class soils (distributed layers), easily recalculated organic carbon content (division 1.72 coefficient). required GIS input data presented maps . Polygons impervious layer one three possible categories: “<50cm”, “50-100cm” “>100cm”. information available impervious layer depth assumed depth last soil layer reclassified one three categories. Water level depth also three categories: “<60cm”, “60-100cm” “>100cm”. information available soil types land use serve good proxy information. instance wet areas wetlands probably assigned “<60cm” class, areas dominant organic soils “60-100cm”, etc. Drainage status divided two: “Y” drained areas, “N” areas without working tile drains. database available, information existence drains might available drainage installation plans.","code":"##Reading granulometry lookup information for soil types into dataframe gran <- read_excel(lookup_path, sheet = \"SandSiltClay\") ##Print example of gran table print(head(gran, 3)) ## # A tibble: 3 × 4 ## `Texture EN` SAND SILT CLAY ## ## 1 CL 32.0 42.9 25.1 ## 2 L 52.3 29.4 18.2 ## 3 LS1 87.2 10.3 2.46 ##Reading humus lookup information in to dataframe and converting it into organic carbon %. humus <- read_excel(lookup_path, sheet = \"Humus\") %>% mutate(OC1 = `Humus 1st layer [%]`/1.72, OC2 = `Humus 2nd layer [%]`/1.72, OC3 = `Humus 3rd layer [%]`/1.72) %>% select(Humus, starts_with(\"OC\")) ##Print example of humus table print(head(humus, 3)) ## # A tibble: 3 × 4 ## Humus OC1 OC2 OC3 ## ## 1 1 1.88 0.942 0.174 ## 2 2 1.63 0.814 0.151 ## 3 3 1.26 0.628 0.116 ##Reading GIS layers for basin boundary, water level, drainage status, soils (with \"Code\" column here representing soil types) and soil hydrological groups. ##All data should be in one coordinate system. ##Impervious layer is prepared from basin layer as it is below 1 m whole catchment falls into one category. impervious<- st_read(basin_path, quiet = TRUE) %>% mutate(Impervious = \">100cm\") %>% st_transform(3035) water_level <- st_read(water_l_path, quiet = TRUE) %>% st_transform(3035) drainage <- st_read(drainage_path, quiet = TRUE) %>% st_transform(3035) soils <- st_read(soil_path, quiet = TRUE) %>% st_transform(3035) hsg <- st_read(hsg_path, quiet = TRUE) %>% st_transform(3035) ##Preparing maps for each GIS data p1 <- ggplot(impervious) + geom_sf(mapping = aes(fill = Impervious)) + theme_void() p2 <- ggplot(water_level) + geom_sf(mapping = aes(fill = Depth)) + theme_void() p3 <- ggplot(drainage) + geom_sf(mapping = aes(fill = Drained)) + theme_void() p4 <- ggplot(soils) + geom_sf(mapping = aes(fill = Code), show.legend = F) + theme_void() ##Combining into one cowplot::plot_grid(p1, p2, p3, p4, ncol = 2, labels = c('Impervious depth', 'Water level', 'Drainage status', 'Soil types'), label_x = 0, label_y = 0, hjust = -0.1, vjust = -0.5, label_size = 10)"},{"path":"/articles/soil.html","id":"preparing-final-soil-layer","dir":"Articles","previous_headings":"","what":"Preparing final soil layer","title":"Soil parameters","text":"final soil layer information added GIS layers soil types. instance example initial soil type code 4 elements separated “_“. looks S3_S3_SL3_3. First three shows texture class soil granulometry three layers last number humus class. intersection GIS layers information added soil code. Thus generating new soil type, information water, impervious layer depth, drainage status well. instance new code S3_S3_SL3_3_>100cm_60-100cm_Y. first four elements stay last three added depth impervious layers, depth water level drainage status classes. important point intersection GIS layers example increased number unique soil types 171 542 new soil type distribution GIS data created.","code":"##Printing example of soil codes before intersection print(head(soils %>% st_drop_geometry(), 3)) ## Code ## 1 S3_S3_SL3_3 ## 2 SL2_S3_SL3_3 ## 3 SL3_SL3_SL3_1 ##Counting number of unique soil types in input soil data soils_n1 <- dim(soils %>% st_drop_geometry() %>% select(Code) %>% unique)[1] soils <- soils %>% st_intersection(impervious) %>% st_intersection(water_level) %>% st_intersection(drainage) %>% mutate(SNAM = paste0(Code, \"_\", Impervious, \"_\", Depth, \"_\", Drained)) %>% select(SNAM) ##Counting number of unique soil types in generated soil data soils_n2 <- dim(soils %>% st_drop_geometry() %>% select(SNAM) %>% unique)[1] ##Example of new soil codes print(head(soils %>% st_drop_geometry(), 3)) ## SNAM ## 3 SL3_SL3_SL3_1_>100cm_>100cm_N ## 5 SL4_SL4_SL4_3_>100cm_>100cm_N ## 6 SL3_SL3_SL3_2_>100cm_>100cm_N ##Praparing figure of new soil data p5 <- ggplot(soils) + geom_sf(mapping = aes(fill = SNAM), show.legend = F) + theme_void() cowplot::plot_grid(p4, p5, ncol = 2, labels = c('Original soil map', 'New soil map'), label_x = 0, label_y = 0, hjust = -0.1, vjust = -0.5, label_size = 10)"},{"path":"/articles/soil.html","id":"preparing-initial-user-soil-table","dir":"Articles","previous_headings":"","what":"Preparing initial user soil table","title":"Soil parameters","text":"part prepares input following parameter generation part, actually uses svatools package functions populate usersoil table. Workflow provided done form input usertable, used get_usersoil_table function. However, information prepared means R, might convenient user loaded R environment. requirements table parameters filled: SNAM soil type text last three parts separated **_** representing impervious layer, water level drainage classes; NLAYERS numeric value soil type representing number soil layers; SOL_Z numeric value soil layer represent max depth soil layer; SAND numeric sand content %; SILT numeric silt content %; CLAY numeric clay content %; SOL_CBN numeric organic carbon content %. example demonstrates data lookup tables used populate initial usertable look generating parameters. case soil types three layers depth. information added mutate function. according textural classes lookup information added table left_join functions.","code":"usertable <- soils %>% st_drop_geometry() %>% distinct() %>% separate(SNAM, c(\"Lyr1\", \"Lyr2\", \"Lyr3\", \"Humus\", \"Impervious\", \"Depth\", \"Drained\"), \"_\", remove = FALSE) %>% mutate(NLAYERS = 3, SOL_Z1 = 250, SOL_Z2 = 750, SOL_Z3 = 1500, Humus = as.numeric(Humus)) %>% left_join(gran %>% rename_with( ~ paste0(.x, \"1\")), by = c(\"Lyr1\" = \"Texture EN1\")) %>% left_join(gran %>% rename_with( ~ paste0(.x, \"2\")), by = c(\"Lyr2\" = \"Texture EN2\")) %>% left_join(gran %>% rename_with( ~ paste0(.x, \"3\")), by = c(\"Lyr3\" = \"Texture EN3\")) %>% left_join(humus, by = c(\"Humus\")) %>% mutate(Impervious = str_split(SNAM, \"_\")[[1]][5], Depth = str_split(SNAM, \"_\")[[1]][6], Drained = str_split(SNAM, \"_\")[[1]][7]) %>% select(SNAM, NLAYERS, Impervious, Depth, Drained, ends_with(\"1\"), ends_with(\"2\"), ends_with(\"3\"), -starts_with(\"Lyr\")) ##Renaming OC column to SOL_CBN sol_oc <- names(usertable)[grep(\"OC>*\", names(usertable))] usertable <- rename_at(usertable, vars(all_of(sol_oc)), ~sub(\"OC\", \"SOL_CBN\", sol_oc)) ##Saving and printing structure example of the required table write.csv(usertable, \"../output/usersoils.csv\", row.names=FALSE, quote=FALSE) str(usertable) ## 'data.frame': 542 obs. of 20 variables: ## $ SNAM : chr \"SL3_SL3_SL3_1_>100cm_>100cm_N\" \"SL4_SL4_SL4_3_>100cm_>100cm_N\" \"SL3_SL3_SL3_2_>100cm_>100cm_N\" \"SL2_SL4_SL4_2_>100cm_>100cm_N\" ... ## $ NLAYERS : num 3 3 3 3 3 3 3 3 3 3 ... ## $ Impervious: chr \">100cm\" \">100cm\" \">100cm\" \">100cm\" ... ## $ Depth : chr \">100cm\" \">100cm\" \">100cm\" \">100cm\" ... ## $ Drained : chr \"N\" \"N\" \"N\" \"N\" ... ## $ SOL_Z1 : num 250 250 250 250 250 250 250 250 250 250 ... ## $ SAND1 : num 62.5 60.5 62.5 71.7 80.7 ... ## $ SILT1 : num 25.4 27.8 25.4 22.1 14.8 ... ## $ CLAY1 : num 12.07 11.69 12.07 6.11 4.45 ... ## $ SOL_CBN1 : num 1.88 1.26 1.63 1.63 1.63 ... ## $ SOL_Z2 : num 750 750 750 750 750 750 750 750 750 750 ... ## $ SAND2 : num 62.5 60.5 62.5 60.5 80.7 ... ## $ SILT2 : num 25.4 27.8 25.4 27.8 14.8 ... ## $ CLAY2 : num 12.07 11.69 12.07 11.69 4.45 ... ## $ SOL_CBN2 : num 0.942 0.628 0.814 0.814 0.814 ... ## $ SOL_Z3 : num 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 ... ## $ SAND3 : num 62.5 60.5 62.5 60.5 62.5 ... ## $ SILT3 : num 25.4 27.8 25.4 27.8 25.4 ... ## $ CLAY3 : num 12.1 11.7 12.1 11.7 12.1 ... ## $ SOL_CBN3 : num 0.174 0.116 0.151 0.151 0.151 ..."},{"path":"/articles/soil.html","id":"par_make","dir":"Articles","previous_headings":"","what":"Adding soil parameters to user table","title":"Soil parameters","text":"section required part essence workflow. get_usersoil_table function filling required SWAT+ parameters. input data formatted correctly, function straightforward. requires prepared csv file.","code":"usertable <- get_usersoil_table(\"../output/usersoils.csv\", hsg = TRUE, keep_values = FALSE, nb_lyr = 10) ## [1] \"Soil hydrological groups were calculated.\" ## [1] \"Adding additional 7 layer(s) added.\""},{"path":"/articles/soil.html","id":"overwritting-with-measured-parameters","dir":"Articles","previous_headings":"","what":"Overwritting with measured parameters","title":"Soil parameters","text":"case soil types required soil parameters get_usersoil_table function results can overwritten. Example provides example data code used replacing parameters generated user table. However, achieved keep_values = TRUE get_usersoil_table function, data provided ‘usersoils.csv’ file used input function. case parameters available GIS data (.e. field measurements local databases), can used overwriting generated parameters usertable. example map soil hydrological groups. used overwrite results get_soil_parameters function.","code":"##Example data loaded to be used for overwriting parameters in the user table rep_pars_path <- system.file(\"extdata\", \"soil_parameters.xlsx\", package = \"svatools\") replace_pars <- read_excel(rep_pars_path) ##Extracting soil type codes for each soil layer usertable <- separate(usertable, SNAM, c(\"Lyr1\", \"Lyr2\", \"Lyr3\", \"LyrX\"), \"_\", extra = \"merge\", remove = FALSE) ##Replacing values for selected soil types for(p in c(\"Emt\", \"Mpl\", \"Tn\")){ ##Selected soil layers for (l in seq(1, 3)){ ##Selected parameters mp <- paste0(c(\"SOL_Z\", \"SOL_BD\", \"SOL_AWC\", \"SOL_K\", \"SOL_CBN\", \"CLAY\", \"SILT\", \"SAND\", \"ROCK\", \"SOL_ALB\", \"USLE_K\", \"SOL_EC\"),l) usertable[usertable[paste0(\"Lyr\",l)] == p, mp] <- replace_pars[replace_pars[\"SNAM\"] == p, mp] } } ##Removing columns with soil types for each layer usertable <- select(usertable, -starts_with(\"Lyr\")) ##Joining GIS data to generated usertable soilp <- soils %>% left_join(usertable, by = \"SNAM\") ##Plotting soil hydrologic groups obtained from get_usersoil_table function plot(soilp[\"HYDGRP\"], main=\"HYDGRP obtained with get_usersoil_table function\") ##Plotting soil hydrologic groups obtained local hydgrp map plot(hsg[\"hsg\"], main=\"HYDGRP from field data\") ##Overwriting generated data with values from field data source soilp <- st_join(soilp, hsg, join = st_overlaps, left = TRUE, largest = TRUE) %>% mutate(HYDGRP = hsg) %>% select(-hsg) ##Plotting updated data result plot(soilp[\"HYDGRP\"])"},{"path":"/articles/soil.html","id":"checking-results-on-a-map","dir":"Articles","previous_headings":"","what":"Checking results on a map","title":"Soil parameters","text":"generation required soil parameters checking results map good practice, help spot various issues validate results. just examples.","code":"##Plot soil sol_k for the top layer plot(soilp[\"SOL_K1\"]) ##Plot soil sol_k for the top layer plot(soilp[\"USLE_K1\"]) ##Plot soil sol_awc for the top layer plot(soilp[\"SOL_AWC1\"])"},{"path":"/articles/soil.html","id":"write-inputs-for-swat","dir":"Articles","previous_headings":"","what":"Write inputs for SWAT+","title":"Soil parameters","text":"last step write SWAT+ input data: raster layer (case named ‘SoilmapSWAT.tif’), linkage table raster codes soil type codes (‘Soil_SWAT_cod.csv’) usersoil table (‘usersoil_lrew.csv’). DEM data step used help rastarize soil types distribution grid, used setup preparation algorithms. write_stars function stars package used write raster file write.csv R base function write .csv files.","code":"##Reading catchment DEM raster data dem <- read_stars(DEM_path) plot(dem) ##Merging unique soil types into one row and multipolygon soils_dis <- soils %>% group_by(SNAM) %>% dplyr::summarise() %>% mutate(SOIL_ID = rownames(.), SNAME = SNAM) ##Rasterizing soil type data soils_raster <- soils_dis %>% st_rasterize(dem) plot(soils_raster) ##Preparing linkage table soil_link_table <- soils_dis %>% st_drop_geometry() %>% select(SOIL_ID, SNAME) print(head(soil_link_table, 3)) ## # A tibble: 3 × 2 ## SOIL_ID SNAME ## ## 1 1 Emt_Emt_Emt_1_>100cm_<60cm_N ## 2 2 Emt_Emt_Emt_1_>100cm_<60cm_Y ## 3 3 Emt_Emt_Emt_1_>100cm_>100cm_N ##Writing files write_stars(soils_raster , \"../output/SoilmapSWAT.tif\") write.csv(soil_link_table, \"../output/Soil_SWAT_cod.csv\", row.names=FALSE, quote=FALSE) write.csv(usertable, \"../output/usersoil_lrew.csv\", row.names=FALSE, quote=FALSE)"},{"path":"/articles/weather.html","id":"load_weather","dir":"Articles","previous_headings":"","what":"Loading","title":"Weather data","text":"Loading weather data templates done load_template function loading calibration data. However, data meteorological station separated different sheets. data loaded nested list list format, used run functions.","code":"library(svatools) temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) ## [1] \"Loading data from template.\" ## [1] \"Reading station ID9 data.\" ## [1] \"Reading station ID3 data.\" ## [1] \"Reading station ID10 data.\" ## [1] \"Reading station ID11 data.\" ## [1] \"Reading station ID12 data.\" ## [1] \"Reading station ID13 data.\" ## [1] \"Reading station ID1 data.\" ## [1] \"Reading station ID8 data.\" ## [1] \"Reading station ID2 data.\" ## [1] \"Reading station ID4 data.\" ## [1] \"Reading station ID5 data.\" ## [1] \"Reading station ID6 data.\" ## [1] \"Reading station ID7 data.\" ## [1] \"Reading station ID14 data.\" ## [1] \"Loading of data is finished.\""},{"path":"/articles/weather.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"Weather data","text":"Function plot_weather allows plot meteorological data selecting variables, time period way aggregating . Time period selected one “second”,“minute”,“hour”,“day”, “week”, “month”, “year”. aggregation functions selected dplyr package summarise function available options. provided meteorological stations` data displayed. Relevant stations selected interactively generated plot selecting deselecting stations legend. show hide buttons allows select deselect stations. Press Shift + left mouse bottom drag zoom specific area, press left mouse button grad explore plot, press Shift + left right mouse buttons time zoom . Toolbox top right corner figure used operations well. Another function plot_weather_compare allows comparing two meteorological datasets time scale. Potential examples usage different. One comparing different datasets order decide, one used modeling. Another example might examine cleaning operations improved original data. Moreover, function helpful examining climate data downscaling bias-correction results comparing observed data. options aggregation plot_weather_compare function can used.","code":"plot_weather(met_lst, \"PCP\", \"month\", \"sum\") temp_path2 <- system.file(\"extdata\", \"weather_data_raw.xlsx\", package = \"svatools\") met_lst2 <- load_template(temp_path2, 3035) ## [1] \"Loading data from template.\" ## [1] \"Reading station ID9 data.\" ## [1] \"Reading station ID3 data.\" ## [1] \"Reading station ID10 data.\" ## [1] \"Reading station ID11 data.\" ## [1] \"Reading station ID12 data.\" ## [1] \"Reading station ID13 data.\" ## [1] \"Reading station ID1 data.\" ## [1] \"Reading station ID8 data.\" ## [1] \"Reading station ID2 data.\" ## [1] \"Reading station ID4 data.\" ## [1] \"Reading station ID5 data.\" ## [1] \"Reading station ID6 data.\" ## [1] \"Reading station ID7 data.\" ## [1] \"Loading of data is finished.\" plot_weather_compare(met_lst, met_lst2, \"PCP\", \"month\", \"mean\", \"clean\", \"raw\")"},{"path":"/articles/weather.html","id":"interpolating","dir":"Articles","previous_headings":"","what":"Interpolating","title":"Weather data","text":"cases stations available selected catchment huge data gaps exists, interpolation applied stations cover gaps. Example loads basin boundary used modeling adds already loaded meteorological stations map. seen basin just one station inside . However, case station observations huge gaps. Therefore, highly recommended cases collect data nearby stations. data can help fill gaps also used generate spatial distribution variables, employed propagate time series data number virtual stations. Therefore, number meteorological stations prepared full time series data. need function interpolate prepared. allows interpolation data presented stations. Function’s grid_spacing parameter defines, dense spatial interpolation points . Interpolate function creates virtual stations. Data needed function loaded stations observations, basin boundary shape path, DEM raster path. important, GIS data used function coordinate system. Function parameters grid_spacing, p_vector - character vector representing weather variables interpolate (optional, default variables selected c(“PCP”, “SLR”, “RELHUM”, “WNDSPD”, “TMP_MAX”, “TMP_MIN” ), idw_exponent - numeric value exponent parameter used interpolation (optional, default value 2). running interpolate function transform_to_list function used transform interpolation results list list format, used functions (loading weather data template). functions can applied. Transformation function requires dates beginning end time series. info can extracted loaded weather data (used interpolation) internal function svatools:::get_dates.","code":"library(mapview) library(sf) library(dplyr) basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") basin <-st_transform(st_read(basin_path), 4326) %>% mutate(NAME = \"Basin\") ## Reading layer `basin' from data source ## `C:\\Users\\laptop\\AppData\\Local\\R\\win-library\\4.2\\svatools\\extdata\\GIS\\basin.shp' ## using driver `ESRI Shapefile' ## Simple feature collection with 1 feature and 1 field ## Geometry type: POLYGON ## Dimension: XY ## Bounding box: xmin: 4898668 ymin: 3304508 xmax: 4915266 ymax: 3319644 ## Projected CRS: ETRS89-extended / LAEA Europe stations <- st_transform(met_lst$stations, 4326) mapview(stations) + mapview(basin) DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") result <- svatools::interpolate(met_lst, basin_path, DEM_path, 2000) ##To transform interpolation results to list of lists, which could be used with other weather functions. start_date <- svatools:::get_dates(met_lst)$min_date end_date <- svatools:::get_dates(met_lst)$max_date met_lst_int <- transform_to_list(result, start_date, end_date)"},{"path":"/articles/weather.html","id":"wgn-input-function","dir":"Articles","previous_headings":"Interpolating","what":"WGN input function","title":"Weather data","text":"Weather generator inputs also can directly prepared prepare_wgn function package.","code":"wgn <- prepare_wgn(met_lst, TMP_MAX = met_lst$data$ID10$TMP_MAX, TMP_MIN = met_lst$data$ID10$TMP_MIN, PCP = met_lst$data$ID9$PCP, RELHUM = met_lst$data$ID9$RELHUM, WNDSPD = met_lst$data$ID10$WNDSPD, MAXHHR = met_lst$data$ID11$MAXHHR, SLR = met_lst$data$ID9$SLR) write.csv(wgn$wgn_st, \"wgn_st.csv\", row.names = FALSE, quote = FALSE) write.csv(wgn$wgn_data, \"wgn_data.csv\", row.names = FALSE, quote = FALSE)"},{"path":[]},{"path":"/articles/weather.html","id":"swat-input-and-updating--sqlite-database","dir":"Articles","previous_headings":"Writing","what":"SWAT+ input and updating .sqlite database","title":"Weather data","text":"add_weather function can used write weather files project’s folder update tables project’s sqlite database weather, weather generator relation weather station information.","code":"##Path to .sqlite db_path <- \"./output/test/project.sqlite\" add_weather(db_path, met_lst, wgn)"},{"path":"/articles/climate.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Climate data","text":"Climate data OPTAIN project case study site obtained UFZ-Cloud. located WPs&Tasks>WP3>Task_3_2>data path. Numbers referring case study number gridded.zip files contains NetCDF files climate data case study basin. Data provided precipitation, solar radiation, relative humidity, min, max, mean temperature wind speed variables. Time period covered starting 1981-01-01 ending 2099 2100. Three representative concentration pathways (RCPs) available (RCP2.6, RCP4.5, RCP8.5) six regional climate models (RCMs). Detained information RCMs provided table . RCMs data bias-corrected ERA5-Land climate data. Description data preparation analysis provided project deliverable D3.1: Climate scenarios integrated modelling.","code":""},{"path":"/articles/climate.html","id":"netcdf-to-swat-input","dir":"Articles","previous_headings":"","what":"NetCDF to SWAT+ input","title":"Climate data","text":"direct way using NetCDF data SWAT model. Therefore tools prepared users provided included package.","code":""},{"path":"/articles/climate.html","id":"extracting-from-netcdf","dir":"Articles","previous_headings":"NetCDF to SWAT+ input","what":"Extracting from NetCDF","title":"Climate data","text":"First step prepare SWAT+ climate input data download XX-gridded.zip file case study site unzip . locate CORDEX-BC folder. Data extraction NetCDF done load_climate_lst function, needs path basin shape file path CORDEX-BC folder. Following lines provides example use . function creates virtual stations NetCDF grid cell, basin boundary touches. Virtual stations coordinates defined center grid cell. virtual station data extracted saved list list format.","code":"library(svatools) basin_path <- \"GIS/basin.shp\" cordex_path <- \"climate/CORDEX-BC\" climate_lst <- load_climate_lst(cordex_path, basin_path)"},{"path":"/articles/climate.html","id":"evaluating-climate-data","dir":"Articles","previous_headings":"NetCDF to SWAT+ input","what":"Evaluating climate data","title":"Climate data","text":"Extraction result arranged specific format. RCP -> RCM top levels list format. format meteorological data used package. stations sf Data frame (ID, Name, Elevation, Source, geometry, Long, Lat) data -> Station ID -> Variable -> Data frame (DATE, Variable) Therefore package function plot_weather used examine function results compare measurement data, instance plot_weather_compare. plot_wgn_comparison function might useful get climate set statistical comparison motoring data. functions Weather data section also applicable. allows update SWAT+ .sqlite database climate data, prepare weather generator parameters, etc.","code":"plot_weather(climate_lst[[\"rcp85\"]][[\"1\"]],\"TMP_MAX\", \"year\", \"max\") ##Loading data from the template with load_template function temp_path <- \"my_folder/weather_data.xlsx\" met_lst <- load_template(temp_path, 4326) ##And plotting comparison of statistical values with climate data plot_wgn_comparison(climate_lst[[\"rcp85\"]][[\"1\"]], met_lst, \"ID4\", \"ID2\", \"Climate set\", \"Monitoring data\", \"comparison\")"},{"path":"/articles/climate.html","id":"preparing-swat-input-climate-sets","dir":"Articles","previous_headings":"NetCDF to SWAT+ input","what":"Preparing SWAT+ input climate sets","title":"Climate data","text":"Nevertheless prepare_climate function used directly prepare/update model input text files needed run climate scenario. Nothing else needed just replace model input files prepared set files. Following lines runs prepare_climate function several loops prepares climate data input sets folder structure.","code":"##Path to your SWAT+ model project directory project_dir <- \"../output/overwrite/tmp\" ##These files will be copied from model directory and updated with prepare_climate function files_to_copy <- paste(project_dir, c(\"aquifer.con\", \"chandeg.con\", \"hru.con\", \"reservoir.con\", \"rout_unit.con\", \"time.sim\"), sep=\"/\") ##Main loop for each rcp for (rcp in c(\"rcp26\", \"rcp45\", \"rcp85\")){ ##Each rcm for (rcm in as.character(c(1:6))){ ##Each period for(p in list(c(\"H\", \"1988-01-01\", \"2020-12-31\"), c(\"N\", \"2032-01-01\", \"2065-12-31\"), c(\"E\", \"2067-01-01\", \"2099-12-31\"))){ ##Creating new directory cli_dir <- paste(\"../output/cli\", rcp, paste0(\"rcm\", rcm), p[1], sep = \"/\") dir.create(cli_dir, recursive = TRUE) ##Coping files to update file.copy(files_to_copy, paste(cli_dir, c(\"aquifer.con\", \"chandeg.con\", \"hru.con\", \"reservoir.con\", \"rout_unit.con\", \"time.sim\"), sep = \"/\"), overwrite = TRUE) ##Running function to prepare climate input for scenario prepare_climate(climate_lst[[rcp]][[rcm]], cli_dir, p[2], p[3]) print(paste(rcp, \"rcm\", rcm, p[1], p[2], \"-\", p[3], \"set prepared.\")) } } }"},{"path":"/reference/add_atmo_dep.html","id":null,"dir":"Reference","previous_headings":"","what":"Update sqlite database with atmospheric deposition data — add_atmo_dep","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"Update sqlite database atmospheric deposition data","code":""},{"path":"/reference/add_atmo_dep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"","code":"add_atmo_dep(df, db_path, t_ext = \"year\")"},{"path":"/reference/add_atmo_dep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"df dafaframe \"DATE\", \"NH4_RF\", \"NO3_RF\" , \"NH4_DRY\" \"NO3_DRY\" columns obtained get_atmo_dep) function. db_path character sqlite database (example \"./output/project.sqlite\") t_ext string, 'year' yearly averages, 'month' - monthly averages 'annual' average period. Optional (default - \"year\").","code":""},{"path":"/reference/add_atmo_dep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"write data 'atmodep.cli' file updated sqlite database codes connection atmospheric deposition data","code":""},{"path":"/reference/add_atmo_dep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update sqlite database with atmospheric deposition data — add_atmo_dep","text":"","code":"if (FALSE) { basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") df <- get_atmo_dep(basin_path) db_path <- \"./output/test/project.sqlite\" add_atmo_dep(df, db_path) }"},{"path":"/reference/add_weather.html","id":null,"dir":"Reference","previous_headings":"","what":"Update sqlite database with weather data — add_weather","title":"Update sqlite database with weather data — add_weather","text":"Update sqlite database weather data","code":""},{"path":"/reference/add_weather.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update sqlite database with weather data — add_weather","text":"","code":"add_weather(db_path, meteo_lst, wgn_lst, fill_missing = TRUE)"},{"path":"/reference/add_weather.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update sqlite database with weather data — add_weather","text":"db_path character sqlite database (example \"./output/project.sqlite\") meteo_lst meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). wgn_lst list two dataframes: wgn_st - wgn station data, wgn_data - wgn data (prepared prepare_wgn() function). fill_missing Boolean, TRUE - fill data missing stations data closest stations available data. FALSE - leave stations without data. Weather generator used fill missing variables model. Optional (Default = TRUE).","code":""},{"path":"/reference/add_weather.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update sqlite database with weather data — add_weather","text":"updated sqlite database weather data","code":""},{"path":"/reference/add_weather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update sqlite database with weather data — add_weather","text":"","code":"if (FALSE) { ##Getting meteo data from template met_lst <- load_template(temp_path, 3035) ##Calculating wgn parameters wgn <- prepare_wgn(met_lst, MAXHHR = met_lst$data$ID11$MAXHHR) ##Writing weather input into model database db_path <- \"./output/test/project.sqlite\" add_weather(db_path, met_lst, wgn) }"},{"path":"/reference/clean_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to clean outliers outside by some standard deviations — clean_outliers","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"Function clean outliers outside standard deviations","code":""},{"path":"/reference/clean_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"","code":"clean_outliers(df, times_sd = 3)"},{"path":"/reference/clean_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"df dataframe columns c(\"Station\", \"DATE\", \"Variables\", \"Values\", \"Source\"). times_sd numeric value representing multiplication factor standard deviation. Values outside mean - sd X times_sd, mean + sd X times_sd identified outliers. Optional default value 3.","code":""},{"path":"/reference/clean_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"list two dataframes. newdf dataframe contains dataframe cleaned outliers. dropped dataframe contains data, removed newdf dataframe.","code":""},{"path":"/reference/clean_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to clean outliers outside by some standard deviations — clean_outliers","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" lst <- clean_outliers(cal_data$data) ##Looking at data to be removed print(head(lst$dropped)) #> # A tibble: 6 × 5 #> Station DATE Variables Values Source #> #> 1 10 2012-12-11 00:00:00 N-NH4 16.8 grab sample #> 2 10 2016-02-04 00:00:00 N-NH4 6 grab sample #> 3 10 2016-03-07 00:00:00 N-NH4 4.67 grab sample #> 4 10 2016-04-04 00:00:00 N-NH4 11.1 grab sample #> 5 10 2020-01-27 00:00:00 N-NH4 20.1 grab sample #> 6 10 2013-10-07 00:00:00 N-NO2 0.837 grab sample ##Updating data cal_data$data <- lst$newdf"},{"path":"/reference/clean_wq.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean water quality data from most typical issues — clean_wq","title":"Clean water quality data from most typical issues — clean_wq","text":"Clean water quality data typical issues","code":""},{"path":"/reference/clean_wq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean water quality data from most typical issues — clean_wq","text":"","code":"clean_wq(df, zero_to_min = 1)"},{"path":"/reference/clean_wq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clean water quality data from most typical issues — clean_wq","text":"df dataframe water quality data columns c(\"Station\", \"DATE\", \"Variables\", \"Values\", \"Source\"). zero_to_min numeric coefficient zeros min variable value X zero_to_min. Optional, default 1.","code":""},{"path":"/reference/clean_wq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clean water quality data from most typical issues — clean_wq","text":"cleaned dataframe","code":""},{"path":"/reference/clean_wq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clean water quality data from most typical issues — clean_wq","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" cal_data$data <- clean_wq(cal_data$data)"},{"path":"/reference/df_t.html","id":null,"dir":"Reference","previous_headings":"","what":"Transforming sp dataframe to dataframe — df_t","title":"Transforming sp dataframe to dataframe — df_t","text":"Transforming sp dataframe dataframe","code":""},{"path":"/reference/df_t.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transforming sp dataframe to dataframe — df_t","text":"","code":"df_t(sp_df)"},{"path":"/reference/df_t.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transforming sp dataframe to dataframe — df_t","text":"sp_df sp dataframe","code":""},{"path":"/reference/df_t.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transforming sp dataframe to dataframe — df_t","text":"Dataframe prepared written .","code":""},{"path":"/reference/df_t.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transforming sp dataframe to dataframe — df_t","text":"","code":"if (FALSE) { df_t(sp_df) }"},{"path":"/reference/extract_rotation.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract rotation information from raster file — extract_rotation","title":"Extract rotation information from raster file — extract_rotation","text":"Extract rotation information raster file","code":""},{"path":"/reference/extract_rotation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract rotation information from raster file — extract_rotation","text":"","code":"extract_rotation(df, start_year, tif_name, r_path, lookup, lu_constant = c())"},{"path":"/reference/extract_rotation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract rotation information from raster file — extract_rotation","text":"df sf data.frame land use. \"id\" \"type\" columns present. start_year numeric, representing year data begins. tif_name string name .tif raster file. r_path string path .tif file. lookup dataframe \"lc1\" column numeric codes \"type\" column text. lu_constant vector strings land uses kept constant land use (.e. water, urban areas, etc.)","code":""},{"path":"/reference/extract_rotation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract rotation information from raster file — extract_rotation","text":"sf data.frame land use amended crop rotation information","code":""},{"path":"/reference/extract_rotation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract rotation information from raster file — extract_rotation","text":"","code":"if (FALSE) { library(sf) ##Loading land use/crop layer lu_path <- system.file(\"extdata\", \"GIS/lu_layer.shp\", package = \"svatools\") lu <- st_read(lu_path, quiet = TRUE) %>% mutate(id = row_number()) ##Preparing lookup table lookup <- data.frame(lc1 = seq(1:length(unique(c(lu$type)))), type = unique(c(lu$type))) lu_constant <- c(\"fesc\", \"orch\", \"frst\", \"frse\", \"frsd\", \"urld\", \"urhd\", \"wetl\", \"past\", \"watr\", \"agrl\") ##Extracting rotation information from raster ##Raster information should have been prepared with remote sensing lu_rot <- extract_rotation(lu, 2015, \"cropmaps.tif\", \"./output/\", lookup, lu_constant) }"},{"path":"/reference/get_atmo_dep.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"Extract EMEP atmospheric deposition data catchment","code":""},{"path":"/reference/get_atmo_dep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"","code":"get_atmo_dep( catchment_boundary_path, t_ext = \"year\", start_year = 1990, end_year = 2020 )"},{"path":"/reference/get_atmo_dep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"catchment_boundary_path path basin boundary shape file. t_ext string, EMEP data access 'year' yearly averages, 'month' - monthly averages. Optional (default - 'year'). start_year integer year start data extraction. Optional (default - 1990). end_year integer year end data extraction. Optional (default - 2020).","code":""},{"path":"/reference/get_atmo_dep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"dafaframe \"DATE\", \"NH4_RF\", \"NO3_RF\" , \"NH4_DRY\" \"NO3_DRY\" columns. Values SWAT+ units. \"NH4_RF\" - ammonia rainfall (mg/l), \"NO3_RF\" - nitrate rainfall (mg/l), NH4_RF - ammonia deposition (kg/ha/yr), \"NO3_DRY\" - nitrate dry deposition (kg/ha/yr)","code":""},{"path":"/reference/get_atmo_dep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract EMEP atmospheric deposition data for a catchment — get_atmo_dep","text":"","code":"if (FALSE) { basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") df <- get_atmo_dep(basin_path) ##Plot results ggplot(pivot_longer(df, !DATE, names_to = \"par\", values_to = \"values\"), aes(x = DATE, y = values))+ geom_line()+ facet_wrap(~par, scales = \"free_y\")+ theme_bw() }"},{"path":"/reference/get_data_to_interpolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to get data to be used in the interpolation — get_data_to_interpolate","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"Function get data used interpolation","code":""},{"path":"/reference/get_data_to_interpolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"","code":"get_data_to_interpolate(meteo_lst, par)"},{"path":"/reference/get_data_to_interpolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par weather variable extract (.e. \"PCP\", \"SLR\", etc)","code":""},{"path":"/reference/get_data_to_interpolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"SpatialPointsDataFrame columns days rows stations.","code":""},{"path":"/reference/get_data_to_interpolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to get data to be used in the interpolation — get_data_to_interpolate","text":"","code":"##get_data_to_interpolate (meteo_lst, \"PCP\")"},{"path":"/reference/get_dates.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"Function get maximum min max dates available time series data stations parameters.","code":""},{"path":"/reference/get_dates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"","code":"get_dates(meteo_lst)"},{"path":"/reference/get_dates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER).","code":""},{"path":"/reference/get_dates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"list 2 values: minimum maximum value available data.","code":""},{"path":"/reference/get_dates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to get maximum min and max dates available for all time series data for all stations for all parameters. — get_dates","text":"","code":"if (FALSE) { get_dates(meteo_lst) }"},{"path":"/reference/get_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare grid for interpolation — get_grid","title":"Prepare grid for interpolation — get_grid","text":"Prepare grid interpolation","code":""},{"path":"/reference/get_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare grid for interpolation — get_grid","text":"","code":"get_grid(b, grid_spacing, wkt_str)"},{"path":"/reference/get_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare grid for interpolation — get_grid","text":"b vector border c(xmin, ymin, xmax, ymax) coordinates define extent grid. grid_spacing Grid spacing. Depending coordinate system meters, degrees. wkt_str projection string","code":""},{"path":"/reference/get_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare grid for interpolation — get_grid","text":"Grid needed interpolation.","code":""},{"path":"/reference/get_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare grid for interpolation — get_grid","text":"","code":"if (FALSE) { get_grid(shp, 2000, wkt_str) }"},{"path":"/reference/get_hsg.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to get soil hydrologic groups — get_hsg","title":"Function to get soil hydrologic groups — get_hsg","text":"Function get soil hydrologic groups","code":""},{"path":"/reference/get_hsg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to get soil hydrologic groups — get_hsg","text":"","code":"get_hsg(d_imp, d_wtr, drn, t)"},{"path":"/reference/get_hsg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to get soil hydrologic groups — get_hsg","text":"d_imp character depth impervious layer. three entry options possible: \"<50cm\", \"50-100cm\", \">100cm\". d_wtr character water table high. three entry options possible: \"<60cm\", \"60-100cm\", \">100cm\". drn character tile drainage status. two entry options possible: \"Y\" drained areas, \"N\" areas without working tile drains. t one row dataframe SOL_Z SOL_K values soil type.","code":""},{"path":"/reference/get_hsg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to get soil hydrologic groups — get_hsg","text":"character soil hydrologic group , B, C D","code":""},{"path":"/reference/get_hsg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to get soil hydrologic groups — get_hsg","text":"","code":"df <- data.frame(SOL_K1 = 10, SOL_K2 = 1, SOL_K3 = 2, SOL_Z1 = 250, SOL_Z2 = 700, SOL_Z3 = 1000) get_hsg(d_imp = \">100cm\", d_wtr = \"<60cm\", drn = \"Y\", df) #> Error in get_hsg(d_imp = \">100cm\", d_wtr = \"<60cm\", drn = \"Y\", df): could not find function \"get_hsg\""},{"path":"/reference/get_interpolated_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Main interpolation function — get_interpolated_data","title":"Main interpolation function — get_interpolated_data","text":"Main interpolation function","code":""},{"path":"/reference/get_interpolated_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Main interpolation function — get_interpolated_data","text":"","code":"get_interpolated_data(meteo_lst, grd, par, shp, dem_data_path, idw_exponent)"},{"path":"/reference/get_interpolated_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Main interpolation function — get_interpolated_data","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). grd sp SpatialGrid grid interpolation. par character representing weather variable extract (.e. \"PCP\", \"SLR\", etc). shp sf dataframe defining basin boundary shape. dem_data_path path DEM raster data projection weather station. idw_exponent numeric value exponent parameter used interpolation. (optional, default value 2).","code":""},{"path":"/reference/get_interpolated_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Main interpolation function — get_interpolated_data","text":"SpatialPointsDataFrame interpolated data.","code":""},{"path":"/reference/get_interpolated_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Main interpolation function — get_interpolated_data","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) get_interpolated_data(met_lst, grd, \"PCP\", read_sf(basin_path), DEM_path, 2) }"},{"path":"/reference/get_interpolation.html","id":null,"dir":"Reference","previous_headings":"","what":"Get interpolation values for catchment — get_interpolation","title":"Get interpolation values for catchment — get_interpolation","text":"Get interpolation values catchment","code":""},{"path":"/reference/get_interpolation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get interpolation values for catchment — get_interpolation","text":"","code":"get_interpolation(sp_df, st, grd, i, idw_exponent)"},{"path":"/reference/get_interpolation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get interpolation values for catchment — get_interpolation","text":"sp_df SpatialPointsDataFrame one column one day rows stations. st SpatialPointsDataFrame virtual stations grid. contain dataframe data station elevation DEM grd Grid interpolation. integer representing day number. idw_exponent numeric value exponent parameter used interpolation.","code":""},{"path":"/reference/get_interpolation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get interpolation values for catchment — get_interpolation","text":"SpatialPointsDataFrame virtual stations interpolated data data dataframe","code":""},{"path":"/reference/get_interpolation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get interpolation values for catchment — get_interpolation","text":"","code":"if (FALSE) { get_interpolation(sp_df, meteo_pts, 2, 2) }"},{"path":"/reference/get_lu_points.html","id":null,"dir":"Reference","previous_headings":"","what":"Preparing training points for remote sensing algorithm — get_lu_points","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"Preparing training points remote sensing algorithm","code":""},{"path":"/reference/get_lu_points.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"","code":"get_lu_points( df, year, lookup, lu_constant = c(), nb_pts = 100, col_name = \"type\" )"},{"path":"/reference/get_lu_points.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"df sf data.frame land use. \"type\" column present. year numeric value, year land use. lookup dataframe \"lc1\" column numeric codes \"type\" column text. lu_constant vector strings land uses kept constant land use (.e. water, urban areas, etc.) nb_pts numeric, number points per land use/crop class. Optional, default 100. col_name string name column used representing type crops/land use. Optional, default \"type\".","code":""},{"path":"/reference/get_lu_points.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"sf data.frame point input remote sensing training algorithm.","code":""},{"path":"/reference/get_lu_points.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preparing training points for remote sensing algorithm — get_lu_points","text":"","code":"if (FALSE) { library(sf) ##Loading land use/crop layer lu_path <- system.file(\"extdata\", \"GIS/lu_layer.shp\", package = \"svatools\") lu <- st_read(lu_path, quiet = TRUE) ##Preparing lookup table lookup <- data.frame(lc1 = seq(1:length(unique(c(lu$type)))), type = unique(c(lu$type))) lu_constant <- c(\"fesc\", \"orch\", \"frst\", \"frse\", \"frsd\", \"urld\", \"urhd\", \"wetl\", \"past\", \"watr\", \"agrl\") ##Getting training points pts <- get_lu_points(lu, 2021, lookup, lu_constant) }"},{"path":"/reference/get_nb_st_with_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Getting number of stations with data for selected parameter — get_nb_st_with_data","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"Getting number stations data selected parameter","code":""},{"path":"/reference/get_nb_st_with_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"","code":"get_nb_st_with_data(meteo_lst, par)"},{"path":"/reference/get_nb_st_with_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par weather variable extract (.e. \"PCP\", \"SLR\", etc)","code":""},{"path":"/reference/get_nb_st_with_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"number stations data parameter.","code":""},{"path":"/reference/get_nb_st_with_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Getting number of stations with data for selected parameter — get_nb_st_with_data","text":"","code":"if (FALSE) { get_nb_st_with_data(meteo_lst, \"PCP\") }"},{"path":"/reference/get_soil_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to fill soil parameters — get_soil_parameters","title":"Function to fill soil parameters — get_soil_parameters","text":"Function fill soil parameters","code":""},{"path":"/reference/get_soil_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to fill soil parameters — get_soil_parameters","text":"","code":"get_soil_parameters(soilp)"},{"path":"/reference/get_soil_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to fill soil parameters — get_soil_parameters","text":"soilp dataframe SNAM, NLAYERS columns SOL_Z, CLAY, SILT, SAND, OC columns available soil layer.","code":""},{"path":"/reference/get_soil_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to fill soil parameters — get_soil_parameters","text":"dataframe fully formatted filled table soil parameters SWAT model.","code":""},{"path":"/reference/get_soil_parameters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to fill soil parameters — get_soil_parameters","text":"","code":"if (FALSE) { soil <- get_soil_parameters(temp_path) }"},{"path":"/reference/hide_show.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"Function put option remove hide show lines chart function also print chart.","code":""},{"path":"/reference/hide_show.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"","code":"hide_show(graph)"},{"path":"/reference/hide_show.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"graph plotly graph object","code":""},{"path":"/reference/hide_show.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"plotly graph object option remove show lines","code":""},{"path":"/reference/hide_show.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to put option remove hide or show all lines in chart and this function also print chart. — hide_show","text":"","code":"if (FALSE) { hide_show(fig) }"},{"path":"/reference/interpolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpolating and writing results into model input files — interpolate","title":"Interpolating and writing results into model input files — interpolate","text":"Interpolating writing results model input files","code":""},{"path":"/reference/interpolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpolating and writing results into model input files — interpolate","text":"","code":"interpolate( meteo_lst, catchment_boundary_path, dem_data_path, grid_spacing, p_vector = c(\"PCP\", \"SLR\", \"RELHUM\", \"WNDSPD\", \"TMP_MAX\", \"TMP_MIN\"), idw_exponent = 2 )"},{"path":"/reference/interpolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpolating and writing results into model input files — interpolate","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). catchment_boundary_path path basin boundary shape file. dem_data_path path DEM raster data projection weather station. grid_spacing numeric value distance grid. Units coordinate system used. p_vector character vector representing weather variables interpolate (optional, default variables selected c(\"PCP\", \"SLR\", \"RELHUM\", \"WNDSPD\", \"TMP_MAX\", \"TMP_MIN\" ). idw_exponent numeric value exponent parameter used interpolation (optional, default value 2).","code":""},{"path":"/reference/interpolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpolating and writing results into model input files — interpolate","text":"nested list lists dataframes interpolation results. Nested structure lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Function also writes SWAT weather text input files interpolation results.","code":""},{"path":"/reference/interpolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interpolating and writing results into model input files — interpolate","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) interpolate(met_lst, basin_path, DEM_path, 2000) }"},{"path":"/reference/list_to_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Transforming list to dataframe — list_to_df","title":"Transforming list to dataframe — list_to_df","text":"Transforming list dataframe","code":""},{"path":"/reference/list_to_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transforming list to dataframe — list_to_df","text":"","code":"list_to_df(lst)"},{"path":"/reference/list_to_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transforming list to dataframe — list_to_df","text":"lst nested list dataframes (weather variable)","code":""},{"path":"/reference/list_to_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transforming list to dataframe — list_to_df","text":"dataframe collected dataframes single dataframe.","code":""},{"path":"/reference/list_to_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transforming list to dataframe — list_to_df","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) df <- list_to_df(met_lst$data$ID1) }"},{"path":"/reference/load_climate.html","id":null,"dir":"Reference","previous_headings":"","what":"Function loading climate data csv files — load_climate","title":"Function loading climate data csv files — load_climate","text":"Function loading climate data csv files","code":""},{"path":"/reference/load_climate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function loading climate data csv files — load_climate","text":"","code":"load_climate( f_path, f_lst = list(PCP = \"prec-1\", SLR = \"solarRad-1\", RELHUM = \"relHum-1\", TMP_MAX = \"Tmax-1\", TMP_MIN = \"Tmin-1\", WNDSPD = \"windSpeed-1\") )"},{"path":"/reference/load_climate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function loading climate data csv files — load_climate","text":"f_path character providing path climate date folder (.e. \"inst/extdata/CORDEX-BC\") f_lst list providing file name weather parameter (default list(\"PCP\" = \"prec-1\", \"SLR\" = \"solarRad-1\", \"RELHUM\" = \"relHum-1\", \"TMP_MAX\" = \"Tmax-1\", \"TMP_MIN\" = \"Tmin-1\", \"WNDSPD\" = \"windSpeed-1\"))","code":""},{"path":"/reference/load_climate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function loading climate data csv files — load_climate","text":"nested list lists dataframes. Nested structure meteo_lst -> RCM_MOD-> Parameter -> Dataframe (DATE, PARAMETER).","code":""},{"path":"/reference/load_climate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function loading climate data csv files — load_climate","text":"","code":"if (FALSE) { temp_path <- paste0(system.file(\"extdata\", package = \"svatools\"), \"/CORDEX-BC\") cli_lst <- load_climate(temp_path) str(cli_lst) }"},{"path":"/reference/load_stations.html","id":null,"dir":"Reference","previous_headings":"","what":"Reading station data from excel templates — load_stations","title":"Reading station data from excel templates — load_stations","text":"Reading station data excel templates","code":""},{"path":"/reference/load_stations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reading station data from excel templates — load_stations","text":"","code":"load_stations(template_path, epsg_code)"},{"path":"/reference/load_stations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reading station data from excel templates — load_stations","text":"template_path path *.xlsx file. epsg_code EPSG code station coordinates system.","code":""},{"path":"/reference/load_stations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reading station data from excel templates — load_stations","text":"sf dataframe station information.","code":""},{"path":"/reference/load_stations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reading station data from excel templates — load_stations","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") stations <- load_stations(temp_path, 3035) str(stations) library(sf) library(mapview) mapview(st_transform(stations, 4326)) }"},{"path":"/reference/load_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"Function providing loading data templates (single multiple data sheets) (data cleaned ).","code":""},{"path":"/reference/load_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"","code":"load_template(template_path, epsg_code = 4326)"},{"path":"/reference/load_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"template_path path *.xlsx file. epsg_code EPSG code station coordinates (default 4326 WGS 84 coordinate system) system","code":""},{"path":"/reference/load_template.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"list two: stations dataframe, contains station information, second member list contains contains measurement data dataframe (data one sheet) nested list dataframes parameter separate dataframe.","code":""},{"path":"/reference/load_template.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function providing loading for data templates (with single and multiple data sheets)\r\n(data should have been cleaned before). — load_template","text":"","code":"##Two types of templates could be used # 1) Example of template for weather data temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) #> [1] \"Loading data from template.\" #> [1] \"Reading station ID9 data.\" #> [1] \"Reading station ID3 data.\" #> [1] \"Reading station ID10 data.\" #> [1] \"Reading station ID11 data.\" #> [1] \"Reading station ID12 data.\" #> [1] \"Reading station ID13 data.\" #> [1] \"Reading station ID1 data.\" #> [1] \"Reading station ID8 data.\" #> [1] \"Reading station ID2 data.\" #> [1] \"Reading station ID4 data.\" #> [1] \"Reading station ID5 data.\" #> [1] \"Reading station ID6 data.\" #> [1] \"Reading station ID7 data.\" #> [1] \"Reading station ID14 data.\" #> [1] \"Loading of data is finished.\" str(met_lst) #> List of 2 #> $ stations: sf [14 × 7] (S3: sf/tbl_df/tbl/data.frame) #> ..$ ID : chr [1:14] \"ID1\" \"ID2\" \"ID3\" \"ID4\" ... #> ..$ Name : chr [1:14] \"RUDZK DUZY\" \"GLEBOKIE\" \"GLODOWO\" \"SOBIESIERNIE\" ... #> ..$ Elevation: chr [1:14] \"98.7\" \"85.0\" \"99.0\" \"80.0\" ... #> ..$ Source : chr [1:14] \"ODR\" \"IMGW\" \"IMGW\" \"IMGW\" ... #> ..$ geometry :sfc_POINT of length 14; first list element: 'XY' num [1:2] 4892472 3300305 #> ..$ Long : num [1:14] 4892472 4891414 4941373 4890114 4899376 ... #> ..$ Lat : num [1:14] 3300305 3315522 3342216 3326585 3286548 ... #> ..- attr(*, \"sf_column\")= chr \"geometry\" #> ..- attr(*, \"agr\")= Factor w/ 3 levels \"constant\",\"aggregate\",..: NA NA NA NA NA NA #> .. ..- attr(*, \"names\")= chr [1:6] \"ID\" \"Name\" \"Elevation\" \"Source\" ... #> $ data :List of 14 #> ..$ ID9 :List of 6 #> .. ..$ SLR : tibble [2,952 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:2952], format: \"2014-01-01\" ... #> .. .. ..$ SLR : num [1:2952] 1.32 2.83 2.73 2.08 2.25 ... #> .. ..$ RELHUM : tibble [3,317 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:3317], format: \"2014-01-01\" ... #> .. .. ..$ RELHUM: num [1:3317] 0.885 0.873 0.881 0.887 0.888 ... #> .. ..$ TMP_MAX: tibble [3,317 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:3317], format: \"2014-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:3317] 3.4 4.2 6.4 6.6 9.3 7.2 9.6 9.3 9.6 9.4 ... #> .. ..$ TMP_MIN: tibble [3,306 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:3306], format: \"2014-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:3306] 0.4 -1.6 0.3 2.4 4.1 1.4 4.2 4.4 3.1 5.1 ... #> .. ..$ PCP : tibble [3,197 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:3197], format: \"2014-05-01\" ... #> .. .. ..$ PCP : num [1:3197] 0 1 0 0.2 0 0.2 1 0 0.8 0.2 ... #> .. ..$ WNDSPD : tibble [760 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:760], format: \"2021-01-01\" ... #> .. .. ..$ WNDSPD: num [1:760] 1.89 1.1 2.68 2.36 1.85 ... #> ..$ ID3 :List of 3 #> .. ..$ TMP_MAX: tibble [6,726 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:6726], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:6726] 6.6 4.4 7.5 6.2 6.6 6.2 5 7 4.6 3.5 ... #> .. ..$ TMP_MIN: tibble [6,726 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:6726], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:6726] 1 1.6 1.5 3.3 2.5 2.5 2.8 3.5 1.6 1.1 ... #> .. ..$ RELHUM : tibble [1,154 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1154], format: \"1998-01-01\" ... #> .. .. ..$ RELHUM: num [1:1154] 0.91 0.903 0.86 0.828 0.823 0.92 0.923 0.895 0.943 0.96 ... #> ..$ ID10:List of 4 #> .. ..$ TMP_MAX: tibble [1,948 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1948], format: \"2016-06-01\" ... #> .. .. ..$ TMP_MAX: num [1:1948] 25.3 23.8 23.5 26 23.6 19 21.4 26.7 20 21.3 ... #> .. ..$ TMP_MIN: tibble [1,948 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1948], format: \"2016-06-01\" ... #> .. .. ..$ TMP_MIN: num [1:1948] 14.3 14.5 13 11.8 10.7 8.9 6.1 8 10.1 8.5 ... #> .. ..$ RELHUM : tibble [1,947 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1947], format: \"2016-06-01\" ... #> .. .. ..$ RELHUM: num [1:1947] 0.787 0.806 0.646 0.646 0.551 0.541 0.486 0.546 0.601 0.62 ... #> .. ..$ WNDSPD : tibble [1,932 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1932], format: \"2016-06-01\" ... #> .. .. ..$ WNDSPD: num [1:1932] 3.4 3.2 3.6 1.5 3.1 3.4 1.6 2.6 2.3 4 ... #> ..$ ID11:List of 3 #> .. ..$ SLR : tibble [6,940 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:6940], format: \"2000-01-01\" ... #> .. .. ..$ SLR : num [1:6940] 1.16 0.85 0.77 0.55 0.82 1.02 0.77 2.72 0.54 0.99 ... #> .. ..$ MAXHHR: tibble [4,040 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:4040], format: \"2008-06-06\" ... #> .. .. ..$ MAXHHR: num [1:4040] 0 0 0 0 0 0 0 0.9 0.1 0.2 ... #> .. ..$ PCP : tibble [4,040 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:4040], format: \"2008-06-06\" ... #> .. .. ..$ PCP : num [1:4040] 0 0 0 0 0 0 0 2.8 0.1 0.3 ... #> ..$ ID12:List of 6 #> .. ..$ TMP_MAX: tibble [8,571 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8571], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:8571] 6.8 4.7 8.1 6.6 7.4 5.6 6.6 8.1 5.5 6 ... #> .. ..$ TMP_MIN: tibble [8,571 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8571], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:8571] 1.1 0.4 1.1 3.2 1.6 2.2 2.7 4.7 1.7 1.7 ... #> .. ..$ RELHUM : tibble [6,574 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:6574], format: \"1998-01-01\" ... #> .. .. ..$ RELHUM: num [1:6574] 0.903 0.94 0.83 0.825 0.755 0.908 0.935 0.9 0.975 0.983 ... #> .. ..$ WNDSPD : tibble [6,574 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:6574], format: \"1998-01-01\" ... #> .. .. ..$ WNDSPD: num [1:6574] 2 1.7 3.7 4.3 7.3 3.7 3 5 2 1.7 ... #> .. ..$ MAXHHR : tibble [3,187 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:3187], format: \"2012-06-13\" ... #> .. .. ..$ MAXHHR: num [1:3187] 6.6 8.5 0.2 3.8 4.4 0.1 0 9.9 2.9 0.5 ... #> .. ..$ PCP : tibble [3,187 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:3187], format: \"2012-06-13\" ... #> .. .. ..$ PCP : num [1:3187] 13 21.3 0.3 10.9 7.8 0.1 0 34.1 21.1 0.9 ... #> ..$ ID13:List of 5 #> .. ..$ TMP_MAX: tibble [8,379 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8379], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:8379] 7.5 5.5 8.7 7.2 8.5 5.7 6.4 9 5.8 4.8 ... #> .. ..$ TMP_MIN: tibble [8,380 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8380], format: \"1998-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:8380] 1.1 0.5 1.5 3.5 2.2 2.7 2.7 4.5 2.6 1.9 ... #> .. ..$ SLR : tibble [6,915 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:6915], format: \"2000-01-01\" ... #> .. .. ..$ SLR : num [1:6915] 1.45 0.97 0.55 0.31 1.15 0.96 0.71 2.6 2.52 1.06 ... #> .. ..$ RELHUM : tibble [8,358 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8358], format: \"1998-01-01\" ... #> .. .. ..$ RELHUM: num [1:8358] 0.949 0.923 0.826 0.809 0.706 0.928 0.911 0.873 0.973 0.994 ... #> .. ..$ WNDSPD : tibble [8,401 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:8401], format: \"1998-01-01\" ... #> .. .. ..$ WNDSPD: num [1:8401] 2.5 3.5 4.9 5.5 7.5 6.4 5.1 6.5 2.5 2 ... #> ..$ ID1 :List of 5 #> .. ..$ TMP_MAX: tibble [930 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:930], format: \"2018-10-27\" ... #> .. .. ..$ TMP_MAX: num [1:930] 11.2 7.9 10.1 19.6 15.4 ... #> .. ..$ TMP_MIN: tibble [868 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:868], format: \"2018-10-27\" ... #> .. .. ..$ TMP_MIN: num [1:868] 4.67 0.64 1.8 10.09 4.57 ... #> .. ..$ RELHUM : tibble [565 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:565], format: \"2018-10-27\" ... #> .. .. ..$ RELHUM: num [1:565] 0.716 0.877 0.983 0.81 0.712 ... #> .. ..$ WNDSPD : tibble [930 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:930], format: \"2018-10-27\" ... #> .. .. ..$ WNDSPD: num [1:930] 1.09 1.47 3.04 2.97 1 ... #> .. ..$ PCP : tibble [384 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:384], format: \"2019-01-01\" ... #> .. .. ..$ PCP : num [1:384] 4.3 1.1 1.8 2.5 5.9 1.2 0.7 1.5 3.3 0.1 ... #> ..$ ID8 :List of 4 #> .. ..$ TMP_MAX: tibble [1,171 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1171], format: \"2018-01-01\" ... #> .. .. ..$ TMP_MAX: num [1:1171] 9.68 3.79 4.13 7.41 7.63 7.54 4.05 1.45 2.14 1.27 ... #> .. ..$ TMP_MIN: tibble [1,165 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1165], format: \"2018-01-01\" ... #> .. .. ..$ TMP_MIN: num [1:1165] 2.59 0.36 0.21 3.95 3.47 2 -2.74 -6.5 -4.29 -0.33 ... #> .. ..$ RELHUM : tibble [1,171 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:1171], format: \"2018-01-01\" ... #> .. .. ..$ RELHUM: num [1:1171] 0.875 0.969 0.974 0.897 0.903 ... #> .. ..$ PCP : tibble [402 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:402], format: \"2018-04-01\" ... #> .. .. ..$ PCP : num [1:402] 6.8 1.6 7 8.4 3.6 4.2 0.6 2.4 27.2 0.2 ... #> ..$ ID2 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0 0 0 0 0 0 0 0 0 0 ... #> ..$ ID4 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0 0 2.2 0 3.5 1.3 9.2 6.3 0.4 0 ... #> ..$ ID5 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0 0 2.1 0.3 4.7 0.8 2.5 13.5 0.6 0.7 ... #> ..$ ID6 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0.5 0 2.2 0 3.6 1.2 3.2 7 0 0 ... #> ..$ ID7 :List of 1 #> .. ..$ PCP: tibble [8,673 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:8673], format: \"1998-01-01\" ... #> .. .. ..$ PCP : num [1:8673] 0 0 2.8 0.3 3.1 2.1 1.5 11.6 1 0.2 ... #> ..$ ID14:List of 2 #> .. ..$ SLR : tibble [365 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE: POSIXct[1:365], format: \"2022-01-01\" ... #> .. .. ..$ SLR : num [1:365] 0.683 0.798 0.671 1.203 1.064 ... #> .. ..$ RELHUM: tibble [365 × 2] (S3: tbl_df/tbl/data.frame) #> .. .. ..$ DATE : POSIXct[1:365], format: \"2022-01-01\" ... #> .. .. ..$ RELHUM: num [1:365] 1 1 1 1 1 1 1 1 1 1 ... ## 2) Example of template for calibration data temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" str(cal_data) #> List of 2 #> $ stations: sf [20 × 6] (S3: sf/tbl_df/tbl/data.frame) #> ..$ ID : chr [1:20] \"5\" \"8\" \"4\" \"2\" ... #> ..$ Name : chr [1:20] \"Zgłowiączka-Strózewo-Parcele\" \"DopZStarRadziejewa - Witowo\" \"Zgłąwiączka-ponizej. Osiecin,Samszyce\" \"Zgłowiączka-pow.Osiecin. Piołunowo\" ... #> ..$ Description: chr [1:20] \"powyżej jez. Głuszyńskiego (60 km)\" \"Kolonia Witowo\" \"Samszyce - poniżej Osięcin (67,8 km)\" \"powyżej Osięcin (75,2 km)\" ... #> ..$ geometry :sfc_POINT of length 20; first list element: 'XY' num [1:2] 18.7 52.6 #> ..$ Long : num [1:20] 18.7 18.7 18.7 18.7 18.7 ... #> ..$ Lat : num [1:20] 52.6 52.6 52.6 52.6 52.6 ... #> ..- attr(*, \"sf_column\")= chr \"geometry\" #> ..- attr(*, \"agr\")= Factor w/ 3 levels \"constant\",\"aggregate\",..: NA NA NA NA NA #> .. ..- attr(*, \"names\")= chr [1:5] \"ID\" \"Name\" \"Description\" \"Long\" ... #> $ data : tibble [22,306 × 5] (S3: tbl_df/tbl/data.frame) #> ..$ Station : chr [1:22306] \"1\" \"1\" \"1\" \"1\" ... #> ..$ DATE : POSIXct[1:22306], format: \"2008-03-11\" \"2008-03-25\" ... #> ..$ Variables: chr [1:22306] \"N-NH4\" \"N-NH4\" \"N-NH4\" \"N-NH4\" ... #> ..$ Values : num [1:22306] 0.05 0.05 0.05 0.05 0.05 0.05 0.83 0.25 0.51 1.89 ... #> ..$ Source : chr [1:22306] \"grab sample\" \"grab sample\" \"grab sample\" \"grab sample\" ..."},{"path":"/reference/min_ks.html","id":null,"dir":"Reference","previous_headings":"","what":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"Get min Ks within depth range SOL_K SOL_Z","code":""},{"path":"/reference/min_ks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"","code":"min_ks(df, max_depth)"},{"path":"/reference/min_ks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"df one row dataframe soil parameters SOL_K SOL_Z columns max_depth numeric value depth range 0.","code":""},{"path":"/reference/min_ks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"numeric min Ks parameter value within depth range","code":""},{"path":"/reference/min_ks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get min Ks within depth range from SOL_K and SOL_Z — min_ks","text":"","code":"if (FALSE) { df <- data.frame(SOL_K1 = 10, SOL_K2 = 1, SOL_K3 = 2, SOL_Z1 = 250, SOL_Z2 = 700, SOL_Z3 = 1000) min_ks(df, 800) }"},{"path":"/reference/my.check.pwd.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for my.pwd function — my.check.pwd","title":"Helper for my.pwd function — my.check.pwd","text":"Helper .pwd function","code":""},{"path":"/reference/my.check.pwd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper for my.pwd function — my.check.pwd","text":"","code":"my.check.pwd(x)"},{"path":"/reference/my.check.pwd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for my.pwd function — my.check.pwd","text":"x vector numbers","code":""},{"path":"/reference/my.check.pwd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper for my.pwd function — my.check.pwd","text":"logical, wet day dry","code":""},{"path":"/reference/my.check.pwd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper for my.pwd function — my.check.pwd","text":"","code":"if (FALSE) { my.check.pwd(x) }"},{"path":"/reference/my.check.pww.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper for my.pww function — my.check.pww","title":"Helper for my.pww function — my.check.pww","text":"Helper .pww function","code":""},{"path":"/reference/my.check.pww.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper for my.pww function — my.check.pww","text":"","code":"my.check.pww(x)"},{"path":"/reference/my.check.pww.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper for my.pww function — my.check.pww","text":"x vector numbers","code":""},{"path":"/reference/my.check.pww.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper for my.pww function — my.check.pww","text":"logical, wet day wet","code":""},{"path":"/reference/my.check.pww.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper for my.pww function — my.check.pww","text":"","code":"if (FALSE) { my.check.pww(x) }"},{"path":"/reference/my.pcpd.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate pcp_days parameter — my.pcpd","title":"Function to calculate pcp_days parameter — my.pcpd","text":"Function calculate pcp_days parameter","code":""},{"path":"/reference/my.pcpd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate pcp_days parameter — my.pcpd","text":"","code":"my.pcpd(x, nyears, na.rm = FALSE)"},{"path":"/reference/my.pcpd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate pcp_days parameter — my.pcpd","text":"x vector numbers nyears numeric number years available PCP variable na.rm logical, NA cleaned","code":""},{"path":"/reference/my.pcpd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate pcp_days parameter — my.pcpd","text":"numeric result","code":""},{"path":"/reference/my.pcpd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate pcp_days parameter — my.pcpd","text":"","code":"if (FALSE) { my.pcpd(x) }"},{"path":"/reference/my.pwd.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate wet_dry parameter — my.pwd","title":"Function to calculate wet_dry parameter — my.pwd","text":"Function calculate wet_dry parameter","code":""},{"path":"/reference/my.pwd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate wet_dry parameter — my.pwd","text":"","code":"my.pwd(x, na.rm = FALSE)"},{"path":"/reference/my.pwd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate wet_dry parameter — my.pwd","text":"x vector numbers na.rm logical, NA cleaned","code":""},{"path":"/reference/my.pwd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate wet_dry parameter — my.pwd","text":"numeric result","code":""},{"path":"/reference/my.pwd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate wet_dry parameter — my.pwd","text":"","code":"if (FALSE) { my.pwd(x) }"},{"path":"/reference/my.pww.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate wet_wet parameter — my.pww","title":"Function to calculate wet_wet parameter — my.pww","text":"Function calculate wet_wet parameter","code":""},{"path":"/reference/my.pww.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate wet_wet parameter — my.pww","text":"","code":"my.pww(x, na.rm = FALSE)"},{"path":"/reference/my.pww.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate wet_wet parameter — my.pww","text":"x vector numbers na.rm logical, NA cleaned","code":""},{"path":"/reference/my.pww.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate wet_wet parameter — my.pww","text":"numeric result","code":""},{"path":"/reference/my.pww.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate wet_wet parameter — my.pww","text":"","code":"if (FALSE) { my.pww(x) }"},{"path":"/reference/my.skew.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate pcp_skew parameter — my.skew","title":"Function to calculate pcp_skew parameter — my.skew","text":"Function calculate pcp_skew parameter","code":""},{"path":"/reference/my.skew.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate pcp_skew parameter — my.skew","text":"","code":"my.skew(x, na.rm = FALSE)"},{"path":"/reference/my.skew.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate pcp_skew parameter — my.skew","text":"x vector numbers na.rm logical, NA cleaned","code":""},{"path":"/reference/my.skew.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate pcp_skew parameter — my.skew","text":"numeric result","code":""},{"path":"/reference/my.skew.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate pcp_skew parameter — my.skew","text":"","code":"if (FALSE) { my.skew(x) }"},{"path":"/reference/nyears.html","id":null,"dir":"Reference","previous_headings":"","what":"Count number of years data available for variable — nyears","title":"Count number of years data available for variable — nyears","text":"Count number years data available variable","code":""},{"path":"/reference/nyears.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count number of years data available for variable — nyears","text":"","code":"nyears(df, col)"},{"path":"/reference/nyears.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count number of years data available for variable — nyears","text":"df dataframe DATE column least one variable column. col variable column name","code":""},{"path":"/reference/nyears.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count number of years data available for variable — nyears","text":"numeric number year data available.","code":""},{"path":"/reference/nyears.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count number of years data available for variable — nyears","text":"","code":"if (FALSE) { nyears(df, \"PCP\") }"},{"path":"/reference/plot_cal_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting calibration data — plot_cal_data","title":"Plotting calibration data — plot_cal_data","text":"Plotting calibration data","code":""},{"path":"/reference/plot_cal_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting calibration data — plot_cal_data","text":"","code":"plot_cal_data(df, stations, variables = levels(as.factor(df$Variables)))"},{"path":"/reference/plot_cal_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting calibration data — plot_cal_data","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) stations character vector listing stations, selected figure. variables optional parameter character vector, parameters figure.","code":""},{"path":"/reference/plot_cal_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting calibration data — plot_cal_data","text":"plotly object interactive figure.","code":""},{"path":"/reference/plot_cal_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting calibration data — plot_cal_data","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_cal_data(cal_data$data, stations = c(\"1\", \"2\", \"3\",\"10\"), variables = c(\"PT\", \"Q\")) 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each month between parts of nutrient and total — plot_fractions","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"Plot regression fractions month parts nutrient total","code":""},{"path":"/reference/plot_fractions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"","code":"plot_fractions(df, station, total_var, min_vars)"},{"path":"/reference/plot_fractions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed). station character vector indicating station/s, selected figure. total_var character vector variable selected represent total certain variable. min_vars character vector variable/s selected represent mineral organic certain variable.","code":""},{"path":"/reference/plot_fractions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"list two ggplot objects: regression - monthly regression plots total vs part (.e. mineral organic), fraction - monthly fraction values.","code":""},{"path":"/reference/plot_fractions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot regression and fractions for each month between parts of nutrient and total — plot_fractions","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_fractions(cal_data$data, c(\"4\"), c(\"NT\"), c(\"N-NO3\", \"N-NH4\", \"N-NO2\")) #> $regression #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()` #> Caused by error in `check_output()`: #> ! out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()` #> Caused by error in `check_output()`: #> ! out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> #> $fraction #> plot_fractions(cal_data$data, c(\"4\"), c(\"PT\"), c(\"P-PO4\")) #> $regression #> #> $fraction #>"},{"path":"/reference/plot_many.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting stations with few data points — plot_many","title":"Plotting stations with few data points — plot_many","text":"Plotting stations data points","code":""},{"path":"/reference/plot_many.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting stations with few data points — plot_many","text":"","code":"plot_many(df, drop_st = c())"},{"path":"/reference/plot_many.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting stations with few data points — plot_many","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) drop_st optional parameter character vector, stations excluded figure. Stations lot data plotted plot_one function.","code":""},{"path":"/reference/plot_many.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting stations with few data points — plot_many","text":"plotly object interactive figure","code":""},{"path":"/reference/plot_many.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting stations with few data points — plot_many","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_many(cal_data$data, drop_st = c(\"4\")) {\"x\":{\"data\":[{\"x\":[1205193600,1206403200,1208217600,1209340800,1210550400,1211500800,1226880000,1228089600,1229299200,1236556800,1237161600,1238371200,1267401600,1269820800,1270512000,1271030400,1274659200,1275264000,1289174400,1290384000,1367193600,1387152000,1388361600,1389571200,1395619200,1395878400,1396828800,1511481600,1513641600,1515024000,1515628800,1517270400],\"y\":[0.05,0.05,0.05,0.05,0.05,0.05,0.83,0.25,0.51,1.89,0.25,0.25,0.25,0.05,0.05,0.05,0.53,0.64,0.4,0.05,0.25,0.25,0.92,0.25,0.25,0.25,0.25,0.25,0.25,0.25,0.25,0.25],\"text\":[\"DATE: 2008-03-11
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points — plot_map","title":"Preparing interactive map of monitoring points — plot_map","text":"Preparing interactive map monitoring points","code":""},{"path":"/reference/plot_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preparing interactive map of monitoring points — plot_map","text":"","code":"plot_map(df, df_station, rch, shp)"},{"path":"/reference/plot_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preparing interactive map of monitoring points — plot_map","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed). df_station dataframe formatted station data (ID, geometry columns needed). rch sf dataframe reaches (id, geometry columns needed). shp sf dataframe basin (name, geometry columns needed).","code":""},{"path":"/reference/plot_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preparing interactive map of monitoring points — plot_map","text":"leaflet object interactive map monitoring data opening pressing points.","code":""},{"path":"/reference/plot_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preparing interactive map of monitoring points — plot_map","text":"","code":"library(sf) #> Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") reach_path <- system.file(\"extdata\", \"GIS/reaches.shp\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") cal_data <- load_template(temp_path, 4326) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" reach <- st_transform(st_read(reach_path), 4326) #> Reading layer `reaches' from data source #> `C:\\Users\\laptop\\AppData\\Local\\R\\win-library\\4.2\\svatools\\extdata\\GIS\\reaches.shp' #> using driver `ESRI Shapefile' #> Simple feature collection with 125 features and 2 fields #> Geometry type: LINESTRING #> Dimension: XY #> Bounding box: xmin: 469806.3 ymin: 519299 xmax: 484124.6 ymax: 533957.5 #> Projected CRS: ETRF2000-PL / CS92 basin <-st_transform(st_read(basin_path), 4326) #> Reading layer `basin' from data source #> `C:\\Users\\laptop\\AppData\\Local\\R\\win-library\\4.2\\svatools\\extdata\\GIS\\basin.shp' #> using driver `ESRI Shapefile' #> Simple feature collection with 1 feature and 1 field #> Geometry type: POLYGON #> Dimension: XY #> Bounding box: xmin: 4898668 ymin: 3304508 xmax: 4915266 ymax: 3319644 #> Projected CRS: ETRS89-extended / LAEA Europe plot_map(cal_data$data, cal_data$stations, reach, basin) {\"x\":{\"options\":{\"crs\":{\"crsClass\":\"L.CRS.EPSG3857\",\"code\":null,\"proj4def\":null,\"projectedBounds\":null,\"options\":{}}},\"calls\":[{\"method\":\"addProviderTiles\",\"args\":[\"OpenStreetMap\",null,\"OSM\",{\"errorTileUrl\":\"\",\"noWrap\":false,\"detectRetina\":false}]},{\"method\":\"addProviderTiles\",\"args\":[\"Esri.WorldImagery\",null,\"Imagery\",{\"errorTileUrl\":\"\",\"noWrap\":false,\"detectRetina\":false}]},{\"method\":\"addProviderTiles\",\"args\":[\"OpenTopoMap\",null,\"Topography\",{\"errorTileUrl\":\"\",\"noWrap\":false,\"detectRetina\":false}]},{\"method\":\"addPolygons\",\"args\":[[[[{\"lng\":[18.7502099240057,18.7504926933119,18.7506871710007,18.7509986800875,18.7512071166416,18.7508115137972,18.7505916199336,18.7505197040129,18.7506401597032,18.7508813711807,18.7507345763972,18.7501611148954,18.7502514424212,18.7506534536152,18.7513363458376,18.7514593333211,18.7516379752494,18.7521121762843,18.7523785823232,18.752837084082,18.753563554398,18.7543039355989,18.7545425346942,18.7546205610143,18.7555547559262,18.7557183852302,18.755808114847,18.7559436334374,18.7562407226947,18.7569373483367,18.7572775706597,18.7583271966211,18.7587571304625,18.758846349337,18.7590546897903,18.7594258430317,18.7594422986243,18.7592984080839,18.7592111677966,18.7590807784513,18.7590103444975,18.759070592959,18.7589271605303,18.7591508518052,18.7597425012178,18.7603033153805,18.7619294488274,18.762772651462,18.7640736140535,18.7643260406867,18.7643416876192,18.7643431366321,18.7640635755685,18.7631646363843,18.7631519465912,18.7635974855205,18.7638787299317,18.7657705493919,18.7659483988432,18.7660681952442,18.7662609148815,18.7665567348396,18.7670889798359,18.7678583626752,18.7685976169044,18.7687462352002,18.7689247840753,18.7692221090683,18.76930128557,18.7696732799711,18.7695566794069,18.7692764145533,18.7686412992974,18.7683758127983,18.7679035647087,18.7670185594167,18.7665017056602,18.7659998683725,18.7648771307765,18.7642279800905,18.7635338790701,18.763342430576,18.7631961336791,18.7633450492251,18.7639236967941,18.7640724614786,18.763896130436,18.7627886019696,18.7617253545133,18.7598504868083,18.7592753604772,18.7585673676424,18.7568833723323,18.7567658741343,18.7567088201532,18.7565629722895,18.7568461234213,18.7564946391979,18.7562885539669,18.7561263416647,18.7558313157327,18.7552428718211,18.755155283476,18.75489146842,18.7546573050474,18.7545560038527,18.754719564037,18.7553416888213,18.7561257577379,18.7566897715909,18.756706737367,18.7562497337737,18.7556151629093,18.755365282292,18.7553819589672,18.755663384716,18.756284471264,18.7565813629803,18.7567597753474,18.7567759923388,18.7566741830516,18.7570005097336,18.7576958295215,18.758006681697,18.7583924796056,18.7596370404868,18.7601399077081,18.7609076281592,18.7617209832863,18.7624165023874,18.7628604665398,18.7621975146983,18.7613722381999,18.7604742979646,18.7595021830991,18.7589728097015,18.7582209012121,18.7573053846903,18.75618233592,18.7554585855524,18.7545579421626,18.753673333048,18.7531441303318,18.7527030013065,18.7523519695625,18.7524592617534,18.7527883945441,18.753237639499,18.7536407400864,18.7537921074785,18.7538247697243,18.7532953336809,18.7528853669791,18.7526951052413,18.7519467699306,18.7520671947247,18.7524530737031,18.7522630768024,18.7520285278717,18.7517632285112,18.7511132741856,18.7500047204835,18.7499160153213,18.7494884865192,18.749298362125,18.7489600064725,18.7485020264981,18.7480148730685,18.7471145060678,18.746420774244,18.7459776644383,18.7455336165549,18.7451494678098,18.744765161691,18.7443080665634,18.7438218072708,18.7433954597056,18.7432051743331,18.7431480007496,18.7433121142697,18.7436973535552,18.7443777054692,18.7448512719434,18.7453399656794,18.7458583422864,18.7460218609611,18.7460230047748,18.7459220356903,18.7456886460065,18.7456604683802,18.7456628112005,18.7456494515216,18.745443570406,18.7451484209989,18.7448539509131,18.7445893791049,18.7444141327971,18.7443273217981,18.7442986816499,18.7442844925026,18.7441672114052,18.7441682063312,18.7443321886255,18.7445844427194,18.7449994172933,18.7456064283742,18.7460648008676,18.746582643235,18.7468934190588,18.7473962831945,18.7476186762919,18.7476194511092,18.7473396050638,18.7470012739343,18.7466482859084,18.7465466502053,18.7465330794998,18.7464602984919,18.7462695128279,18.7461673622238,18.7461543130422,18.7462140940279,18.7462439324602,18.7464966223444,18.7469415411975,18.7470023543017,18.7468287721994,18.7464915605989,18.7460222713869,18.7451106440782,18.7448180603188,18.7445695596948,18.744188259059,18.7434820498631,18.7431736819231,18.7427632500902,18.7425592754916,18.742545409654,18.7425614877859,18.7428149543502,18.7432172861389,18.7436780717777,18.7438567720383,18.7440935696852,18.7446251584884,18.7452304448501,18.7457915531925,18.7463227763357,18.7464992285063,18.7466747487117,18.7468806212503,18.7471012040726,18.7474110767007,18.7477509872302,18.7482540371705,18.7485206039952,18.7490232354614,18.7496430774134,18.7499972292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values of single station to interactive monthly box plots — plot_monthly","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"Aggregating values single station interactive monthly box plots","code":""},{"path":"/reference/plot_monthly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"","code":"plot_monthly(df, station, variables = levels(as.factor(df$Variables)))"},{"path":"/reference/plot_monthly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character indicating station, selected figure. variables optional parameter character vector, parameters figure.","code":""},{"path":"/reference/plot_monthly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"plotly object interactive figure","code":""},{"path":"/reference/plot_monthly.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly","text":"","code":"temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_monthly(cal_data$data, station = \"4\") 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figure.","code":""},{"path":"/reference/plot_monthly_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly_one","text":"plotly object interactive figure","code":""},{"path":"/reference/plot_monthly_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregating values of single station to interactive monthly box plots — plot_monthly_one","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_monthly_one(cal_data$data, station = \"4\", drop_variables = c(\"Q\", \"Q1h\", \"Q2h\")) 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N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"Plot N min NT regression month fraction Nmin NT","code":""},{"path":"/reference/plot_NminTN.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"","code":"plot_NminTN(df, station)"},{"path":"/reference/plot_NminTN.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character vector indicating station/s, selected figure.","code":""},{"path":"/reference/plot_NminTN.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"list two ggplot objects: regression - monthly regression plots NT vs Nmin, fraction - monthly fraction values Nmin/NT","code":""},{"path":"/reference/plot_NminTN.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_NminTN","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_NminTN(cal_data$data, station = c(\"4\")) #> $regretion #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()`: #> out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()`: #> out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> #> $fraction #>"},{"path":"/reference/plot_Nmin_NT.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"Plot N min NT regression month fraction Nmin NT","code":""},{"path":"/reference/plot_Nmin_NT.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"","code":"plot_Nmin_NT(df, station)"},{"path":"/reference/plot_Nmin_NT.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character vector indicating station/s, selected figure.","code":""},{"path":"/reference/plot_Nmin_NT.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"list two ggplot objects: regression - monthly regression plots NT vs Nmin, fraction - monthly fraction values Nmin/NT","code":""},{"path":"/reference/plot_Nmin_NT.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot N min to NT regression in each month and fraction Nmin of NT — plot_Nmin_NT","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_Nmin_NT(cal_data$data, station = c(\"4\")) #> $regretion #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()`: #> out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> Warning: Upper limit outside search range. Set to the maximum of the parameter range. #> Warning: Computation failed in `stat_poly_eq()`: #> out[1] <= out[2] is not TRUE #> Warning: Not enough data to perform fit for group 1; computing mean instead. #> #> $fraction #>"},{"path":"/reference/plot_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting single, data rich station — plot_one","title":"Plotting single, data rich station — plot_one","text":"Plotting single, data rich station","code":""},{"path":"/reference/plot_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting single, data rich station — plot_one","text":"","code":"plot_one(df, station)"},{"path":"/reference/plot_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting single, data rich station — plot_one","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character indicating station, selected figure.","code":""},{"path":"/reference/plot_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting single, data rich station — plot_one","text":"plotly object interactive figure.","code":""},{"path":"/reference/plot_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting single, data rich station — plot_one","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_one(cal_data$data, station = \"4\") #> Warning: The `keep` argument of `group_map()` is deprecated as of dplyr 1.0.0. #> Please use the `.keep` argument instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated. #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors {\"x\":{\"data\":[{\"x\":[\"2008-03-11 00:00:00.000000\",\"2008-03-25 00:00:00.000000\",\"2008-04-04 00:00:00.000000\",\"2008-04-15 00:00:00.000000\",\"2008-04-28 00:00:00.000000\",\"2008-05-12 00:00:00.000000\",\"2008-05-23 00:00:00.000000\",\"2008-06-04 00:00:00.000000\",\"2008-06-16 00:00:00.000000\",\"2008-07-01 00:00:00.000000\",\"2008-07-11 00:00:00.000000\",\"2008-07-15 00:00:00.000000\",\"2008-07-28 00:00:00.000000\",\"2008-08-08 00:00:00.000000\",\"2008-08-25 00:00:00.000000\",\"2008-09-10 00:00:00.000000\",\"2008-09-22 00:00:00.000000\",\"2008-10-06 00:00:00.000000\",\"2008-10-20 00:00:00.000000\",\"2008-11-03 00:00:00.000000\",\"2008-11-17 00:00:00.000000\",\"2008-12-01 00:00:00.000000\",\"2008-12-15 00:00:00.000000\",\"2009-01-21 00:00:00.000000\",\"2009-02-02 00:00:00.000000\",\"2009-02-16 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plot","code":""},{"path":"/reference/plot_PminTP.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Pmin fraction of PT — plot_PminTP","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_PminTP(cal_data$data, station = unique(cal_data$stations$ID))"},{"path":"/reference/plot_Pmin_PT.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Pmin fraction of PT — plot_Pmin_PT","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"Plot Pmin fraction PT","code":""},{"path":"/reference/plot_Pmin_PT.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"","code":"plot_Pmin_PT(df, station)"},{"path":"/reference/plot_Pmin_PT.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"df dataframe formatted data (Station, DATE, Variables Values columns needed) station character vector indicating station/s, selected figure.","code":""},{"path":"/reference/plot_Pmin_PT.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"ggplot object monthly Pmin/PT fraction box plot","code":""},{"path":"/reference/plot_Pmin_PT.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Pmin fraction of PT — plot_Pmin_PT","text":"","code":"temp_path <- system.file(\"templates\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) #> [1] \"Loading data from template.\" #> [1] \"Loading of data is finished.\" plot_Pmin_PT(cal_data$data, station = unique(station$ID)) #> Error in filter(., Station %in% station & Variables %in% c(\"P-PO4\", \"PT\")): Problem while computing `..1 = Station %in% station & Variables %in% #> c(\"P-PO4\", \"PT\")`. #> Caused by error in `unique()`: #> ! object 'station' not found"},{"path":"/reference/plot_ts_fig.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to prepare static time series figure for single station — plot_ts_fig","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"Function prepare static time series figure single station","code":""},{"path":"/reference/plot_ts_fig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"","code":"plot_ts_fig(station, df)"},{"path":"/reference/plot_ts_fig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"station character indicating station, selected figure. df dataframe formatted data (Station, DATE, Variables Values columns needed)","code":""},{"path":"/reference/plot_ts_fig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"ggplot object static figure.","code":""},{"path":"/reference/plot_ts_fig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to prepare static time series figure for single station — plot_ts_fig","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"calibration_data.xlsx\", package = \"svatools\") cal_data <- load_template(temp_path) plot_ts_fig(\"4\", cal_data$data) }"},{"path":"/reference/plot_weather.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare the plotly figure for weather data — plot_weather","title":"Prepare the plotly figure for weather data — plot_weather","text":"Prepare plotly figure weather data","code":""},{"path":"/reference/plot_weather.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare the plotly figure for weather data — plot_weather","text":"","code":"plot_weather(meteo_lst, par, period = \"day\", fn_summarize = \"mean\")"},{"path":"/reference/plot_weather.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare the plotly figure for weather data — plot_weather","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par character marking weather variable extract (.e. \"PCP\", \"SLR\", etc). period character describing, time interval display (default \"day\", examples \"week\", \"month\", etc). fn_summarize function recalculate time interval (default \"mean\", examples \"median\", \"sum\", etc).","code":""},{"path":"/reference/plot_weather.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare the plotly figure for weather data — plot_weather","text":"plotly figure object displayed weather data","code":""},{"path":"/reference/plot_weather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare the plotly figure for weather data — plot_weather","text":"","code":"temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 4326) #> [1] \"Loading data from template.\" 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plot_weather_compare","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"Plotting figure comparing two datasets weather data","code":""},{"path":"/reference/plot_weather_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"","code":"plot_weather_compare( meteo_lst1, meteo_lst2, par, period = \"day\", fn_summarize = \"mean\", name_set1 = \"dataset 1\", name_set2 = \"dataset 2\" )"},{"path":"/reference/plot_weather_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"meteo_lst1 first nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). meteo_lst2 second nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par character marking weather variable extract (.e. \"PCP\", \"SLR\", etc). period character describing, time interval display (default \"day\", examples \"week\", \"month\", etc). fn_summarize function recalculate time interval (default \"mean\", examples \"median\", \"sum\", etc). name_set1 character name first dataset. name_set2 character name second dataset.","code":""},{"path":"/reference/plot_weather_compare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"plotly figure object displayed weather data two datasets.","code":""},{"path":"/reference/plot_weather_compare.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting figure to comparing two datasets with weather data — plot_weather_compare","text":"","code":"temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst1 <- load_template(temp_path, 4326) #> [1] \"Loading data from template.\" #> [1] \"Reading station ID9 data.\" #> [1] \"Reading station ID3 data.\" #> [1] \"Reading station ID10 data.\" #> [1] \"Reading station ID11 data.\" #> [1] \"Reading station ID12 data.\" #> [1] \"Reading station ID13 data.\" #> [1] \"Reading station ID1 data.\" #> [1] \"Reading station ID8 data.\" #> [1] \"Reading station ID2 data.\" #> [1] \"Reading station ID4 data.\" #> [1] \"Reading station ID5 data.\" #> [1] \"Reading station ID6 data.\" #> [1] \"Reading station ID7 data.\" #> [1] \"Reading station ID14 data.\" #> [1] \"Loading of data is finished.\" temp_path <- system.file(\"extdata\", \"weather_data_raw.xlsx\", package = \"svatools\") met_lst2 <- load_template(temp_path, 4326) #> [1] \"Loading data from template.\" #> [1] \"Reading station ID9 data.\" #> Warning: RELHUM variable values at station ID9 should be between 0 and 1. Please check, correct your input data and reload template!!! #> Warning: PCP variable values at station ID9 should be between 0 and 1825 mm/d (maximum 1-day recorded rainfall). Please check, correct your input data and reload template!!! #> [1] \"Reading station ID3 data.\" #> Warning: RELHUM variable values at station ID3 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID10 data.\" #> Warning: RELHUM variable values at station ID10 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID11 data.\" #> [1] \"Reading station ID12 data.\" #> Warning: RELHUM variable values at station ID12 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID13 data.\" #> Warning: RELHUM variable values at station ID13 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID1 data.\" #> Warning: RELHUM variable values at station ID1 should be between 0 and 1. Please check, correct your input data and reload template!!! #> [1] \"Reading station ID8 data.\" #> Warning: RELHUM variable values at station ID8 should be between 0 and 1. Please check, correct your input data and reload template!!! #> Warning: WNDSPD variable values at station ID8 should be between 0 and 104 m/s (the highest recorded natural surface wind velocity). Please check, correct your input data and reload template!!! #> [1] \"Reading station ID2 data.\" #> [1] \"Reading station ID4 data.\" #> [1] \"Reading station ID5 data.\" #> [1] \"Reading station ID6 data.\" #> [1] \"Reading station ID7 data.\" #> [1] \"Loading of data is finished.\" plot_weather_compare(met_lst1, met_lst2, \"PCP\", \"month\", \"mean\", \"clean\", \"raw\") #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors #> Warning: n too large, allowed maximum for palette Set2 is 8 #> Returning the palette you asked for with that many colors {\"x\":{\"data\":[{\"x\":[\"2018-04-01 00:00:00.000000\",\"2018-05-01 00:00:00.000000\",\"2018-06-01 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the plotly figure for weather data — plot_weather_fig","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"Prepare plotly figure weather data","code":""},{"path":"/reference/plot_weather_fig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"","code":"plot_weather_fig(meteo_lst, par, period = \"day\", fn_summarize = \"mean\")"},{"path":"/reference/plot_weather_fig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par character marking weather variable extract (.e. \"PCP\", \"SLR\", etc). period character describing, time interval display (default \"day\", examples \"week\", \"month\", etc). fn_summarize function recalculate time interval (default \"mean\", examples \"median\", \"sum\", etc).","code":""},{"path":"/reference/plot_weather_fig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"plotly figure object displayed weather data","code":""},{"path":"/reference/plot_weather_fig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare the plotly figure for weather data — plot_weather_fig","text":"","code":"##get_weather_fig(meteo_lst, \"PCP\", \"month\", \"sum\")"},{"path":"/reference/plot_weather_fig_compare.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"Plotting figure comparing two datasets weather data","code":""},{"path":"/reference/plot_weather_fig_compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"","code":"plot_weather_fig_compare( meteo_lst1, meteo_lst2, par, period = \"day\", fn_summarize = \"mean\", name_set1 = \"raw\", name_set2 = \"clean\" )"},{"path":"/reference/plot_weather_fig_compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"meteo_lst1 first nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). meteo_lst2 second nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). par character marking weather variable extract (.e. \"PCP\", \"SLR\", etc). period character describing, time interval display (default \"day\", examples \"week\", \"month\", etc). fn_summarize function recalculate time interval (default \"mean\", examples \"median\", \"sum\", etc). name_set1 character name first dataset. name_set2 character name second dataset.","code":""},{"path":"/reference/plot_weather_fig_compare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"plotly figure object displayed weather data two datasets.","code":""},{"path":"/reference/plot_weather_fig_compare.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting figure to comparing two datasets with weather data — plot_weather_fig_compare","text":"","code":"##plot_weather_fig_compare(meteo_lst1, meteo_lst2, \"PCP\", \"month\", \"mean\", \"raw\", \"clean\")"},{"path":"/reference/prepare_wgn.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to generate wgn data for the model — prepare_wgn","title":"Function to generate wgn data for the model — prepare_wgn","text":"Function generate wgn data model","code":""},{"path":"/reference/prepare_wgn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to generate wgn data for the model — prepare_wgn","text":"","code":"prepare_wgn( meteo_lst, TMP_MAX = NULL, TMP_MIN = NULL, PCP = NULL, RELHUM = NULL, WNDSPD = NULL, MAXHHR = NULL, SLR = NULL )"},{"path":"/reference/prepare_wgn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to generate wgn data for the model — prepare_wgn","text":"meteo_lst meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). TMP_MAX dataframe two columns: DATE : POSIXct, TMP_MAX : num. Optional (default NULL). parameter refers data, used instead, TMP_MAX variable missing station. TMP_MIN dataframe two columns: DATE : POSIXct, TMP_MIN : num. Optional (default NULL). parameter refers data, used instead, TMP_MIN missing station. PCP dataframe two columns: DATE : POSIXct, PCP : num. Optional (default NULL). parameter refers data, used instead, PCP missing station. RELHUM dataframe two columns: DATE : POSIXct, RELHUM : num. Optional (default NULL). parameter refers data, used instead, RELHUM missing station. WNDSPD dataframe two columns: DATE : POSIXct, WNDSPD : num. Optional (default NULL). parameter refers data, used instead, WNDSPD missing station. MAXHHR dataframe two columns: DATE : POSIXct, MAXHHR : num. Optional (default NULL). parameter refers data, used instead, MAXHHR missing station. SLR dataframe two columns: DATE : POSIXct, SLR : num. Optional (default NULL). parameter refers data, used instead, SLR missing station.","code":""},{"path":"/reference/prepare_wgn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to generate wgn data for the model — prepare_wgn","text":"list two dataframes: wgn_st - wgn station data, wgn_data - wgn data","code":""},{"path":"/reference/prepare_wgn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to generate wgn data for the model — prepare_wgn","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) TMP_MAX <- met_lst$data$ID10$TMP_MAX TMP_MIN <- met_lst$data$ID10$TMP_MIN PCP <- met_lst$data$ID9$PCP RELHUM = met_lst$data$ID9$RELHUM WNDSPD <- met_lst$data$ID12$WNDSPD MAXHHR <- met_lst$data$ID11$MAXHHR SLR <- met_lst$data$ID9$SLR ##Does the thing wgn <- prepare_wgn(met_lst, TMP_MAX, TMP_MIN, PCP, RELHUM, WNDSPD, MAXHHR, SLR) }"},{"path":"/reference/rch.html","id":null,"dir":"Reference","previous_headings":"","what":"River data — rch","title":"River data — rch","text":"Example GIS river dataset Polish case study","code":""},{"path":"/reference/rch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"River data — rch","text":"","code":"rch"},{"path":"/reference/rch.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"River data — rch","text":"sf data.frame 2 variables: id id reach. geometry Geometry objects.","code":""},{"path":"/reference/shp.html","id":null,"dir":"Reference","previous_headings":"","what":"Basin boundary data — shp","title":"Basin boundary data — shp","text":"Example basin GIS dataset Polish case study","code":""},{"path":"/reference/shp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basin boundary data — shp","text":"","code":"shp"},{"path":"/reference/shp.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Basin boundary data — shp","text":"data.frame 2 variables: name Name catchment. geometry Geometry objects.","code":""},{"path":"/reference/sp.na.omit.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to remove NAs in sp dataframe — sp.na.omit","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"Function remove NAs sp dataframe","code":""},{"path":"/reference/sp.na.omit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"","code":"sp.na.omit(x, margin = 1)"},{"path":"/reference/sp.na.omit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"x sp dataframe margin numeric. Optional, default 1 remove rows, 2 removing columns.","code":""},{"path":"/reference/sp.na.omit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"sp dataframe without NAs data","code":""},{"path":"/reference/sp.na.omit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to remove NAs in sp dataframe — sp.na.omit","text":"","code":"if (FALSE) { sp_df <- sp.na.omit(sp_df) }"},{"path":"/reference/transform_to_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Transforming list of sp dataframes to list of list — transform_to_list","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"Transforming list sp dataframes list list","code":""},{"path":"/reference/transform_to_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"","code":"transform_to_list(list_sp, start_date, end_date)"},{"path":"/reference/transform_to_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"list_sp list SpatialPointsDataFrames (weather variable) prepared interpolate function. start_date time series starting date string. Example \"1998-01-01\". end_date time series ending date string. Example \"2021-12-31\".","code":""},{"path":"/reference/transform_to_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat).","code":""},{"path":"/reference/transform_to_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transforming list of sp dataframes to list of list — transform_to_list","text":"","code":"if (FALSE) { ###First step to get interpolation results temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") DEM_path <- system.file(\"extdata\", \"GIS/DEM.tif\", package = \"svatools\") basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) result <- interpolate(met_lst, \"./output/\", basin_path, DEM_path, 2000) ###Second step converting interpolation results to list of list format. start_date <- svatools:::get_dates(met_lst)$min_date end_date <- svatools:::get_dates(met_lst)$max_date int_met_lst <- transform_to_list(result, start_date, end_date) }"},{"path":"/reference/update_wst_id.html","id":null,"dir":"Reference","previous_headings":"","what":"Updating sqlite database table with wst_id information — update_wst_id","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"Updating sqlite database table wst_id information","code":""},{"path":"/reference/update_wst_id.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"","code":"update_wst_id(tname, db_path, wst_cli)"},{"path":"/reference/update_wst_id.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"tname character table name (example \"hru_con\") db_path character sqlite database (example \"./output/project.sqlite\") wst_cli data.frame weather_sta_cli table","code":""},{"path":"/reference/update_wst_id.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"updates sql database table amended wst_id (id closest weather station)","code":""},{"path":"/reference/update_wst_id.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Updating sqlite database table with wst_id information — update_wst_id","text":"","code":"if (FALSE) { update_wst_id(\"hru_con\", \"./output/project.sqlite\", weather_sta_cli) }"},{"path":"/reference/write_atmo_cli.html","id":null,"dir":"Reference","previous_headings":"","what":"Write 'atmo.cli' file — write_atmo_cli","title":"Write 'atmo.cli' file — write_atmo_cli","text":"Write 'atmo.cli' file","code":""},{"path":"/reference/write_atmo_cli.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write 'atmo.cli' file — write_atmo_cli","text":"","code":"write_atmo_cli(df, write_path, t_ext = \"year\")"},{"path":"/reference/write_atmo_cli.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write 'atmo.cli' file — write_atmo_cli","text":"df dafaframe \"DATE\", \"NH4_RF\", \"NO3_RF\" , \"NH4_DRY\" \"NO3_DRY\" columns obtained get_atmo_dep) function. write_path path folder results written. t_ext string, prepare file: 'year' yearly averages, 'month' - monthly averages 'annual' average period. Optional (default - \"year\"). df monthly data, 'month' used.","code":""},{"path":"/reference/write_atmo_cli.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write 'atmo.cli' file — write_atmo_cli","text":"'atmo.cli' file one station catchment.","code":""},{"path":"/reference/write_atmo_cli.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write 'atmo.cli' file — write_atmo_cli","text":"","code":"if (FALSE) { basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") df <- get_atmo_dep(basin_path) write_atmo_cli(df, \"./output/\") }"},{"path":"/reference/write_input_files.html","id":null,"dir":"Reference","previous_headings":"","what":"Writing input files (all except TMP) — write_input_files","title":"Writing input files (all except TMP) — write_input_files","text":"Writing input files (except TMP)","code":""},{"path":"/reference/write_input_files.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Writing input files (all except TMP) — write_input_files","text":"","code":"write_input_files(write_path, sp_df, meteo_lst, par)"},{"path":"/reference/write_input_files.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Writing input files (all except TMP) — write_input_files","text":"write_path path folder results written. sp_df SpatialPointsDataFrame resulting interpolated data. meteo_lst nested list lists dataframes. par weather variable (.e. \"PCP\", \"SLR\", etc).","code":""},{"path":"/reference/write_input_files.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Writing input files (all except TMP) — write_input_files","text":"weather data text files virtual stations (created interpolation) format usable SWAT model.","code":""},{"path":"/reference/write_input_files.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Writing input files (all except TMP) — write_input_files","text":"","code":"if (FALSE) { write_input_files(\"./output/\", sp_df, \"PCP\") }"},{"path":"/reference/write_input_files_tmp.html","id":null,"dir":"Reference","previous_headings":"","what":"Writing input files for TMP data — write_input_files_tmp","title":"Writing input files for TMP data — write_input_files_tmp","text":"Writing input files TMP data","code":""},{"path":"/reference/write_input_files_tmp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Writing input files for TMP data — write_input_files_tmp","text":"","code":"write_input_files_tmp(write_path, sp_df_mx, sp_df_mn, meteo_lst)"},{"path":"/reference/write_input_files_tmp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Writing input files for TMP data — write_input_files_tmp","text":"write_path path folder results written. sp_df_mx SpatialPointsDataFrame resulting interpolated data TMP_MAX variable. sp_df_mn SpatialPointsDataFrame resulting interpolated data TMP_MIN variable. meteo_lst nested list lists dataframes.","code":""},{"path":"/reference/write_input_files_tmp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Writing input files for TMP data — write_input_files_tmp","text":"temperature weather data text files virtual stations (created interpolation) format usable SWAT model.","code":""},{"path":"/reference/write_input_files_tmp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Writing input files for TMP data — write_input_files_tmp","text":"","code":"if (FALSE) { write_input_files_tmp(\"./output/\", sp_df_mx, sp_df_mn) }"},{"path":"/reference/write_ref_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Writing reference file for weather data — write_ref_file","title":"Writing reference file for weather data — write_ref_file","text":"Writing reference file weather data","code":""},{"path":"/reference/write_ref_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Writing reference file for weather data — write_ref_file","text":"","code":"write_ref_file(write_path, sp_df, par)"},{"path":"/reference/write_ref_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Writing reference file for weather data — write_ref_file","text":"write_path path folder results written. sp_df SpatialPointsDataFrame resulting interpolated data. par weather variable (.e. \"PCP\", \"SLR\", etc).","code":""},{"path":"/reference/write_ref_file.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Writing reference file for weather data — write_ref_file","text":"weather virtual stations (created interpolation) reference file format usable SWAT model.","code":""},{"path":"/reference/write_ref_file.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Writing reference file for weather data — write_ref_file","text":"","code":"if (FALSE) { write_ref_file(\"./output/\", sp_df, \"PCP\") }"},{"path":"/reference/write_wgnmaker_files.html","id":null,"dir":"Reference","previous_headings":"","what":"Preparing input files for WGNmaker. — write_wgnmaker_files","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"WGNmaker excel macro downloaded following link: https://swat.tamu.edu/media/41583/wgen-excel.zip","code":""},{"path":"/reference/write_wgnmaker_files.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"","code":"write_wgnmaker_files(write_path, meteo_lst, wgn_stations_lst, station_name)"},{"path":"/reference/write_wgnmaker_files.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"write_path path folder results written. meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). wgn_stations_lst List selected stations parameter used data preparation. Example list(PCP = \"ID12\", SLR = \"ID11\", RELHUM = \"ID12\", TMP_MAX = \"ID12\", TMP_MIN = \"ID12\", WNDSPD = \"ID12\", MAXHHR = \"ID12\") station_name string name station.","code":""},{"path":"/reference/write_wgnmaker_files.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"Excel files needed WGNmaker","code":""},{"path":"/reference/write_wgnmaker_files.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preparing input files for WGNmaker. — write_wgnmaker_files","text":"","code":"if (FALSE) { write_wgnmaker_files(\"./wgn/\", meteo_lst, wgn_stations_lst, \"Station1\") }"},{"path":"/reference/add_missing_pcp_zero.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace empty PCP entry with 0 — add_missing_pcp_zero","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"Replace empty PCP entry 0","code":""},{"path":"/reference/add_missing_pcp_zero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"","code":"add_missing_pcp_zero(df_to_correct, df_valid = NULL, value_to_zero = 0.2)"},{"path":"/reference/add_missing_pcp_zero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"df_to_correct dataframe correct PCP variable \"DATE\" \"PCP\" columns. df_valid (optional) dataframe valid data PCP variable \"DATE\" \"PCP\" columns. Also overlap df_to_correct data. value_to_zero (optional) numeric value daily PCP. NA values can set 0 df_valid PCP values equal value_to_zero (default 0.2).","code":""},{"path":"/reference/add_missing_pcp_zero.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"updated dataframe","code":""},{"path":"/reference/add_missing_pcp_zero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Replace empty PCP entry with 0 — add_missing_pcp_zero","text":"","code":"if (FALSE) { met_lst$data$ID8$PCP <- add_missing_pcp_zero(met_lst$data$ID8$PCP, met_lst$data$ID9$PCP) }"},{"path":"/reference/write_weather_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Write weather meteo_lst into template .xlsx file — write_weather_template","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":"Write weather meteo_lst template .xlsx file","code":""},{"path":"/reference/write_weather_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":"","code":"write_weather_template( meteo_lst, write_path = \"\", f_name = \"weather_data.xlsx\" )"},{"path":"/reference/write_weather_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":"meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). write_path (optional) character path folder results written (default \"\"). f_name (optional) character name file (default 'weather_data.xlsx').","code":""},{"path":"/reference/write_weather_template.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":".xlsx file written template format, de read load_template function.","code":""},{"path":"/reference/write_weather_template.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write weather meteo_lst into template .xlsx file — write_weather_template","text":"","code":"if (FALSE) { write_weather_template(met_lst) }"},{"path":"/reference/my.pcpmhhr.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to calculate hhr parameter — my.pcpmhhr","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"Function calculate hhr parameter","code":""},{"path":"/reference/my.pcpmhhr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"","code":"my.pcpmhhr(x, na.rm = FALSE)"},{"path":"/reference/my.pcpmhhr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"x vector numbers na.rm logical, NA cleaned","code":""},{"path":"/reference/my.pcpmhhr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"numeric result","code":""},{"path":"/reference/my.pcpmhhr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to calculate hhr parameter — my.pcpmhhr","text":"","code":"if (FALSE) { my.pcpmhhr(x) }"},{"path":"/reference/fill_with_closest.html","id":null,"dir":"Reference","previous_headings":"","what":"Filling missing variables from the closest stations, which has data. — fill_with_closest","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"Filling missing variables closest stations, data.","code":""},{"path":"/reference/fill_with_closest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"","code":"fill_with_closest( meteo_lst, par_fill = c(\"TMP_MAX\", \"TMP_MIN\", \"PCP\", \"RELHUM\", \"WNDSPD\", \"SLR\") )"},{"path":"/reference/fill_with_closest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"meteo_lst meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). par_fill vector variables filled. Optional (default c(\"TMP_MAX\", \"TMP_MIN\",\"PCP\", \"RELHUM\", \"WNDSPD\", \"SLR\")).","code":""},{"path":"/reference/fill_with_closest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"list dataframe filled data. Returned list meteo_lst$data.","code":""},{"path":"/reference/fill_with_closest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filling missing variables from the closest stations, which has data. — fill_with_closest","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 3035) met_lst$data <- fill_with_closest(met_lst, c(\"TMP_MAX\", \"TMP_MIN\")) }"},{"path":"/reference/usersoil_to_sol.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert usersoil.csv to soils.sol — usersoil_to_sol","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"Convert usersoil.csv soils.sol","code":""},{"path":"/reference/usersoil_to_sol.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"","code":"usersoil_to_sol(csv_path, db_path = NULL)"},{"path":"/reference/usersoil_to_sol.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"csv_path character path csv file (example \"usersoil_lrew.csv\"\") db_path character sqlite project database (example \"output/project.sqlite\"). Optional, default NULL. used reduce soils.sol file, less soil types sqlite database usersoil csv file.","code":""},{"path":"/reference/usersoil_to_sol.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"soils.sol SWAT+ model input file","code":""},{"path":"/reference/usersoil_to_sol.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert usersoil.csv to soils.sol — usersoil_to_sol","text":"","code":"if (FALSE) { usersoil_to_sol(\"output/usersoil_lrew.csv\") ##Or usersoil_to_sol(\"output/usersoil_lrew.csv\", \"output/project.sqlite\") }"},{"path":"/reference/get_usersoil_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to prepare user soil table for SWAT model — get_usersoil_table","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"Function prepare user soil table SWAT model","code":""},{"path":"/reference/get_usersoil_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"","code":"get_usersoil_table(csv_path, hsg = FALSE, keep_values = FALSE, nb_lyr = NA)"},{"path":"/reference/get_usersoil_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"csv_path character path csv file (example \"usersoil_lrew.csv\"). File comma separated minimum columns SNAM, NLAYERS SOL_Z, CLAY, SILT, SAND, SOL_CBN available soil layer (minimum 1). hsg Boolean, TRUE - prepare soil hydrological groups, FALSE - soil hydrological group preparation done. Optional (Default = FALSE). hsg = TRUE three additional columns input table: Impervious (allowed values \"<50cm\", \"50-100cm\", \">100cm\"), Depth (allowed values \"<60cm\", \"60-100cm\", \">100cm\") Drained (allowed values \"Y\" drained areas, \"N\" areas without working tile drains). information can found SWAT+ modeling protocol Table 3.3. keep_values Boolean character vector, TRUE - keep old values (new values left 0 NA values present input table), FALSE - keep new values. Character vector also used keep specified columns. instance, c(\"HYDGRP\", \"ROCK1\") keep values soil hydro groups rock data first layer, c(\"HYDGRP\", \"ROCK\") keep values rock data columns. Optional (Default = FALSE). nb_lyr integer, number layers resulting user soil data contain. Optional (Default = NA, stands number input data).","code":""},{"path":"/reference/get_usersoil_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"dataframe fully formatted filled table soil parameters SWAT model.","code":""},{"path":"/reference/get_usersoil_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to prepare user soil table for SWAT model — get_usersoil_table","text":"","code":"if (FALSE) { usersoils <- get_usersoil_table(\"table.csv\") write.csv(usersoils, \"usersoils.csv\", row.names=FALSE, quote=FALSE) }"},{"path":"/reference/plot_wgn_comparison.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot wgn parameters comparison — plot_wgn_comparison","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"Plot wgn parameters comparison","code":""},{"path":"/reference/plot_wgn_comparison.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"","code":"plot_wgn_comparison( meteo_lst1, meteo_lst2, station1, station2, type1 = \"set 1\", type2 = \"set 2\", title = \"comparison\" )"},{"path":"/reference/plot_wgn_comparison.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"meteo_lst1 first nested list lists dataframes. meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). meteo_lst2 second nested list lists dataframes. meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). station1 character, id one station first list selected comparison (example \"ID1\"). station2 character, id one station second list selected comparison (example \"ID1\"). type1 character, naming first dataset (example \"measured\"). type2 character, naming second dataset (example \"netCDF\"). title character, information added figure title","code":""},{"path":"/reference/plot_wgn_comparison.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"ggplot figure bar plots","code":""},{"path":"/reference/plot_wgn_comparison.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot wgn parameters comparison — plot_wgn_comparison","text":"","code":"if (FALSE) { temp_path <- system.file(\"extdata\", \"weather_data.xlsx\", package = \"svatools\") met_lst <- load_template(temp_path, 4326) plot_wgn_comparison(met_lst, met_lst, \"ID9\", \"ID2\", \"Samszyce\", \"Glebokie\", \"comparison\") }"},{"path":"/reference/load_climate_lst.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract climate data into nested list of lists — load_climate_lst","title":"Extract climate data into nested list of lists — load_climate_lst","text":"Extract climate data nested list lists","code":""},{"path":"/reference/load_climate_lst.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract climate data into nested list of lists — load_climate_lst","text":"","code":"load_climate_lst(dir_path, location)"},{"path":"/reference/load_climate_lst.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract climate data into nested list of lists — load_climate_lst","text":"dir_path character, path CORDEX-BC folder (example climate/CORDEX-BC\"). location character list. case character, path provided catchment boundary file (example GIS/basin.shp). case list, nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). List list obtained using load_template() prepared excel template.","code":""},{"path":"/reference/load_climate_lst.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract climate data into nested list of lists — load_climate_lst","text":"Nested lists lists. First nesting level RCP, second RCM model numbers, rest meteo_lst. part used package functions (example plot_weather(result$rcp26$1), \"PCP\", \"month\", \"sum\").","code":""},{"path":"/reference/load_climate_lst.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract climate data into nested list of lists — load_climate_lst","text":"","code":"if (FALSE) { basin_path <- system.file(\"extdata\", \"GIS/basin.shp\", package = \"svatools\") data_path <- \"climate/CORDEX-BC\" result <- load_climate_lst(data_path, basin_path) }"},{"path":"/reference/update_wst_txt.html","id":null,"dir":"Reference","previous_headings":"","what":"Updating wst_id information in model text files — update_wst_txt","title":"Updating wst_id information in model text files — update_wst_txt","text":"Updating wst_id information model text files","code":""},{"path":"/reference/update_wst_txt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Updating wst_id information in model text files — update_wst_txt","text":"","code":"update_wst_txt(fname, write_path, wst_sf, spacing, folder_to_save = \"temp\")"},{"path":"/reference/update_wst_txt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Updating wst_id information in model text files — update_wst_txt","text":"fname character, model file name (example 'aquifer.con'). write_path character, path SWAT+ txtinout folder (example \"my_model\"). wst_sf sf dataframe weather station information. contain \"name\" \"geometry\" columns. spacing character, contain information spacing columns file. Example c('%8s', '%-12s', rep('%12s', 5), '%8s', '%16s', rep('%8s', 4)). folder_to_save character, folder save function results. Optional, default = \"temp\".","code":""},{"path":"/reference/update_wst_txt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Updating wst_id information in model text files — update_wst_txt","text":"Updated text file, updated station names (nearest stations features).","code":""},{"path":"/reference/update_wst_txt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Updating wst_id information in model text files — update_wst_txt","text":"","code":"if (FALSE) { spacing <- c('%8s', '%-12s', rep('%12s', 5), '%8s', '%16s', rep('%8s', 4), '%12s', '%8s', rep('%12s', 2)) update_wst_txt(\"reservoir.con\", \"model_folder\", wst_sf, spacing) }"},{"path":"/reference/read_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Loading SWAT+ text files into dataframe — read_tbl","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"Loading SWAT+ text files dataframe","code":""},{"path":"/reference/read_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"","code":"read_tbl(tbl_name, proj_path, row_data_start = 3, row_col_names = 2)"},{"path":"/reference/read_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"tbl_name character, name file read eaxmple ('rout_unit.con'). proj_path character, path SWAT+ txtinout folder (example \"my_model\"). row_data_start numeric, row number data written. Optional, default = 3. row_col_names numeric, row nu,mber column names written. Optional, default = 2.","code":""},{"path":"/reference/read_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"dataframe information text file","code":""},{"path":"/reference/read_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Loading SWAT+ text files into dataframe — read_tbl","text":"","code":"if (FALSE) { df <- read_tbl('rout_unit.con', 'model_folder', 3, 2) }"},{"path":"/reference/prepare_climate.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"Function prepare update climate data text input files SWAT+ model","code":""},{"path":"/reference/prepare_climate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"","code":"prepare_climate(meteo_lst, write_path, period_starts = NA, period_ends = NA)"},{"path":"/reference/prepare_climate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"meteo_lst meteo_lst nested list lists dataframes. Nested structure meteo_lst -> data -> Station ID -> Parameter -> Dataframe (DATE, PARAMETER). Nested meteo_lst -> stations Dataframe (ID, Name, Elevation, Source, geometry, Long, Lat). write_path character, path SWAT+ txtinout folder (example \"my_model\"). period_starts character, date string (example '1991-01-01'). Optional (default = NA, stands available data). period_ends character, date string (example '2020-12-31'). Optional (default = NA, stands available data).","code":""},{"path":"/reference/prepare_climate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"Fill update multiple weather related weather text files.","code":""},{"path":"/reference/prepare_climate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Function to prepare or update climate data text input files in SWAT+ model — prepare_climate","text":"","code":"if (FALSE) { prepare_climate(meteo_lst, \"output\", \"1991-01-01\", \"2020-12-31\") }"},{"path":"/reference/df_to_txt.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform dataframe into text and writes into existing file — df_to_txt","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"Transform dataframe text writes existing file","code":""},{"path":"/reference/df_to_txt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"","code":"df_to_txt(write_path, fname, df, spacing)"},{"path":"/reference/df_to_txt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"write_path character, path SWAT+ txtinout folder (example \"my_model\"). fname character, file name. df dataframe spacing character, example spacing <- c('%-30s', rep('%-13s', 2), '%-15s', '%-3s')","code":""},{"path":"/reference/df_to_txt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"updated text file","code":""},{"path":"/reference/df_to_txt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform dataframe into text and writes into existing file — df_to_txt","text":"","code":"if (FALSE) { spacing <- c(rep('%10s', 5)) df_to_txt(\"output\", \"time.sim\", df, spacing) }"},{"path":"/news/index.html","id":"svatools-010","dir":"Changelog","previous_headings":"","what":"svatools 0.1.0","title":"svatools 0.1.0","text":"new prepare_climate() function added.","code":""},{"path":"/news/index.html","id":"svatools-009","dir":"Changelog","previous_headings":"","what":"svatools 0.0.9","title":"svatools 0.0.9","text":"New functions get_usersoil_table(), load_climate_lst() plot_wgn_comparison() added.","code":""},{"path":"/news/index.html","id":"svatools-008","dir":"Changelog","previous_headings":"","what":"svatools 0.0.8","title":"svatools 0.0.8","text":"bugs fixed get_soil_parameters() get_atmo_dep().","code":""},{"path":"/news/index.html","id":"svatools-007","dir":"Changelog","previous_headings":"","what":"svatools 0.0.7","title":"svatools 0.0.7","text":"Fixed SOL_BD calculation get_soil_parameters(). Added function usersoil_to_sol().","code":""},{"path":"/news/index.html","id":"svatools-006","dir":"Changelog","previous_headings":"","what":"svatools 0.0.6","title":"svatools 0.0.6","text":"Updated functions interpolate(), prepare_wgn(), add_weather() load_template() deal different data. Added new function fill_with_closest(). Bug fixes.","code":""},{"path":"/news/index.html","id":"svatools-005","dir":"Changelog","previous_headings":"","what":"svatools 0.0.5","title":"svatools 0.0.5","text":"Different bugs various functions corrected. Plotting functions updated. Added function add_missing_pcp_zero().","code":""},{"path":"/news/index.html","id":"svatools-004","dir":"Changelog","previous_headings":"","what":"svatools 0.0.4","title":"svatools 0.0.4","text":"Land use data functions added get_lu_points() extract_rotation() well Land use article page explain use .","code":""},{"path":"/news/index.html","id":"svatools-003","dir":"Changelog","previous_headings":"","what":"svatools 0.0.3","title":"svatools 0.0.3","text":"Atmospheric deposition data functions added get_atmo_dep(), write_atmo_cli() add_atmo_dep() well Atmospheric deposition article page explain use .","code":""},{"path":"/news/index.html","id":"svatools-002","dir":"Changelog","previous_headings":"","what":"svatools 0.0.2","title":"svatools 0.0.2","text":"add_weather(), clean_wq(), clean_outliers() get_hsg() functions added.","code":""},{"path":"/news/index.html","id":"svatools-001","dir":"Changelog","previous_headings":"","what":"svatools 0.0.1","title":"svatools 0.0.1","text":"first release package svatools.","code":""}] diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 8c5d852..a33435c 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -33,6 +33,9 @@ /articles/weather.html + + /articles/climate.html + /news/index.html diff --git a/vignettes/climate.Rmd b/vignettes/climate.Rmd index d00f091..7e41aeb 100644 --- a/vignettes/climate.Rmd +++ b/vignettes/climate.Rmd @@ -12,18 +12,15 @@ knitr::opts_chunk$set(echo = TRUE) ## Introduction -Climate data in the [OPTAIN project](https://www.optain.eu/) for each -case study site could be obtained from -[UFZ-Cloud](https://nc.ufz.de/s/KA9Cr2bbtALGMHr/authenticate/showShare). -It is located under *WPs&Tasks\>WP3\>Task_3\_2\>data* path. Numbers are -referring to case study number and *gridded.zip* files contains `NetCDF` -files with climate data for the case study basin. Data are provided for -precipitation, solar radiation, relative humidity, min, max, mean -temperature and wind speed variables. Time period covered is from -1981-01-01 the end of this century with three representative -concentration pathways (RCPs) available (RCP2.6, RCP4.5, RCP8.5) and six -regional climate models (RCMs). Detained information about RCMs is -provided in Table \@ref(tab:rcms). +Climate data in the [OPTAIN project](https://www.optain.eu/) for each case study site could be obtained from +[UFZ-Cloud](https://nc.ufz.de/s/KA9Cr2bbtALGMHr/authenticate/showShare). It is located under *WPs&Tasks\>WP3\>Task_3\_2\>data* path. + + +```{r fig-cloud, echo=FALSE, out.width='100%', out.height ='100%', fig.align = 'center'} +knitr::include_graphics('fig_cloud.png') +``` + +Numbers are referring to a case study number and *gridded.zip* files contains `NetCDF` files with climate data for each case study basin. Data are provided for precipitation, solar radiation, relative humidity, min, max, mean temperature and wind speed variables. Time period covered is starting from 1981-01-01 while ending 2099 or 2100. Three representative concentration pathways (RCPs) available (RCP2.6, RCP4.5, RCP8.5) and six regional climate models (RCMs). Detained information about RCMs is provided in the table below. | Model number | Driving Model (GCM) | Ensemble | RCM | End date | |:------------:|:-------------------:|:--------:|:----------:|:----------:| @@ -34,31 +31,16 @@ provided in Table \@ref(tab:rcms). | 5 | HadGEM2-ES | r1i1p1 | RCA4 | 30.12.2099 | | 6 | MPI-ESM-LR | r2i1p1 | REMO2009 | 31.12.2100 | -: (#tab:rcms) RCMs models provided in OPTAIN. - -```{r fig-cloud, echo=FALSE, out.width='100%', out.height ='100%', fig.align = 'center'} -knitr::include_graphics('cloud.png') -``` -All RCMs data were bias-corrected to the -[ERA5-Land](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview) -climate data. Description of data preparation and analysis is provided -in the project deliverable [D3.1: Climate scenarios for integrated -modelling](https://www.optain.eu/sites/default/files/delivrables/OPTAIN%20D3.1%20-%20Climate%20scenarios%20for%20integrated%20modelling.pdf). +All RCMs data were bias-corrected to the [ERA5-Land](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview) climate data. Description of data preparation and analysis of they is provided in the project deliverable [D3.1: Climate scenarios for integrated modelling](https://www.optain.eu/sites/default/files/delivrables/OPTAIN%20D3.1%20-%20Climate%20scenarios%20for%20integrated%20modelling.pdf). ## NetCDF to SWAT+ input -There is no direct way of using `NetCDF` data in the SWAT model. -Therefore tools have been prepared for the users and provided with this -package. +There is no direct way of using `NetCDF` data in the SWAT model. Therefore tools have been prepared for the users and provided included this package. -### Extraction from NetCDF +### Extracting from NetCDF -First step on a way to prepare SWAT+ climate input data is to download -*XX-gridded.zip* file for your case study site and to unzip it. Then -locate *CORDEX-BC* folder. Data extraction from `NetCDF` is done with -[load_climate_lst](../reference/load_climate_lst.html) function, which -needs path to your basin shape file and path to *CORDEX-BC* folder. +First step to prepare SWAT+ climate input data is to download *XX-gridded.zip* file for your case study site and to unzip it. Then locate *CORDEX-BC* folder. Data extraction from `NetCDF` is done with [load_climate_lst](../reference/load_climate_lst.html) function, which only needs path to your basin shape file and path to *CORDEX-BC* folder. Following lines provides an example how to use it. ```{r ext_cli, eval = FALSE, message = FALSE, warning = FALSE} library(svatools) @@ -67,30 +49,24 @@ cordex_path <- "climate/CORDEX-BC" climate_lst <- load_climate_lst(cordex_path, basin_path) ``` -This function creates virtual stations for each `NetCDF` grid cell, -which basin boundary touches. Virtual stations are defined and center of -grid cell. Then for each of this station data are extracted and saved in -list of list format. +The function creates virtual stations for each `NetCDF` grid cell, which basin boundary touches. Virtual stations coordinates are defined as center of grid cell. Then for each of this virtual station data are extracted and saved in the list of list format. ```{r fig-basin, echo=FALSE, out.width='100%', out.height ='100%', fig.align = 'center'} knitr::include_graphics('fig_basin.png') ``` -### Evaluation +### Evaluating climate data Extraction result is arranged in specific format. *RCP -\> RCM* are the top levels of the list format. Then format is same as with other meteorological data used in this package. - Meteo data list - - stations sf Data frame (ID, Name, Elevation, Source, geometry, + - *stations* sf Data frame (ID, Name, Elevation, Source, geometry, Long, Lat) - - data -\> Station ID -\> Variable -\> Data frame (DATE, Variable) + - *data* -\> Station ID -\> Variable -\> Data frame (DATE, Variable) -Therefore other package function as -[plot_weather](../reference/plot_weather.html) could be used to examine -function results or to compare to measurement data for example with -[plot_weather_compare](../reference/plot_weather_compare.html). +Therefore other package function as [plot_weather](../reference/plot_weather.html) could be used to examine function results or to compare to measurement data, for instance with [plot_weather_compare](../reference/plot_weather_compare.html). ```{r plot_cli, eval = FALSE, message = FALSE, warning = FALSE} plot_weather(climate_lst[["rcp85"]][["1"]],"TMP_MAX", "year", "max") @@ -99,11 +75,13 @@ plot_weather(climate_lst[["rcp85"]][["1"]],"TMP_MAX", "year", "max") ```{r fig-temp, echo=FALSE, out.width='100%', out.height ='100%', fig.align = 'center'} knitr::include_graphics('fig_temp.png') ``` -[plot_wgn_comparison](../reference/plot_wgn_comparison.html) function might be useful to get climate set statistical comparison to motoring data. +[plot_wgn_comparison](../reference/plot_wgn_comparison.html) function might be useful to get climate set statistical comparison with motoring data. ```{r plot_wng, eval = FALSE, message = FALSE, warning = FALSE} -temp_path <- system.file("extdata", "weather_data.xlsx", package = "svatools") +##Loading data from the template with load_template function +temp_path <- "my_folder/weather_data.xlsx" met_lst <- load_template(temp_path, 4326) +##And plotting comparison of statistical values with climate data plot_wgn_comparison(climate_lst[["rcp85"]][["1"]], met_lst, "ID4", "ID2", "Climate set", "Monitoring data", "comparison") ``` @@ -113,19 +91,19 @@ knitr::include_graphics('fig_wng.png') Other functions from [Weather data section](../articles/weather.html) are also applicable. This allows to update SWAT+ *.sqlite* [database](https://biopsichas.github.io/svatools/articles/weather.html#wgn-input-function) with climate data, prepare [weather generator](https://biopsichas.github.io/svatools/articles/weather.html#wgn-input-function) parameters, etc. -### Preparing all climate sets +### Preparing SWAT+ input climate sets -Nevertheless [prepare_climate](../reference/prepare_climate.html) function could be used to directly prepare/update model input text files needed to run each climate scenario. Nothing else is needed just to overwrite model input files with prepared text files. +Nevertheless [prepare_climate](../reference/prepare_climate.html) function could be used to directly prepare/update model input text files needed to run each climate scenario. Nothing else is needed just to replace model input files with prepared set of files. -Following lines runs [prepare_climate](../reference/prepare_climate.html) function in a loop and prepares all climate data input sets and folder stucture. +Following lines runs [prepare_climate](../reference/prepare_climate.html) function in several loops and prepares all climate data input sets and folder structure. ```{r get_cli, eval = FALSE, message = FALSE, warning = FALSE} ##Path to your SWAT+ model project directory project_dir <- "../output/overwrite/tmp" -##These files will be copied from model directory and updated +##These files will be copied from model directory and updated with prepare_climate function files_to_copy <- paste(project_dir, c("aquifer.con", "chandeg.con", "hru.con", "reservoir.con", "rout_unit.con", "time.sim"), sep="/") -##Main loop to each rcp +##Main loop for each rcp for (rcp in c("rcp26", "rcp45", "rcp85")){ ##Each rcm for (rcm in as.character(c(1:6))){ diff --git a/vignettes/fig_cloud.png b/vignettes/fig_cloud.png new file mode 100644 index 0000000..67c61d9 Binary files /dev/null and b/vignettes/fig_cloud.png differ diff --git a/vignettes/fig_wng.png b/vignettes/fig_wng.png index 8a11fed..3aa46f1 100644 Binary files a/vignettes/fig_wng.png and b/vignettes/fig_wng.png differ