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Set 2 MakeFinalTables.Rmd
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Set 2 MakeFinalTables.Rmd
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---
title: "Make WetPath Tables"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
theme: yeti
highlighted: default
toc: yes
toc_float: true
---
### Set up
Here are details on metrics
Runoff frequency:
US2yr2hha_pptMinusSWD_mmB.tif
0 = least likely to flood; # 4 = most likely to flood]
0: -1600 to -1200 (mm)
1: -1200 to -852 mm
2: -852 to -505 mm
3: -505 to -157 mm
4: -157 to 200 mm
Flow class (may be misnamed table):
Need cutoff values for FlowClass table (from BdrckPerm_4_classes_Alb83.tif)
11=Shallow
12=Overland
21=Shallow Deep
22=Overland
For Aquifer Conductivity details, see: L:\Priv\CORFiles\Geospatial_Library\Data\Project\WetlandConnectivity\SpatialDataInputs\Gleason\Hydraulic Conductivity Calculations.xlsx
Low Perm Aquifer = < 8.46 x103 m/d Hydraulic Conductivity
High Perm Aquifer > 8.46 x103 m/d Hydraulic Conductivity
Soil Permeability
Low = < 5.08 cm/hr
High > 5.08 cm/hr
Drain Class (STATSGO):
SoilDrainageClass_DrClNum_300m.tif
What do values mean?
3 = Excessively drained or Somewhat excessively drained
2 = Well drained or Moderately well drained
1 = Poorly drained, somewhat poorly drained or very poorly drained
0 = No data
DOM_FLOWTY
1 = Overland
2 = Riparian
TOMN_300m
1 = 0-4 hours
2 = 4-12 hours
3 = 12-24 hours
4 = 24-48 hours
5 = 48-72 hours
6 = >72 hours
TSMN_300m
1 = < 1 year
2 = 1-10 years
3 = 10-100 years
4 = 100-1000 years
5 = > 1000 years
DomFldFreq
0 = No data
1 = None
4 = Frequent
DomDrainClass
0 = No data
1 = Poorly drained; Somewhat poorly drained; Very poorly drained
2 = Well drained; Moderately well drained
3 = Excessively drained; Somewhat excessively drained
Imperv
0 = NoData or 0
1 = < 50%
2 = >= 50%
AgDrainBasin
0 = NoData or 0
1 = < 50%
2 = >= 50%
AgDrainPath
0 = NoData or 0
1 = < 50%
2 = >= 50%
Pct Levees
0 = NoData
1 = < 10
2 = <=20
3 = <= 30
4 = <= 40
5 = > 40
### Get RPUs for processing and set directories
```{r, eval=F}
year = '2011'
working_dir = 'D:/WorkFolder/WetlandConnectivity/WetConnect_Oct2019/Wetlands_NLCD'
final_path = paste0(working_dir, year,'/FinalTables/WetlandTables/')
accum_path = paste0(working_dir, year,'/WetlandPath/Accumulation/')
accum_cat = paste0(working_dir, year,'/WetlandCat/Accumulation/')
lookup_tables = paste0(working_dir, year,'/WetlandPath/LookupTables/')
files = list.files(accum_path, pattern = 'NLCD'); rpus = c()
for(i in 1:length(files)){
#print(files[i])
rpus[i] = substr(files[i], 18, 20)
}
```
### Make rise, run, slope tables (path)
```{r, eval=F}
#Make rise tables (path)
for(i in 1:length(rpus)){
print(rpus[i])
elevmax = read.csv(paste0(accum_path, 'Elev_MAX_', rpus[i], '.csv'))
elevmin = read.csv(paste0(accum_path, 'Elev_MIN_', rpus[i], '.csv'))
rise = merge(elevmax, elevmin)
rise$rise = (rise$MAX - rise$MIN) / 100 #convert to meters
rise$rise = rise$rise + 0.1
rise = rise[ ,c('PathID', 'rise')]
names(rise) = c('PATHID', 'rise')
write.csv(rise, paste0(accum_path, 'rise_m_', rpus[i], '.csv'), row.names=F)
}
#Make run tables (path)
for(i in 1:length(rpus)){
print(rpus[i])
flmax = read.csv(paste0(accum_path, 'flowlength_m_MAX_', rpus[i], '.csv'))
flmin = read.csv(paste0(accum_path, 'flowlength_m_MIN_', rpus[i], '.csv'))
run = merge(flmax, flmin)
run$run = (run$MAX - run$MIN) # give non-isolated wetlands a minimum distance of 15m
run$run = ifelse(run$run == 0, NA, run$run)
run = run[ ,c('PathID', 'run')]
names(run) = c('PATHID', 'run')
write.csv(run, paste0(accum_path, 'run_m_', rpus[i], '.csv'), row.names=F)
}
#Make slope tables (path)
for(i in 1:length(rpus)){
print(rpus[i])
rise = read.csv(paste0(accum_path, 'rise_m_', rpus[i], '.csv'))
run = read.csv(paste0(accum_path, 'run_m_', rpus[i], '.csv'))
slope = merge(rise, run, on='PATHID')
slope$slope = (slope$rise / slope$run)
slope = slope[ ,c('PATHID', 'slope')]
write.csv(slope, paste0(accum_path, 'slope_', rpus[i], '.csv'), row.names=F)
}
```
### Make Mannings tables (path)
* Applies a lookup table of Manning's roughness values to NLCD land cover types
```{r, eval=F}
library(stringr); library(foreign); library(matrixStats)
path_dir = accum_path
variables=list.files(path_dir)
# Mannings (path)
if (year=='2011') rat <- read.dbf('L:/Priv/CORFiles/Geospatial_Library_Projects/StreamCat/LandscapeRasters/QAComplete/NLCD_Archive/nlcd2011.tif.vat.dbf')
if (year=='2001') rat <- read.dbf('L:/Priv/CORFiles/Geospatial_Library_Projects/WetlandConnectivity/SpatialDataInputs/NLCD2001/nlcd2001.tif.vat.dbf')
names(rat) <- toupper(names(rat))
r <- paste0('VALUE_', rat$VALUE)
nlcdlist = variables[grep(year, variables)]
mannings = read.csv(paste0(working_dir, year, '/LookupTables/NLCD_Mannings_Lookup.csv'))
head(mannings)
for (i in 1:length(nlcdlist)){
print(nlcdlist[i])
nlcd = read.csv(paste0(path_dir,'/',nlcdlist[i]))
head(nlcd)
#Add missing nlcd cover column if not included
missing <- r[is.na(match(r, names(nlcd)))]
nlcd[,paste(missing)] <- c(0)
#calculate %s for each nlcd category
cols <- grep('VALUE_', names(nlcd))
nlcd[cols] <- prop.table(as.matrix(nlcd[, cols]), margin = 1) * 100
#Calculate final mannings geometric mean
#First raise each mannings to the % of flow path of NLCD type
for (m in 2:length(nlcd)){
nlcd[,m] = (mannings$Mannings[match(names(nlcd)[m], mannings$NLCD)])^(nlcd[,m])
}
#Calculate geomean using product
nlcd$GeoMean = (rowProds(as.matrix(nlcd[,2:length(nlcd)])))^(1/100)
names(nlcd)[1] = 'PATHID'
nlcd = nlcd[, c('PATHID','GeoMean')]
write.csv(nlcd, paste0(path_dir, '/Mannings', str_sub(strsplit(nlcdlist[i],'\\.')[[1]][1],-3),'.csv'), row.names = F)
}
```
### Make precip tables (basin)
* Convert units of precip from mm*10 to mm
* Combines precip table with lookup table of path IDs and wetland IDs so data can be combined
```{r, eval=F}
cat_dir = accum_cat
variables=list.files(cat_dir)
preciplist = variables[grep("US2yr24ha_mm_10x_MEAN_", variables)]
for(i in 1:length(rpus)){
print(rpus[i])
precip = read.csv(paste0(cat_dir, 'US2yr24ha_mm_10x_MEAN_', rpus[i], '.csv'))
precip$PRECIP_2YR24HR_MM = (precip$SUM / precip$COUNT) * 0.1
lookup = read.csv(paste0(lookup_tables, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
names(precip) = toupper(names(precip))
# precip$PATHID = lookup$PATHID[match(precip$BASINID, lookup$WET_ID)]
lookup$PRECIP_2YR24HR_MM = precip$PRECIP_2YR24HR_MM[match(lookup$WET_ID, precip$BASINID)]
names(lookup)[grep(pattern='WET_ID', names(lookup))] = 'BASINID'
precip = lookup[, c('BASINID','PATHID','PRECIP_2YR24HR_MM')]
write.csv(precip, paste0(accum_path, 'Precip2Yr24hr_', rpus[i], '.csv'), row.names=F)
}
```
### Combine to make start of subsurface flow table (path)
```{r, eval=F}
dir.create(final_path, showWarnings = FALSE)
for(i in 1:length(rpus)){
print(rpus[i])
run = read.csv(paste0(accum_path, 'run_m_', rpus[i], '.csv'))
slope = read.csv(paste0(accum_path, 'slope_', rpus[i], '.csv'))
porosity = read.csv(paste0(accum_path, 'AvgPorosity_MAX_', rpus[i], '.csv'))
names(porosity) <- c('PATHID', 'porosity_max')
perm = read.csv(paste0(accum_path, 'AvgPerm_MIN_', rpus[i], '.csv'))
perm$MIN = perm$MIN / 1000 #convert from m/d to mm/d
names(perm) <- c('PATHID', 'perm_min')
out_table = run
out_table = merge(out_table, slope)
out_table = merge(out_table, porosity)
out_table = merge(out_table, perm)
write.csv(out_table, paste0(final_path, 'ShallowFlow_', rpus[i], '.csv'), row.names=F)
}
```
### Make subsurface travel time (path)
```{r, eval=F}
for(i in 1:length(rpus)){
print(rpus[i])
travel = read.csv(paste0(final_path, 'ShallowFlow_', rpus[i], '.csv'))
#Add small factor to perm and slope values
travel$perm_min = travel$perm_min + 0.1
travel$slope = travel$slope + 0.01
travel$t_shallow = travel$run / ((travel$perm_min / travel$porosity_max) * travel$slope)
print(summary(travel))
print(nrow(is.infinite(travel$t_shallow)))
write.csv(travel, paste0(final_path, 'ShallowFlow_', rpus[i], '.csv'), row.names=F)
}
```
### Make flow type table (path)
```{r, eval=F}
library(dplyr)
r = c('VALUE_11', 'VALUE_12', 'VALUE_21', 'VALUE_22')
for(i in 1:length(rpus)){
print(rpus[i])
flow_cls = read.csv(paste0(accum_path, 'BdrckPerm_4_classes_Alb83_PERCENT_', rpus[i], '.csv'))
missing <- r[is.na(match(r, names(flow_cls)))]
flow_cls[,paste(missing)] <- c(0)
flow_cls = flow_cls[, c('PathID', r)] #make in right order
flow_cls[ ,2:length(flow_cls)] = prop.table(as.matrix(flow_cls[ ,2:length(flow_cls)]), margin = 1) * 100
flow_cls$Overland = flow_cls$VALUE_12 + flow_cls$VALUE_22
#Make sure in correct order
flow_cls = flow_cls[, c('PathID','VALUE_11','VALUE_21','Overland')]
names(flow_cls)[1:3] <- c('PATHID','Shallow','ShallowDeep')
type_names = names(flow_cls)[2:4]
# Set row where all NA to zero
flow_cls[is.na(flow_cls)] <- 0
flow_cls$DomFlow = type_names[apply(flow_cls[, 2:4], 1, which.max)]
# which.max assigns first column to rows where all zero (path fell outside raster boundary)
# we'll recycle missing and set DomFlow to NA for these records
missing <- flow_cls[apply(flow_cls[, 2:4], MARGIN = 1, function(x) all(x == 0)), ]
flow_cls$DomFlow[flow_cls$PATHID %in% missing$PATHID] <- NA
print(summary(flow_cls))
write.csv(flow_cls, paste0(final_path, 'FlowClass_', rpus[i], '.csv'), row.names=F)
}
```
### Make Wetland path Drain Class AND Drain Freq table (Drain Freq is the 'min of min' drainage class over a flow path)
3 = Excessively drained or Somewhat excessively drained (0 in Drain Freq)
2 = Well drained or Moderately well drained (.5 in Drain Freq)
1 = Poorly drained, somewhat poorly drained or very poorly drained (1 in Drain Freq)
0 = No data (NA in Drain Freq)
```{r, eval=F}
library(dplyr)
r = c('VALUE_0', 'VALUE_1', 'VALUE_2', 'VALUE_3')
for(i in 1:length(rpus)){
print(rpus[i])
flow_cls = read.csv(paste0(accum_path, 'DrainClass_PERCENT_', rpus[i], '.csv'))
missing <- r[is.na(match(r, names(flow_cls)))]
flow_cls[,paste(missing)] <- c(0)
flow_cls = flow_cls[, c('PathID', r)] #make in right order
print(summary(flow_cls))
flow_cls[ ,3:length(flow_cls)] = prop.table(as.matrix(flow_cls[ ,3:length(flow_cls)]), margin = 1) * 100
type_names = names(flow_cls)[3:length(flow_cls)]
names(flow_cls)[1] <- 'PATHID'
# Set row where all NA to zero
flow_cls[is.na(flow_cls)] <- 0
flow_cls$FreqClass <- as.factor(ifelse(flow_cls$VALUE_3 > 0, 'VALUE_3',
ifelse(flow_cls$VALUE_2 > 0, 'VALUE_2',
ifelse(flow_cls$VALUE_1 > 0, 'VALUE_1', 'VALUE_0'))))
print(summary(flow_cls))
write.csv(flow_cls, paste0(final_path, 'DrainClass_', rpus[i], '.csv'), row.names=F)
}
```
### Combine to create start of overland table
```{r, eval=F}
for(i in 1:length(rpus)){
print(rpus[i])
run = read.csv(paste0(accum_path, 'run_m_', rpus[i], '.csv'))
slope = read.csv(paste0(accum_path, 'slope_', rpus[i], '.csv'))
slope$slope = slope$slope + 0.01
man = read.csv(paste0(accum_path, 'Mannings', rpus[i], '.csv'))
#man = man[ , c('PATHID','GeoMean')]
names(man) <- c('PATHID','Manning')
precip = read.csv(paste0(accum_path, 'Precip2Yr24hr_', rpus[i], '.csv'))
#precip = precip[!is.na(precip$PATHID),] #Was causing problems later in code
#Precip 2yr24hr values already divided by 10 when making accumulation tables, so use as is
#precip = precip[c(2:4)]
out_table = precip
out_table = merge(out_table, slope, by='PATHID', all.x=TRUE)
out_table = merge(out_table, man, by='PATHID', all.x=TRUE)
out_table = merge(out_table, run, by='PATHID', all.x=TRUE)
print(summary(out_table))
write.csv(out_table, paste0(final_path, 'OverlandFlow_', rpus[i], '.csv'), row.names=F)
}
```
### Make overland travel time
```{r, eval=F}
for(i in 1:length(rpus)){
print(rpus[i])
travel = read.csv(paste0(final_path, 'OverlandFlow_', rpus[i], '.csv'))
travel$t_overland = 0.0913*(((travel$Manning * travel$run)^0.8)/((travel$PRECIP_2YR24HR_MM)^0.5 * (travel$slope)^0.4))
print(summary(travel))
print(nrow(is.infinite(travel$t_overland)))
write.csv(travel, paste0(final_path, 'OverlandFlow_', rpus[i], '.csv'), row.names=F)
}
```
### Make levee influence tables
* Index is binary: 0 = no influence, 1 = influence
* If SUM of path pixels coming in contact with buffered levees > 0; influence == 1
```{r, eval=F}
lookup_dir = paste0(working_dir ,year,'/WetlandPath/LookupTables')
for(i in 1:length(rpus)){
print(rpus[i])
lookup = read.csv(paste0(lookup_dir, '/WetlandsWithPath_StreamLink_Lookup_', rpus[i], '.csv'))
levee = read.csv(paste0(accum_path, 'LeveeProximity_MEAN_', rpus[i], '.csv'))
lookup$LeveeInfluence = levee$SUM[match(lookup$PATHID,levee$PathID)]
if (! "LeveeInfluence" %in% colnames(lookup)) lookup$LeveeInfluence = 0
lookup$LeveeInfluence[lookup$LeveeInfluence > 0] = 1
lookup$LeveeInfluence[is.na(lookup$LeveeInfluence)] = 0
levee = lookup
levee = levee[, c('PATHID','LeveeInfluence')]
print(summary(levee))
write.csv(levee, paste0(final_path, 'LeveeInfluence_', rpus[i], '.csv'), row.names=F)
}
```
### Make canal / ditch influence tables
* Index is binary: 0 = no influence, 1 = influence
* If SUM of path pixels coming in contact with NHD canals > 0; influence == 1
```{r, eval=F}
lookup_dir = paste0(working_dir ,year,'/WetlandPath/LookupTables')
for(i in 1:length(rpus)){
print(rpus[i])
lookup = read.csv(paste0(lookup_dir, '/WetlandsWithPath_StreamLink_Lookup_', rpus[i], '.csv'))
canals = read.csv(paste0(accum_path, 'CanalsDitches_MEAN_', rpus[i], '.csv'))
lookup$CanalInfluence = canals$SUM[match(lookup$PATHID,canals$PathID)]
if (! "CanalInfluence" %in% colnames(lookup)) lookup$CanalInfluence = 0
lookup$CanalInfluence[lookup$CanalInfluence > 0] = 1
lookup$CanalInfluence[is.na(lookup$CanalInfluence)] = 0
canals = lookup
canals = canals[, c('PATHID','CanalInfluence')]
print(summary(canals))
write.csv(canals, paste0(final_path, 'CanalInfluence_', rpus[i], '.csv'), row.names=F)
}
```
### Make wetland basin imperviousness tables
```{r, eval=F}
for(i in 1:length(rpus)){
print(rpus[i])
imperv = read.csv(paste0(accum_cat, 'Impervious_',year,'_MEAN_', rpus[i], '.csv'))
imperv$BasinImperv = imperv$SUM / imperv$COUNT
names(imperv)[1] = 'BASINID'
imperv = imperv[ , c('BASINID','BasinImperv')]
print(summary(imperv))
write.csv(imperv, paste0(final_path, 'Imperviousness_', rpus[i], '.csv'), row.names=F)
}
```
### Make wetland basin ag drainage tables
```{r, eval=F}
r = c('VALUE_0', 'VALUE_1')
for(i in 1:length(rpus)){
print(rpus[i])
ad = read.csv(paste0(accum_cat, 'AgDrainage_PERCENT_', rpus[i], '.csv'))
missing <- r[is.na(match(r, names(ad)))]
ad[,paste(missing)] <- c(0)
ad[ ,2:3] = prop.table(as.matrix(ad[ ,2:3]), margin = 1) * 100
ad = ad[, c('BasinID','VALUE_1')]
names(ad) = c('BASINID','PctAgDrainBasin')
print(summary(ad))
write.csv(ad, paste0(final_path, 'AgDrainage_Basin_', rpus[i], '.csv'), row.names=F)
}
```
### Make wetland flowpath ag drainage tables (isolated wetlands)
```{r, eval=F}
r = c('VALUE_0', 'VALUE_1')
for(i in 1:length(rpus)){
print(rpus[i])
ad = read.csv(paste0(accum_path, 'AgDrain_PERCENT_', rpus[i], '.csv'))
missing <- r[is.na(match(r, names(ad)))]
ad[,paste(missing)] <- c(0)
ad[ ,2:3] = prop.table(as.matrix(ad[ ,2:3]), margin = 1) * 100
ad = ad[, c('PathID','VALUE_1')]
names(ad) = c('PATHID','PctAgDrainPath')
print(summary(ad))
write.csv(ad, paste0(final_path, 'AgDrainage_Path_', rpus[i], '.csv'), row.names=F)
}
```
### Make basin area tables
```{r, eval=F}
for(i in 1:length(rpus)){
print(rpus[i])
precip = read.csv(paste0(accum_cat, 'US2yr24ha_mm_10x_MEAN_', rpus[i], '.csv'))
precip$AreaSqKm = (precip$COUNT * 900) / 1e6
precip = precip[, c('BasinID','AreaSqKm')]
names(precip)[1] = 'BASINID'
print(summary(precip))
write.csv(precip, paste0(final_path, 'BasinAreaSqKm_', rpus[i], '.csv'), row.names=F)
}
```
### Create shapefiles of wetland outlets
```{r, eval=F}
library(sf)
library(raster)
wetpoints_path = paste0(working_dir, year,'/WetlandPoints')
for(i in 1:length(rpus)){
print(rpus[i])
wetpoint_raster <- raster(paste0(wetpoints_path, '/WetlandPoints', rpus[i], '.tif'))
wetpoints <- rasterToPoints(wetpoint_raster)
wetpoints <- st_as_sf(as.data.frame(wetpoints), coords = c('x','y'), crs=5070)
st_write(wetpoints, paste0(wetpoints_path,'/WetlandPoints', rpus[i], '.shp'))
}
```
### Tie NHDPlus Catchment COMID to WET_ID for wetlands
```{r, eval=F}
library(foreign)
library(rgdal)
library(raster)
library(rgeos)
library(sf)
nhd_path = 'L:/Priv/CORFiles/Geospatial_Library_Resource/PHYSICAL/HYDROLOGY/NHDPlusV21'
wetpoints_path = paste0(working_dir, year,'/WetlandPoints')
#lookup_path = paste0(working_dir, year,'/WetlandPath/LookupTables/')
#rpu_lookup_path = paste0(working_dir, year,'/WetlandPath/LookupTables/')
out_lookup = paste0(working_dir, year,'/LookupTables/')
hydro.rgns <- c("01","02","03S","03N","03W","04","05","06","07","08","09","10L","10U","11","12","13","14","15","16","17","18")
rgns <- c("NE","MA","SA","SA","SA","GL","MS","MS","MS","MS","SR","MS","MS","MS","TX","RG","CO","CO","GB","PN","CA")
for (i in 1:21){
print(hydro.rgns[i])
#Read in catchment DBF
cats = raster(paste0(nhd_path,"/NHDPlus",rgns[i],"/NHDPlus",hydro.rgns[i],"/NHDPlusCatchment/cat"))
catpolys = st_read(paste0(nhd_path,"/NHDPlus",rgns[i],"/NHDPlus",hydro.rgns[i],"/NHDPlusCatchment/Catchment.shp"))
#Read in wetland pour points
files = list.files(paste0(nhd_path,"/NHDPlus",rgns[i],"/NHDPlus",hydro.rgns[i]), pattern = 'FdrFac')
files = files[!grepl('\\.txt$',files) & ! grepl('\\.7z$',files)]
vpu_rpus = substr(files, 14,16)
for (k in 1:length(vpu_rpus)){
wetpoints <- st_read(paste0(wetpoints_path,'/WetlandPoints', vpu_rpus[k], '.shp'))
wetID_CatID = extract(cats, wetpoints, df=TRUE)
wetID_CatID$COMID = catpolys$FEATUREID[match(wetID_CatID$cat,catpolys$GRIDCODE)]
wetID_CatID = wetID_CatID[c('COMID')]
st_geometry(wetpoints) <- NULL
wetID_CatID = cbind(wetpoints, wetID_CatID)
names(wetID_CatID) <- c('WetID','COMID')
wetID_CatID$CatAreaSqKm = catpolys$AreaSqKM[match(wetID_CatID$COMID,catpolys$FEATUREID)]
# NOTE - there are wetlands with NAs for catchments - this is fine - the wetlands lie outside of NHDPlus catchments.
write.csv(wetID_CatID, paste0(out_lookup, 'WetlandsID_COMID_', vpu_rpus[k], '.csv'), row.names=F)
}
}
```
### Add Ecoregion to final lookup tables
```{r, eval=F}
library(rgdal); library(rgeos); library(sf)
out_lookup = paste0(working_dir, year,'/LookupTables/')
wetpoints_path = paste0(working_dir, year,'/WetlandPoints')
eco9_lookup = read.csv('L:/Priv/CORFiles/Geospatial_Library_Projects/WetlandConnectivity/eco9_all.csv')
eco9_sp = readOGR('L:/Priv/CORFiles/Geospatial_Library_RESOURCE/PHYSICAL/ECOREGIONS','Aggr_Eco9_2015')
eco9 = gSimplify(eco9_sp, 1000, topologyPreserve=TRUE)
eco9 = gBuffer(eco9, byid=TRUE,width=15000)
eco9_sp = SpatialPolygonsDataFrame(eco9, eco9_sp@data)
OverUpdate <- function(points, polys) {
pointpoly <- over(points, polys)
points@data <- data.frame(points@data, pointpoly)
}
for(i in 1:length(rpus)){
print(rpus[i])
lookup = read.csv(paste0(out_lookup, 'WetlandsID_COMID_', rpus[i], '.csv'))
lookup$WSA_9 = eco9_lookup$WSA_9[match(lookup$COMID, eco9_lookup$COMID)]
if (any(is.na(lookup$WSA_9))){
print(paste0('grab missing WSA9 for ',rpus[i]))
# get nearest WSA9 where missing
missing <- lookup[is.na(lookup$WSA_9),]
wetpoints = readOGR(wetpoints_path, paste0('WetlandPoints', rpus[i]))
crs(wetpoints) <- crs(eco9_sp)
names(wetpoints@data) <- 'GRID_CODE'
missing <- wetpoints[wetpoints$GRID_CODE %in% missing$WetID,]
missing@data = OverUpdate(missing, eco9_sp)
if (any(is.na(missing$WSA9))){
# just check if missing any - looks like we aren't
print(paste0('missing a few in ',rpus[i]))
}
}
lookup$WSA_9[is.na(lookup$WSA_9)] = missing$WSA9[match(lookup$WetID[is.na(lookup$WSA_9)],missing$GRID_CODE)]
write.csv(lookup, paste0(out_lookup, 'WetlandsID_COMID_', rpus[i], '.csv'), row.names=F)
}
```
### Add State and Tiner Wetland Regions to final lookup tables
* Use UnID code in the Tine shapefile rather than Tiner wetland region name to get individual Tiner polygon IDs
```{r, eval=F}
library(rgdal); library(rgeos)
#Define update function for points that miss polygons
OverUpdate <- function(points, polys) {
pointpoly <- over(points, polys)
points@data <- data.frame(points@data, pointpoly)
}
out_lookup = paste0(working_dir,year,'/LookupTables/')
wetpoints_path = paste0(working_dir,year,'/WetlandPoints')
#eco9_lookup = read.csv('L:/Priv/CORFiles/Geospatial_Library_Projects/WetlandConnectivity/eco9_all.csv')
states_sp = readOGR('L:/Priv/CORFiles/Geospatial_Library_RESOURCE/POLITICAL/BOUNDARIES/NATIONAL','TIGER_2010_State_Boundaries')
Wetland_regions = readOGR('L:/Priv/CORFiles/Geospatial_Library_Projects/WetlandConnectivity/SpatialDataInputs/Tiner Region Boundaries','TinerWetlandGroups')
states_sp = states_sp[!states_sp$STUSPS10 %in% c('AK', 'HI', 'PR'),]
states_sp = states_sp[c('STUSPS10')]
states_sp = spTransform(states_sp, CRS("+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"))
Wetland_regions = spTransform(Wetland_regions, CRS("+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"))
Wetland_regions = Wetland_regions[c('UnID')]
states_buf = gSimplify(states_sp, 1000, topologyPreserve=TRUE)
states_buf = gBuffer(states_buf, byid=TRUE,width=15000)
states_buf = SpatialPolygonsDataFrame(states_buf, states_sp@data)
for(i in 1:length(rpus)){
print(rpus[i])
wetpoints = readOGR(wetpoints_path, paste0('WetlandPoints', rpus[i]))
wetpoints <- spTransform(wetpoints, CRS(proj4string(states_sp)))
names(wetpoints@data) <- 'GRID_CODE'
wetpoints@data <- OverUpdate(wetpoints, states_sp)
if (any(is.na(wetpoints$STUSPS10))){
missing = subset(wetpoints, is.na(STUSPS10))
wetpoints = wetpoints[!is.na(wetpoints$STUSPS10), ]
missing = missing[, 'GRID_CODE']
missing@data = OverUpdate(missing, states_buf)
wetpoints = rbind(wetpoints, missing)
}
wetpoints@data <- OverUpdate(wetpoints, Wetland_regions)
wetpoints$UnID = as.character(wetpoints$UnID)
wetpoints$UnID[is.na(wetpoints$UnID)] = 'None'
lookup = read.csv(paste0(out_lookup, 'WetlandsID_COMID_', rpus[i], '.csv'))
lookup$STATE = wetpoints$STUSPS10[match(lookup$WetID, wetpoints$GRID_CODE)]
lookup$WetlandRegion = wetpoints$UnID[match(lookup$WetID, wetpoints$GRID_CODE)]
write.csv(lookup, paste0(out_lookup, 'WetlandsID_COMID_', rpus[i], '.csv'), row.names=F)
}
```
### Add additional ID/attributes/location info to flow type table
* Code adds COMID, catchment area, ecoregion, state, wetland region, and wetland/RPU IDs to FlowType tables
* Add Riparian as a flow type definition
```{r, eval=F}
table_path = paste0(working_dir, year, '/FinalTables/WetlandTables/')
lookup_path = paste0(working_dir, year, '/WetlandPath/LookupTables/')
comeco_path = paste0(working_dir, year, '/LookupTables/')
for(i in 1:length(rpus)){
print(rpus[i])
in_table = read.csv(paste0(table_path, 'FlowClass_', rpus[i], '.csv'))
nrow(in_table)
head(in_table)
allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
head(allwet)
head(comeco)
# We end up dropping 'intermediate' flow path records here that are not
# directly associated with a wetland ID - this is to be expected
allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
in_table = merge(in_table, allwet, by = 'PATHID', all.y=T)
in_table = in_table[ , c('WET_ID', 'PATHID','COMID','CatAreaSqKm','WSA_9', 'WET_ID_RPU','STATE','WetlandRegion', 'Riparian', 'Shallow', 'ShallowDeep', 'Overland', 'DomFlow')]
summary(in_table)
in_table$Shallow[is.na(in_table$PATHID)] = 0
in_table$ShallowDeep[is.na(in_table$PATHID)] = 0
in_table$Overland[is.na(in_table$PATHID)] = 0
in_table$DomFlow = as.character(in_table$DomFlow)
in_table$DomFlow[in_table$Riparian==1] = 'Riparian'
print(summary(in_table))
write.csv(in_table, paste0(table_path, 'FlowClass_', rpus[i], '.csv'), row.names=F)
}
```
### Add COMID and eco9 to Overland Flow tables
```{r, eval=F}
table_path = paste0(working_dir, year, '/FinalTables/WetlandTables/')
lookup_path = paste0(working_dir, year, '/WetlandPath/LookupTables/')
comeco_path = paste0(working_dir, year, '/LookupTables/')
for(i in 1:length(rpus)){
print(rpus[i])
in_table = read.csv(paste0(table_path, 'OverlandFlow_', rpus[i], '.csv'))
nrow(in_table)
head(in_table)
allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
head(allwet)
head(comeco)
# We end up dropping 'intermediate' flow path records here that are not
# directly associated with a wetland ID - this is to be expected
allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
in_table = merge(in_table, allwet, by.x='BASINID',by.y='WET_ID',all.y=T)
names(in_table)[2] = 'PATHID'
in_table = in_table[ , c('BASINID', 'PATHID','COMID','CatAreaSqKm','WSA_9', 'WET_ID_RPU','STATE','WetlandRegion', 'Riparian','PRECIP_2YR24HR_MM', 'slope', 'Manning', 'run', 't_overland')]
names(in_table)[1] <- 'WET_ID'
in_table$t_overland[in_table$Riparian == 1] = 0
print(head(in_table))
write.csv(in_table, paste0(table_path, 'OverlandFlow_', rpus[i], '.csv'), row.names=F)
}
```
### Add COMID and eco9 to Subsurface Flow
```{r, eval=F}
table_path = paste0(working_dir, year, '/FinalTables/WetlandTables/')
lookup_path = paste0(working_dir, year, '/WetlandPath/LookupTables/')
comeco_path = paste0(working_dir, year, '/LookupTables/')
for(i in 1:length(rpus)){
print(rpus[i])
in_table = read.csv(paste0(table_path, 'ShallowFlow_', rpus[i], '.csv'))
nrow(in_table)
head(in_table)
allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
head(allwet)
head(comeco)
allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
#allwet = allwet[!is.na(allwet$PATHID),]
in_table = merge(in_table, allwet, by='PATHID',all.y=T)
in_table = in_table[ , c('WET_ID', 'PATHID','COMID','CatAreaSqKm','WSA_9','WET_ID_RPU','STATE','WetlandRegion', 'Riparian', 'run', 'slope', 'porosity_max', 'perm_min', 't_shallow')]
in_table$t_shallow[in_table$Riparian == 1] = 0
print(summary(in_table))
write.csv(in_table, paste0(table_path, 'ShallowFlow_', rpus[i], '.csv'), row.names=F)
}
```
### Add COMID and eco9 to tables
Imperviousness
```{r, eval=F}
table_path = paste0(working_dir, year, '/FinalTables/WetlandTables/')
lookup_path = paste0(working_dir, year, '/WetlandPath/LookupTables/')
comeco_path = paste0(working_dir, year, '/LookupTables/')
for(i in 1:length(rpus)){
print(rpus[i])
in_table = read.csv(paste0(table_path, 'Imperviousness_', rpus[i], '.csv'))
nrow(in_table)
head(in_table)
allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
head(allwet)
head(comeco)
allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
in_table = merge(in_table, allwet, by.x='BASINID', by.y='WET_ID',all.y=T)
names(in_table)[1] = 'WET_ID'
names(in_table)[3] = 'PATHID'
in_table = in_table[ , c('WET_ID','PATHID','COMID','CatAreaSqKm','WSA_9','WET_ID_RPU', 'STATE','WetlandRegion', 'Riparian','BasinImperv')]
print(summary(in_table))
write.csv(in_table, paste0(table_path, 'Imperviousness_', rpus[i], '.csv'), row.names=F)
}
```
### Add COMID and eco9 to tables
Ag Drainage Basin
```{r, eval=F}
table_path = paste0(working_dir, year, '/FinalTables/WetlandTables/')
lookup_path = paste0(working_dir, year, '/WetlandPath/LookupTables/')
comeco_path = paste0(working_dir, year, '/LookupTables/')
for(i in 1:length(rpus)){
print(rpus[i])
in_table = read.csv(paste0(table_path, 'AgDrainage_Basin_', rpus[i], '.csv'))
nrow(in_table)
head(in_table)
allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
head(allwet)
head(comeco)
allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
in_table = merge(in_table, allwet, by.x='BASINID', by.y='WET_ID',all.y=T)
names(in_table)[1] = 'WET_ID'
names(in_table)[3] = 'PATHID'
in_table = in_table[ , c('WET_ID','PATHID','COMID','CatAreaSqKm','WSA_9','WET_ID_RPU', 'STATE','WetlandRegion', 'Riparian','PctAgDrainBasin')]
print(summary(in_table))
write.csv(in_table, paste0(table_path, 'AgDrainage_Basin_', rpus[i], '.csv'), row.names=F)
}
```
### Add COMID and eco9 to tables
Ag Drainage Path
```{r, eval=F}
table_path = paste0(working_dir, year, '/FinalTables/WetlandTables/')
lookup_path = paste0(working_dir, year, '/WetlandPath/LookupTables/')
comeco_path = paste0(working_dir, year, '/LookupTables/')
for(i in 1:length(rpus)){
print(rpus[i])
in_table = read.csv(paste0(table_path, 'AgDrainage_Path_', rpus[i], '.csv'))
nrow(in_table)
head(in_table)
allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
head(allwet)
head(comeco)
allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
in_table = merge(in_table, allwet, by='PATHID',all.y=T)
in_table = in_table[ , c('WET_ID','PATHID','COMID','CatAreaSqKm','WSA_9','WET_ID_RPU', 'STATE','WetlandRegion', 'Riparian','PctAgDrainPath')]
in_table$PctAgDrainPath[in_table$Riparian == 1] = 0
print(summary(in_table))
write.csv(in_table, paste0(table_path, 'AgDrainage_Path_', rpus[i], '.csv'), row.names=F)
}
```
### We no longer use this!
Add COMID and eco9 to tables
RunoffFreq Basin
Classes [0 = least likely to flood; 4 = most likely to flood] 0: -1600 to -1200 (mm) 1: -1200 to -852 2: -852 to -505 3: -505 to -157 4: -157 to 200
```{r, eval=F}
# table_path = paste0(working_dir,year,'/FinalTables/WetlandTables/')
# lookup_path = paste0(working_dir,year,'/WetlandPath/LookupTables/')
# comeco_path = paste0(working_dir,year,'/LookupTables/')
#
# for(i in 1:length(rpus)){
# print(rpus[i])
#
# in_table = read.csv(paste0(table_path, 'SatOvr2yr24hmm_', rpus[i], '.csv'))
#
# nrow(in_table)
# head(in_table)
# allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
# comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
# head(allwet)
# head(comeco)
#
# allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
# allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
# allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
# allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
# allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
# allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
# in_table = merge(in_table, allwet, by.x='BasinID', by.y='WET_ID',all.y=T)
#
# names(in_table)[1] = 'WET_ID'
# names(in_table)[2] = 'SatOvr2yr24hmm'
# names(in_table)[3] = 'PATHID'
#
# in_table = in_table[ , c('WET_ID','PATHID','COMID','CatAreaSqKm','WSA_9','WET_ID_RPU', 'STATE','WetlandRegion', 'Riparian','SatOvr2yr24hmm')]
#
# print(summary(in_table))
# write.csv(in_table, paste0(table_path, 'SatOvr2yr24hmm_', rpus[i], '.csv'), row.names=F)
# }
```
### Add COMID and eco9 to tables
DrainFreqClass Path
```{r, eval=F}
table_path = paste0(working_dir, year, '/FinalTables/WetlandTables/')
lookup_path = paste0(working_dir, year, '/WetlandPath/LookupTables/')
comeco_path = paste0(working_dir, year, '/LookupTables/')
for(i in 1:length(rpus)){
print(rpus[i])
in_table = read.csv(paste0(table_path, 'DrainClass_', rpus[i], '.csv'))
nrow(in_table)
head(in_table)
allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
head(allwet)
head(comeco)
allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
in_table = merge(in_table, allwet, by='PATHID',all.y=T)
names(in_table)[6] = 'DrainFreqClass'
#names(in_table)[7] = 'DrainFreq'
in_table = in_table[ , c('WET_ID','PATHID','COMID','CatAreaSqKm','WSA_9','WET_ID_RPU', 'STATE','WetlandRegion', 'Riparian', 'DrainFreqClass')]
print(summary(in_table))
write.csv(in_table, paste0(table_path, 'DrainClass_', rpus[i], '.csv'), row.names=F)
}
```
### Add COMID and eco9 to tables
LeveeInfluence
```{r, eval=F}
table_path = paste0(working_dir, year, '/FinalTables/WetlandTables/')
lookup_path = paste0(working_dir, year, '/WetlandPath/LookupTables/')
comeco_path = paste0(working_dir, year, '/LookupTables/')
for(i in 1:length(rpus)){
print(rpus[i])
in_table = read.csv(paste0(table_path, 'LeveeInfluence_', rpus[i], '.csv'))
nrow(in_table)
head(in_table)
allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
head(allwet)
head(comeco)
allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
in_table = merge(in_table, allwet, by='PATHID',all.y=T)
#names(in_table)[3] = 'LeveeInfluence'
in_table = in_table[ , c('WET_ID','PATHID','COMID','CatAreaSqKm','WSA_9','WET_ID_RPU', 'STATE','WetlandRegion', 'Riparian','LeveeInfluence')]
in_table$LeveeInfluence[in_table$Riparian == 1] = 0
write.csv(in_table, paste0(table_path, 'LeveeInfluence_', rpus[i], '.csv'), row.names=F)
}
```
### Add COMID and eco9 to tables
Wetland basin areas
```{r, eval=F}
table_path = paste0(working_dir, year, '/FinalTables/WetlandTables/')
lookup_path = paste0(working_dir, year, '/WetlandPath/LookupTables/')
comeco_path = paste0(working_dir, year, '/LookupTables/')
for(i in 1:length(rpus)){
print(rpus[i])
in_table = read.csv(paste0(table_path, 'BasinAreaSqKm_', rpus[i], '.csv'))
nrow(in_table)
head(in_table)
allwet = read.csv(paste0(lookup_path, 'AllWetlands_StreamLink_Lookup_', rpus[i], '.csv'))
comeco = read.csv(paste0(comeco_path, 'WetlandsID_COMID_', rpus[i], '.csv'))
head(allwet)
head(comeco)
allwet$COMID <- comeco$COMID[match(allwet$WET_ID, comeco$WetID)]
allwet$CatAreaSqKm <- comeco$CatAreaSqKm[match(allwet$WET_ID, comeco$WetID)]
allwet$WSA_9 <- comeco$WSA_9[match(allwet$WET_ID, comeco$WetID)]
allwet$STATE <- comeco$STATE[match(allwet$WET_ID, comeco$WetID)]
allwet$WetlandRegion <- comeco$WetlandRegion[match(allwet$WET_ID, comeco$WetID)]
allwet$WET_ID_RPU = paste0(allwet$WET_ID,"_",rpus[i])
in_table = merge(in_table, allwet, by.x='BASINID', by.y='WET_ID',all.y=T)
names(in_table)[1] = 'WET_ID'
names(in_table)[3] = 'PATHID'
in_table = in_table[ , c('WET_ID','PATHID','COMID','CatAreaSqKm','WSA_9','WET_ID_RPU', 'STATE','WetlandRegion', 'Riparian','AreaSqKm')]
#in_table$AreaSqKm[is.na(in_table$PATHID)] = 0
write.csv(in_table, paste0(table_path, 'BasinAreaSqKm_', rpus[i], '.csv'), row.names=F)
}