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spafhy_io.py
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spafhy_io.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jun 30 10:34:37 2016
@author: slauniai
"""
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
eps = np.finfo(float).eps # machine epsilon
def clear_console():
"""
clears Spyder console window - does not affect namespace
"""
import os
clear = lambda: os.system('cls')
clear()
return None
""" ******* Get forcing data for FIHy and FICage sites ******** """
def read_HydeDaily(filename):
cols=['time','doy','NEE','GPP','TER','ET','H','NEEflag','ETflag','Hflag','Par','Rnet','Ta','VPD','CO2','PrecSmear','Prec','U','Pamb',
'SWE0','SWCh','SWCa','SWCb','SWCc', 'Tsh','Tsa','Tsb','Tsc','RnetFlag','Trfall','Snowdepth','Snowdepthstd','SWE','SWEstd','Roff1','Roff2']
dat=pd.read_csv(filename,sep='\s+',header=None, names=None, parse_dates=[[0,1,2]], keep_date_col=False)
dat.columns=cols
dat.index=dat['time']; dat=dat.drop(['time','SWE0'],axis=1)
forc=dat[['doy','Ta','VPD','Prec','Par','U']]; forc['Par']= 1/4.6*forc['Par']; forc['Rg']=2.0*forc['Par']
forc['VPD'][forc['VPD']<=0]=eps
#relatively extractable water, Hyde A-horizon
#poros = 0.45
fc = 0.30
wp = 0.10
Wliq = dat['SWCa']
Rew = np.maximum( 0.0, np.minimum( (Wliq-wp)/(fc - wp + eps), 1.0) )
forc['Rew'] = Rew
forc['CO2'] = 380.0
# beta, soil evaporation parameter
#forc['beta'] = Wliq / fc
return dat, forc
def read_CageDaily(filepath):
cols=['time','doy','NEE','GPP','TER','ET','H','NEEflag','ETflag','Hflag','Par','Rnet','Ta','VPD','CO2','SWCa','PrecSmear','Prec','U','Pamb']
dat1=pd.read_csv(filepath + 'HydeCage4yr-2000.txt',sep='\s+',header=None, names=None, parse_dates=[[0,1,2]], keep_date_col=False)
dat1.columns=cols
dat1.index=dat1['time']; dat1=dat1.drop('time',axis=1)
forc1=dat1[['doy','Ta','VPD','Prec','Par','U']]; forc1['Par']= 1/4.6*forc1['Par']; forc1['Rg']=2.0*forc1['Par']
dat2=pd.read_csv(filepath + 'HydeCage12yr-2002.txt',sep='\s+',header=None, names=None, parse_dates=[[0,1,2]], keep_date_col=False)
dat2.columns=cols
dat2.index=dat2['time']; dat2=dat2.drop('time',axis=1)
forc2=dat2[['doy','Ta','VPD','Prec','Par','U']]; forc2['Par']= 1/4.6*forc2['Par']; forc2['Rg']=2.0*forc2['Par']
return dat1, dat2,forc1,forc2
""" ********* Get Forcing data: SVE catchments ****** """
def read_FMI_weather(ID, start_date, end_date, sourcefile, CO2=380.0):
"""
reads FMI interpolated daily weather data from file
IN:
ID - sve catchment ID. set ID=0 if all data wanted
start_date - 'yyyy-mm-dd'
end_date - 'yyyy-mm-dd'
sourcefile - optional
CO2 - atm. CO2 concentration (float), optional
OUT:
fmi - pd.dataframe with datetimeindex
fmi columns:['ID','Kunta','aika','lon','lat','T','Tmax','Tmin',
'Prec','Rg','h2o','dds','Prec_a','Par',
'RH','esa','VPD','doy']
units: T, Tmin, Tmax, dds[degC], VPD, h2o,esa[kPa],
Prec, Prec_a[mm], Rg,Par[Wm-2],lon,lat[deg]
"""
# OmaTunniste;OmaItä;OmaPohjoinen;Kunta;siteid;vuosi;kk;paiva;longitude;latitude;t_mean;t_max;t_min;
# rainfall;radiation;hpa;lamposumma_v;rainfall_v;lamposumma;lamposumma_cum
# -site number
# -date (yyyy mm dd)
# -latitude (in KKJ coordinates, metres)
# -longitude (in KKJ coordinates, metres)
# -T_mean (degrees celcius)
# -T_max (degrees celcius)
# -T_min (degrees celcius)
# -rainfall (mm)
# -global radiation (per day in kJ/m2)
# -H2O partial pressure (hPa)
sourcefile = os.path.join(sourcefile)
ID = int(ID)
# import forcing data
fmi = pd.read_csv(sourcefile, sep=';', header='infer',
usecols=['OmaTunniste', 'Kunta', 'aika', 'longitude',
'latitude', 't_mean', 't_max', 't_min', 'rainfall',
'radiation', 'hpa', 'lamposumma_v', 'rainfall_v'],
parse_dates=['aika'],encoding="ISO-8859-1")
time = pd.to_datetime(fmi['aika'], format='%Y%m%d')
fmi.index = time
fmi = fmi.rename(columns={'OmaTunniste': 'ID', 'longitude': 'lon',
'latitude': 'lat', 't_mean': 'T', 't_max': 'Tmax',
't_min': 'Tmin', 'rainfall': 'Prec',
'radiation': 'Rg', 'hpa': 'h2o', 'lamposumma_v': 'dds',
'rainfall_v': 'Prec_a'})
# get desired period and catchment
fmi = fmi[(fmi.index >= start_date) & (fmi.index <= end_date)]
if ID > 0:
fmi = fmi[fmi['ID'] == ID]
fmi['h2o'] = 1e-1*fmi['h2o'] # hPa-->kPa
fmi['Rg'] = 1e3 / 86400.0*fmi['Rg'] # kJ/m2/d-1 to Wm-2
fmi['Par'] = 0.5*fmi['Rg']
# saturated vapor pressure
esa = 0.6112*np.exp((17.67*fmi['T']) / (fmi['T'] + 273.16 - 29.66)) # kPa
vpd = esa - fmi['h2o'] # kPa
vpd[vpd < 0] = 0.0
rh = 100.0*fmi['h2o'] / esa
rh[rh < 0] = 0.0
rh[rh > 100] = 100.0
fmi['RH'] = rh
fmi['esa'] = esa
fmi['VPD'] = vpd
fmi['doy'] = fmi.index.dayofyear
fmi = fmi.drop(['aika'], axis=1)
# replace nan's in prec with 0.0
fmi['Prec'][np.isnan(fmi['Prec'])] = 0.0
# add CO2 concentration to dataframe
fmi['CO2'] = float(CO2)
return fmi
""" ************ Get Runoffs from SVE catchments ******* """
def read_SVE_runoff(ID, start_date,end_date, sourcefile):
"""
reads FMI interpolated daily weather data from file
IN:
ID - sve catchment ID. str OR list of str (=many catchments)
start_date - 'yyyy-mm-dd'
end_date - 'yyyy-mm-dd'
OUT:
roff - pd.dataframe with datetimeindex
columns: measured runoff (mm/d)
if ID=str, then column is 'Qm'
if ID = list of str, then column is catchment ID
MISSING DATA = np.NaN
CODE: Samuli Launiainen (Luke, 7.2.2017)
"""
# Runoffs compiled from Hertta-database (Syke) and Metla/Luke old observations.
# Span: 1935-2015, missing data=-999.99
# File columns:
# pvm;14_Paunulanpuro;15_Katajaluoma;16_Huhtisuonoja;17_Kesselinpuro;18_Korpijoki;20_Vaarajoki;
# 22_Vaha-Askanjoki;26_Iittovuoma;24_Kotioja;27_Laanioja;1_Lompolojanganoja;10_Kelopuro;
# 13_Rudbacken;3_Porkkavaara;11_Hauklammenoja;19_Pahkaoja;21_Myllypuro;23_Ylijoki;2_Liuhapuro;
# 501_Kauheanpuro;502_Korsukorvenpuro;503_Kangasvaaranpuro;504_Kangaslammenpuro;56_Suopuro;
# 57_Valipuro;28_Kroopinsuo;30_Pakopirtti;31_Ojakorpi;32_Rantainrahka
# import data
sourcefile = os.path.join(sourcefile)
dat = pd.read_csv(sourcefile, sep=';', header='infer', parse_dates=['pvm'], index_col='pvm', na_values=-999)
# split column names so that they equal ID's
cols = [x.split("_")[0] for x in dat.columns]
dat.columns = cols
# get desired period & rename column ID to Qm
dat = dat[(dat.index >= start_date) & (dat.index <= end_date)]
dat = dat[ID]
if type(ID) is str:
dat.columns = ['Qm']
return dat
def preprocess_soildata(pbu, psoil, soiltype, cmask, spatial=True):
"""
creates input dictionary for initializing BucketGrid
Args:
bbu - bucket parameters dict
psoil - soiltype dict
soiltype - soiltype code classified into 5 groups
cmask - catchment mask
"""
# create dict for initializing soil bucket.
# copy pbu into sdata and make each value np.array(np.shape(cmask))
data = pbu.copy()
data.update((x, y*cmask) for x, y in data.items())
if spatial:
for key in psoil.keys():
c = psoil[key]['soil_id']
ix = np.where(soiltype == c)
data['poros'][ix] = psoil[key]['poros']
data['fc'][ix] = psoil[key]['fc']
data['wp'][ix] = psoil[key]['wp']
data['ksat'][ix] = psoil[key]['ksat']
data['beta'][ix] = psoil[key]['beta']
del ix
#data['soilcode'] = soiltype
return data
def read_catchment_data(ID, fpath, plotgrids=False, plotdistr=False):
"""
reads gis-data grids from selected catchments and returns numpy 2d-arrays
Args:
ID - catchment id (str)
fpath - full path to data folder (str)
plotgrids - True plots
Returns:
gis - dict of gis-data rasters and info. Keys (* marked used in spafhy):
cmask - catchment mask *
flowacc - flow accumulation *
slope - local slope (deg) *
soilclass - soil classification: 1=coarse, 2=medium, 3=fine, 4=peat *
LAI_pine, LAI_spruce - pine and spruce LAI (m2m-2)
LAI_conif - conifer total annual max LAI (m2m-2) *
LAI_dedid - deciduous annual max LAI (m2m-2) *
cf - canopy closure (-) *
hc - mean stand height (m)*
vol - stand volume (m3/ha)
ba - stand basal area (dm2/ha)
age - stand age (yr)
dem - elevation grid (m)
info - info dict *
lat0, lon0 - latitude and longiture vectors *
loc - outlet coordinates *
cellsize - grid cell size (m) *
"""
# specific leaf area (m2/kg) for converting leaf mass to leaf area
# SLA = {'pine': 5.54, 'spruce': 5.65, 'decid': 18.46} # m2/kg, Kellomäki et al. 2001 Atm. Env.
SLA = {'pine': 6.8, 'spruce': 4.7, 'decid': 14.0} # Härkönen et al. 2015 BER 20, 181-195
# dem, set values outside boundaries to NaN
dem, info, pos, cellsize, nodata = read_AsciiGrid(os.path.join(fpath, 'dem.dat'))
# latitude, longitude arrays
nrows, ncols = np.shape(dem)
lon0 = np.arange(pos[0], pos[0] + cellsize*ncols, cellsize)
lat0 = np.arange(pos[1], pos[1] + cellsize*nrows, cellsize)
lat0 = np.flipud(lat0) # why this is needed to get coordinates correct when plotting?
# catchment mask cmask ==1, np.NaN outside
cmask = dem.copy()
cmask[np.isfinite(cmask)] = 1.0
# flowacc, D-infinity, nr of draining cells
flowacc, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'flowacc.dat'))
# catchment outlet location and catchment mean elevation
(iy, ix) = np.where(flowacc == np.nanmax(flowacc))
loc = {'lat': lat0[iy], 'lon': lon0[ix], 'elev': np.nanmean(dem)}
# slope, degrees
slope, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'slope.dat'))
# twi: this is recomputed in Topmodel init
twi, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'twi.dat'))
# water bodies
# Maastotietokanta water bodies: 1=waterbody
stream, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'stream.dat'))
stream[np.isfinite(stream)] = 1.0
# soil classificication: 1=coarse, 2=medium, 3=fine, 4=peat -1=water
soilclass, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'soilclass.dat'))
# update catchment mask so that water bodies are left out (SL 20.2.18)
cmask[soilclass <= 0] = np.NaN
soilclass = soilclass * cmask
""" stand data (MNFI)"""
# stand volume [m3ha-1]
vol, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'vol.dat'), setnans=False)
vol = vol*cmask
# basal area
ba, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'ba.dat'), setnans=False)
ba = ba*cmask
# tree height [m]
hc, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'hc.dat'))
# canopy closure [-]
cf, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'cf.dat'))
# stand age [yrs]
age, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'age.dat'))
# leaf area indices
LAI_pine, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'LAI_pine.dat'))
LAI_spruce, _, _, _, _ = read_AsciiGrid(os.path.join(fpath,'LAI_spruce.dat'))
LAI_decid, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'LAI_decid.dat'))
# # leaf biomasses (10 kg/ha) and one-sided LAI (m2m-2)
# bmleaf_pine, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'bmleaf_pine.dat'))
# bmleaf_spruce, _, _, _, _ = read_AsciiGrid(os.path.join(fpath,'bmleaf_spruce.dat'))
# bmleaf_decid, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, 'bmleaf_decid.dat'))
#
# LAI_pine = 1e-3*bmleaf_pine*SLA['pine'] # 1e-3 converts 10kg/ha to kg/m2
# LAI_spruce = 1e-3*bmleaf_spruce*SLA['spruce']
# LAI_decid = 1e-3*bmleaf_decid*SLA['decid']
# dict of all rasters
gis = {'cmask': cmask, 'dem': dem, 'flowacc': flowacc, 'slope': slope,
'twi': twi, 'soilclass': soilclass, 'stream': stream,
'LAI_pine': LAI_pine, 'LAI_spruce': LAI_spruce,
'LAI_conif': LAI_pine + LAI_spruce,
'LAI_decid': LAI_decid, 'ba': ba, 'hc': hc,
'vol': vol, 'cf': cf, 'age': age, 'cellsize': cellsize,
'info': info, 'lat0': lat0, 'lon0': lon0, 'loc': loc
}
if plotgrids is True:
#%matplotlib qt
#xx,yy=np.meshgrid(lon0, lat0)
plt.close('all')
plt.figure()
plt.subplot(221);plt.imshow(dem); plt.colorbar(); plt.title('DEM (m)');plt.plot(ix,iy,'rs')
plt.subplot(222);plt.imshow(twi); plt.colorbar(); plt.title('TWI')
plt.subplot(223);plt.imshow(slope); plt.colorbar(); plt.title('slope(deg)')
plt.subplot(224);plt.imshow(flowacc); plt.colorbar(); plt.title('flowacc (m2)')
#
plt.figure()
plt.subplot(221); plt.imshow(soilclass); plt.colorbar(); plt.title('soiltype')
plt.subplot(222); plt.imshow(LAI_pine+LAI_spruce); plt.colorbar();
plt.title('LAI conif (m2/m2)')
plt.subplot(223); plt.imshow(LAI_decid); plt.colorbar();
plt.title('LAI decid (m2/m2)')
plt.subplot(224); plt.imshow(cf); plt.colorbar(); plt.title('cf (-)')
plt.figure()
plt.subplot(221);plt.imshow(vol); plt.colorbar(); plt.title('vol (m3/ha)')
plt.subplot(222);plt.imshow(hc); plt.colorbar(); plt.title('hc (m)')
plt.subplot(223);plt.imshow(ba); plt.colorbar(); plt.title('ba (m2/ha)')
plt.subplot(224);plt.imshow(age); plt.colorbar(); plt.title('age (yr)')
if plotdistr is True:
plt.figure()
#twi
twi0=twi[np.isfinite(twi)]; vol=vol[np.isfinite(vol)];
lai=LAI_pine + LAI_spruce + LAI_decid
lai=lai[np.isfinite(lai)];soil0=soilclass[np.isfinite(soilclass)]
plt.subplot(221); plt.hist(twi0,bins=100,color='b',alpha=0.5,normed=True);
plt.ylabel('f');plt.ylabel('twi')
s=np.unique(soil0); # print(s)
colcode='rgcym'
for k in range(0,len(s)):
# print(k)
a=twi[np.where(soilclass==s[k])]; a=a[np.isfinite(a)]
plt.hist(a,bins=50,alpha=0.5,color=colcode[k], normed=True,
label='soil ' +str(s[k]))
plt.legend(); plt.show()
plt.subplot(222); plt.hist(vol,bins=100,color='k',normed=True)
plt.ylabel('f');plt.ylabel('vol')
plt.subplot(223); plt.hist(lai,bins=100,color='g',normed=True)
plt.ylabel('f');plt.ylabel('lai')
plt.subplot(224); plt.hist(soil0, bins=5,color='r',normed=True)
plt.ylabel('f');plt.ylabel('soiltype')
return gis
""" ************ Reading and writing Ascii -grids ********* """
def read_AsciiGrid(fname, setnans=True):
""" reads AsciiGrid format in fixed format as below:
ncols 750
nrows 375
xllcorner 350000
yllcorner 6696000
cellsize 16
NODATA_value -9999
-9999 -9999 -9999 -9999 -9999
-9999 4.694741 5.537514 4.551162
-9999 4.759177 5.588773 4.767114
IN:
fname - filename (incl. path)
OUT:
data - 2D numpy array
info - 6 first lines as list of strings
(xloc,yloc) - lower left corner coordinates (tuple)
cellsize - cellsize (in meters?)
nodata - value of nodata in 'data'
Samuli Launiainen Luke 7.9.2016
"""
import numpy as np
fid = open(fname, 'r')
info = fid.readlines()[0:6]
fid.close()
# print info
# conversion to float is needed for non-integers read from file...
xloc = float(info[2].split(' ')[-1])
yloc = float(info[3].split(' ')[-1])
cellsize = float(info[4].split(' ')[-1])
nodata = float(info[5].split(' ')[-1])
# read rest to 2D numpy array
data = np.loadtxt(fname, skiprows=6)
if setnans is True:
data[data == nodata] = np.NaN
nodata = np.NaN
return data, info, (xloc, yloc), cellsize, nodata
def write_AsciiGrid(fname, data, info, fmt='%.18e'):
""" writes AsciiGrid format txt file
IN:
fname - filename
data - data (numpy array)
info - info-rows (list, 6rows)
fmt - output formulation coding
Samuli Launiainen Luke 7.9.2016
"""
import numpy as np
# replace nans with nodatavalue according to info
nodata = int(info[-1].split(' ')[-1])
data[np.isnan(data)] = nodata
# write info
fid = open(fname, 'w')
fid.writelines(info)
fid.close()
# write data
fid = open(fname, 'a')
np.savetxt(fid, data, fmt=fmt, delimiter=' ')
fid.close()
""" ********* Flatten 2d array with nans to dense 1d array ********** """
def matrix_to_array(x, nodata=None):
""" returns 1d array and their indices in original 2d array"""
s = np.shape(x)
if nodata is None: # Nan
ix = np.where(np.isfinite(x))
else:
ix = np.where(x != nodata)
y = x[ix].copy()
return y, ix, s
def array_to_matrix(y, ix, s, nodata=None):
"""returns 1d array reshaped into 2d array x of shape s"""
if nodata is None:
x = np.ones(s)*np.NaN
else:
x = np.ones(s)*nodata
x[ix] = y
return x
""" ****** Following are for personal use; not needed in demos ***** """
def inputs_netCDF(ID, fname, data):
"""
Store gridded data required by SpaFHy into netCDF
IN:
ID -catchment id as str
fname - filename
data - dict with keys:
cmask - catchment mask; integers within np.Nan outside
LAI_conif [m2m-2]
LAI_decid [m2m-2]
hc, canopy closure [m]
fc, canopy closure fraction [-]
soil, soil type integer code 1-5
flowacc - flow accumulation [units]
slope - local surface slope [units]
cellsize - gridcell size
lon0 - x-grid
lat0 - y-grid
OUT:
ncf - netCDF file handle. Initializes data
ff - netCDF filename incl. path
LAST EDIT 05.10.2018 / Samuli
"""
from netCDF4 import Dataset #, date2num, num2date
from datetime import datetime
print('**** creating SpaFHy input netCDF4 file: ' + fname + ' ****')
# create dataset & dimensions
ncf = Dataset(fname, 'w')
ncf.description = 'SpatialData from : ' + str(ID)
ncf.history = 'created ' + datetime.now().strftime('%Y-%m-%d %H:%M:%S')
ncf.source = 'SpaFHy v.1.0 inputs'
dlat, dlon = np.shape(data['cmask'])
ncf.createDimension('dlon', int(dlon))
ncf.createDimension('dlat', int(dlat))
ncf.createDimension('scalar', 1)
# create variables
# call as createVariable(varname,type,(dimensions))
cellsize = ncf.createVariable('cellsize', 'f4', ('scalar',))
cellsize.units = 'm'
lat = ncf.createVariable('lat', 'f4', ('dlat',))
lat.units = 'ETRS-TM35FIN'
lon = ncf.createVariable('lon', 'f4', ('dlon',))
lon.units = 'ETRS-TM35FIN'
cellsize[0] = data['cellsize']
lon[:] = data['lon0']
lat[:] = data['lat0']
# required inputs
cmask = ncf.createVariable('cmask', 'i4', ('dlat','dlon',))
cmask.units = 'integer inside catchment, Nan outside'
LAI_conif = ncf.createVariable('LAI_conif', 'f4', ('dlat','dlon',))
LAI_conif.units = 'conifer LAI (m2m-2)'
LAI_decid = ncf.createVariable('LAI_decid', 'f4', ('dlat','dlon',))
LAI_decid.units = 'deciduous annual max LAI (m2m-2)'
hc = ncf.createVariable('hc', 'f4', ('dlat','dlon',))
hc.units = 'canopy height m'
cf = ncf.createVariable('cf', 'f4', ('dlat','dlon',))
cf.units = 'canopy closure (-)'
soilclass = ncf.createVariable('soilclass', 'i4', ('dlat','dlon',))
soilclass.units = 'soil class (1 - 5)'
flowacc = ncf.createVariable('flowacc', 'f4', ('dlat','dlon',))
flowacc.units = 'flow accumualtion area m2'
slope = ncf.createVariable('slope', 'f4', ('dlat','dlon',))
slope.units = 'local slope (deg)'
for k in ['LAI_conif', 'LAI_decid', 'hc', 'cf', 'soilclass', 'flowacc', 'slope']:
ncf[k][:,:] = data[k]
print('**** done ****')
"""
***** SVE -valuma-alueet -- get gis data to create catchment ******
"""
def create_catchment(ID, fpath, plotgrids=False, plotdistr=False):
"""
reads gis-data grids from selected catchments and returns numpy 2d-arrays
IN:
ID - SVE catchment ID (int or str)
fpath - folder (str)
psoil - soil properties
plotgrids - True plots
OUT:
GisData - dictionary with 2d numpy arrays and some vectors/scalars.
keys [units]:'dem'[m],'slope'[deg],'soil'[coding 1-4], 'cf'[-],'flowacc'[m2], 'twi'[log m??],
'vol'[m3/ha],'ba'[m2/ha], 'age'[yrs], 'hc'[m], 'bmroot'[1000kg/ha],'LAI_pine'[m2/m2 one-sided],'LAI_spruce','LAI_decid',
'info','lat0'[latitude, euref_fin],'lon0'[longitude, euref_fin],loc[outlet coords,euref_fin],'cellsize'[cellwidth,m],
'peatm','stream','cmask','rockm'[masks, 1=True]
TODO (6.2.2017 Samuli):
mVMI-datan koodit >32766 ovat vesialueita ja ei-metsäalueita (tiet, sähkölinjat, puuttomat suot) käytä muita maskeja (maastotietokanta, kysy
Auralta tie + sähkölinjamaskit) ja IMPOSE LAI ja muut muuttujat ko. alueille. Nyt menevät no-data -luokkaan eikä oteta mukaan laskentaan.
"""
# fpath = os.path.join(fpath, str(ID) + '\\sve_' + str(ID) + '_')
fpath = os.path.join(fpath, str(ID))
bname = 'sve_' + str(ID) + '_'
print(fpath)
# specific leaf area (m2/kg) for converting leaf mass to leaf area
# SLA = {'pine': 5.54, 'spruce': 5.65, 'decid': 18.46} # m2/kg, Kellomäki et al. 2001 Atm. Env.
SLA = {'pine': 6.8, 'spruce': 4.7, 'decid': 14.0} # Härkönen et al. 2015 BER 20, 181-195
# values to be set for 'open peatlands' and 'not forest land'
nofor = {'vol': 0.1, 'ba': 0.01, 'height': 0.1, 'cf': 0.01, 'age': 0.0,
'LAIpine': 0.01, 'LAIspruce': 0.01, 'LAIdecid': 0.01, 'bmroot': 0.01,
'bmleaf': 0.01}
opeatl = {'vol': 0.01, 'ba': 0.01, 'height': 0.1, 'cf': 0.1, 'age': 0.0,
'LAIpine': 0.01, 'LAIspruce': 0.01, 'LAIdecid': 0.1, 'bmroot': 0.01}
# dem, set values outside boundaries to NaN
dem, info, pos, cellsize, nodata = read_AsciiGrid(os.path.join(fpath, bname + 'dem_16m_aggr.asc'))
# latitude, longitude arrays
nrows, ncols = np.shape(dem)
lon0 = np.arange(pos[0], pos[0] + cellsize*ncols, cellsize)
lat0 = np.arange(pos[1], pos[1] + cellsize*nrows, cellsize)
lat0 = np.flipud(lat0) # why this is needed to get coordinates correct when plotting?
# catchment mask cmask ==1, np.NaN outside
cmask = dem.copy()
cmask[np.isfinite(cmask)] = 1.0
# flowacc, D-infinity, nr of draining cells
flowacc, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'Flow_accum_D-Inf_grids.asc'))
flowacc = flowacc*cellsize**2 # in m2
# slope, degrees
slope, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'slope_16m.asc'))
# twi
twi, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'TWI_16m.asc'))
"""
Create soiltype grid and masks for waterbodies, streams, peatlands and rocks
"""
# Maastotietokanta water bodies: 1=waterbody
stream, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'vesielementit_mtk.asc'))
stream[np.isfinite(stream)] = 1.0
# maastotietokanta peatlandmask
peatm, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'suo_mtk.asc'))
peatm[np.isfinite(peatm)] = 1.0
# maastotietokanta kalliomaski
rockm, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'kallioalue_mtk.asc'))
rockm[np.isfinite(rockm)] = 1.0
"""
gtk soilmap: read and re-classify into 4 texture classes
#GTK-pintamaalaji grouped to 4 classes (Samuli Launiainen, Jan 7, 2017)
#Codes based on maalaji 1:20 000 AND ADD HERE ALSO 1:200 000
"""
CoarseTextured = [195213, 195314, 19531421, 195313, 195310]
MediumTextured = [195315, 19531521, 195215, 195214, 195601, 195411, 195112,
195311, 195113, 195111, 195210, 195110, 195312]
FineTextured = [19531521, 195412, 19541221, 195511, 195413, 195410,
19541321, 195618]
Peats = [195512, 195513, 195514, 19551822, 19551891, 19551892]
Water = [195603]
gtk_s, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'soil.asc'))
r, c = np.shape(gtk_s)
soil = np.ravel(gtk_s)
#del gtk_s
soil[np.in1d(soil, CoarseTextured)] = 1.0 # ; soil[f]=1; del f
soil[np.in1d(soil, MediumTextured)] = 2.0
soil[np.in1d(soil, FineTextured)] = 3.0
soil[np.in1d(soil, Peats)] = 4.0
soil[np.in1d(soil, Water)] = -1.0
# reshape back to original grid
soil = soil.reshape(r, c)
del r, c
soil[np.isfinite(peatm)] = 4.0
# update waterbody mask
ix = np.where(soil == -1.0)
stream[ix] = 1.0
# update catchment mask so that water bodies are left out (SL 20.2.18)
#cmask[soil == -1.0] = np.NaN
cmask[soil <= 0] = np.NaN
soil = soil * cmask
""" stand data (MNFI)"""
# stand volume [m3ha-1]
vol, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'tilavuus.asc'), setnans=False)
vol = vol*cmask
# indexes for cells not recognized in mNFI
ix_n = np.where((vol >= 32727) | (vol == -9999) ) # no satellite cover or not forest land: assign arbitrary values
ix_p = np.where((vol >= 32727) & (peatm == 1)) # open peatlands: assign arbitrary values
ix_w = np.where((vol >= 32727) & (stream == 1)) # waterbodies: leave out
cmask[ix_w] = np.NaN # NOTE: leaves waterbodies out of catchment mask
vol[ix_n] = nofor['vol']
vol[ix_p] = opeatl['vol']
vol[ix_w] = np.NaN
# basal area [m2 ha-1]
ba, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'ppa.asc') )
ba[ix_n] = nofor['ba']
ba[ix_p] = opeatl['ba']
ba[ix_w] = np.NaN
# tree height [m]
height, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'keskipituus.asc'))
height = 0.1*height # m
height[ix_n] = nofor['height']
height[ix_p] = opeatl['height']
height[ix_w] = np.NaN
# canopy closure [-]
cf, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'latvuspeitto.asc'))
cf = 1e-2*cf
cf[ix_n] = nofor['cf']
cf[ix_p] = opeatl['cf']
cf[ix_w] = np.NaN
# cfd, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'lehtip_latvuspeitto.asc'))
# cfd = 1e-2*cfd # percent to fraction
# stand age [yrs]
age, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname+'ika.asc'))
age[ix_n] = nofor['age']
age[ix_p] = opeatl['age']
age[ix_w] = np.NaN
# leaf biomasses and one-sided LAI
bmleaf_pine, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_manty_neulaset.asc'))
bmleaf_spruce, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_kuusi_neulaset.asc'))
bmleaf_decid, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_lehtip_neulaset.asc'))
bmleaf_pine[ix_n] = nofor['bmleaf']
bmleaf_spruce[ix_n] = nofor['bmleaf']
bmleaf_decid[ix_n] = nofor['bmleaf']
LAI_pine = 1e-3*bmleaf_pine*SLA['pine'] # 1e-3 converts 10kg/ha to kg/m2
LAI_pine[ix_n] = nofor['LAIpine']
LAI_pine[ix_p] = opeatl['LAIpine']
LAI_pine[ix_w] = np.NaN
LAI_spruce = 1e-3*bmleaf_spruce*SLA['spruce']
LAI_spruce[ix_n] = nofor['LAIspruce']
LAI_spruce[ix_p] = opeatl['LAIspruce']
LAI_spruce[ix_w] = np.NaN
LAI_decid = 1e-3*bmleaf_decid*SLA['decid']
LAI_decid[ix_n] = nofor['LAIdecid']
LAI_decid[ix_p] = opeatl['LAIdecid']
LAI_decid[ix_w] = np.NaN
bmroot_pine, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_manty_juuret.asc'))
bmroot_spruce, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_kuusi_juuret.asc'))
bmroot_decid, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_lehtip_juuret.asc'))
bmroot = 1e-2*(bmroot_pine + bmroot_spruce + bmroot_decid) # 1000 kg/ha
bmroot[ix_n] = nofor['bmroot']
bmroot[ix_p] = opeatl['bmroot']
bmroot[ix_w] = np.NaN
# site types
maintype, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'paatyyppi.asc'))
maintype = maintype*cmask
sitetype, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'kasvupaikka.asc'))
sitetype = sitetype*cmask
# catchment outlet location and catchment mean elevation
(iy, ix) = np.where(flowacc == np.nanmax(flowacc))
loc = {'lat': lat0[iy], 'lon': lon0[ix], 'elev': np.nanmean(dem)}
# dict of all rasters
GisData = {'cmask': cmask, 'dem': dem, 'flowacc': flowacc, 'slope': slope,
'twi': twi, 'gtk_soilcode': gtk_s, 'soilclass': soil, 'peatm': peatm, 'stream': stream,
'rockm': rockm, 'LAI_pine': LAI_pine, 'LAI_spruce': LAI_spruce,
'LAI_conif': LAI_pine + LAI_spruce,
'LAI_decid': LAI_decid, 'bmroot': bmroot, 'ba': ba, 'hc': height,
'vol': vol, 'cf': cf, 'age': age, 'maintype': maintype, 'sitetype': sitetype,
'cellsize': cellsize, 'info': info, 'lat0': lat0, 'lon0': lon0, 'loc': loc}
GisData['bmleaf_pine'] = bmleaf_pine
GisData['bmleaf_spruce'] = bmleaf_spruce
GisData['bmleaf_decid'] = bmleaf_decid
if plotgrids is True:
# %matplotlib qt
# xx, yy = np.meshgrid(lon0, lat0)
plt.close('all')
plt.figure()
plt.subplot(221)
plt.imshow(dem); plt.colorbar(); plt.title('DEM (m)')
plt.plot(ix, iy,'rs')
plt.subplot(222)
plt.imshow(twi); plt.colorbar(); plt.title('TWI')
plt.subplot(223)
plt.imshow(slope); plt.colorbar(); plt.title('slope(deg)')
plt.subplot(224)
plt.imshow(flowacc); plt.colorbar(); plt.title('flowacc (m2)')
plt.figure(figsize=(6, 14))
plt.subplot(221)
plt.imshow(soil); plt.colorbar(); plt.title('soiltype')
mask = cmask.copy()*0.0
mask[np.isfinite(peatm)] = 1
mask[np.isfinite(rockm)] = 2
mask[np.isfinite(stream)] = 3
plt.subplot(222)
plt.imshow(mask); plt.colorbar(); plt.title('masks')
plt.subplot(223)
plt.imshow(LAI_pine+LAI_spruce + LAI_decid); plt.colorbar(); plt.title('LAI (m2/m2)')
plt.subplot(224)
plt.imshow(cf); plt.colorbar(); plt.title('cf (-)')
plt.figure(figsize=(6,11))
plt.subplot(321)
plt.imshow(vol); plt.colorbar(); plt.title('vol (m3/ha)')
plt.subplot(323)
plt.imshow(height); plt.colorbar(); plt.title('hc (m)')
#plt.subplot(223)
#plt.imshow(ba); plt.colorbar(); plt.title('ba (m2/ha)')
plt.subplot(325)
plt.imshow(age); plt.colorbar(); plt.title('age (yr)')
plt.subplot(322)
plt.imshow(1e-3*bmleaf_pine); plt.colorbar(); plt.title('pine needles (kg/m2)')
plt.subplot(324)
plt.imshow(1e-3*bmleaf_spruce); plt.colorbar(); plt.title('spruce needles (kg/m2)')
plt.subplot(326)
plt.imshow(1e-3*bmleaf_decid); plt.colorbar(); plt.title('decid. leaves (kg/m2)')
if plotdistr is True:
twi0 = twi[np.isfinite(twi)]
vol = vol[np.isfinite(vol)]
lai = LAI_pine + LAI_spruce + LAI_decid
lai = lai[np.isfinite(lai)]
soil0 = soil[np.isfinite(soil)]
plt.figure(100)
plt.subplot(221)
plt.hist(twi0, bins=100, color='b', alpha=0.5, normed=True)
plt.ylabel('f');plt.ylabel('twi')
s = np.unique(soil0)
colcode = 'rgcym'
for k in range(0,len(s)):
# print k
a = twi[np.where(soil==s[k])]
a = a[np.isfinite(a)]
plt.hist(a, bins=50, alpha=0.5, color=colcode[k], normed=True, label='soil ' +str(s[k]))
plt.legend()
plt.show()
plt.subplot(222)
plt.hist(vol, bins=100, color='k', normed=True); plt.ylabel('f'); plt.ylabel('vol')
plt.subplot(223)
plt.hist(lai, bins=100, color='g', normed=True); plt.ylabel('f'); plt.ylabel('lai')
plt.subplot(224)
plt.hist(soil0, bins=5, color='r', normed=True); plt.ylabel('f');plt.ylabel('soiltype')
return GisData
# specific for MEOLO-sites
""" ****************** creates gisdata dictionary from Vihti-koealue ************************ """
def create_vihti_catchment(ID='Vihti', fpath='c:\\projects\\fotetraf\\spathy\\data', plotgrids=False, plotdistr=False):
"""
reads gis-data grids from selected catchments and returns numpy 2d-arrays
IN:
ID - SVE catchment ID (int or str)
fpath - folder (str)
plotgrids - True plots
OUT:
GisData - dictionary with 2d numpy arrays and some vectors/scalars.
keys [units]:'dem'[m],'slope'[deg],'soil'[coding 1-4], 'cf'[-],'flowacc'[m2], 'twi'[log m??],
'vol'[m3/ha],'ba'[m2/ha], 'age'[yrs], 'hc'[m], 'bmroot'[1000kg/ha],'LAI_pine'[m2/m2 one-sided],'LAI_spruce','LAI_decid',
'info','lat0'[latitude, euref_fin],'lon0'[longitude, euref_fin],loc[outlet coords,euref_fin],'cellsize'[cellwidth,m],
'peatm','stream','cmask','rockm'[masks, 1=True]
TODO (6.2.2017 Samuli):
mVMI-datan koodit >32766 ovat vesialueita ja ei-metsäalueita (tiet, sähkölinjat, puuttomat suot) käytä muita maskeja (maastotietokanta, kysy
Auralta tie + sähkölinjamaskit) ja IMPOSE LAI ja muut muuttujat ko. alueille. Nyt menevät no-data -luokkaan eikä oteta mukaan laskentaan.
"""
from iotools import read_AsciiGrid
fpath=os.path.join(fpath,str(ID)+'_')
#specific leaf area (m2/kg) for converting leaf mass to leaf area
# SLA={'pine':5.54, 'spruce': 5.65, 'decid': 18.46} #m2/kg, Kellomäki et al. 2001 Atm. Env.
SLA = {'pine': 6.8, 'spruce': 4.7, 'decid': 14.0} # Härkönen et al. 2015 BER 20, 181-195
#values to be set for 'open peatlands' and 'not forest land'
nofor={'vol':0.1, 'ba':0.01, 'height':0.1, 'cf': 0.01, 'age': 0.0, 'LAIpine': 0.01, 'LAIspruce':0.01, 'LAIdecid': 0.01, 'bmroot':0.01}
opeatl={'vol':0.01, 'ba':0.01, 'height':0.1, 'cf': 0.1, 'age': 0.0, 'LAIpine': 0.01, 'LAIspruce':0.01, 'LAIdecid': 0.01, 'bmroot':0.01}
#dem, set values outside boundaries to NaN
dem, info, pos, cellsize, nodata = read_AsciiGrid(fpath+'dem_16m.asc')
#latitude, longitude arrays
nrows, ncols=np.shape(dem)
lon0=np.arange(pos[0], pos[0]+cellsize*ncols,cellsize)
lat0=np.arange(pos[1], pos[1]+cellsize*nrows,cellsize)
lat0=np.flipud(lat0) #why this is needed to get coordinates correct when plotting?
#catchment mask cmask ==1, np.NaN outside
cmask=dem.copy(); cmask[np.isfinite(cmask)]=1.0
#flowacc, D-infinity, nr of draining cells
flowacc, _, _, _, _ = read_AsciiGrid(fpath +'flowaccum_16m.asc')
conv = np.nanmin(flowacc) # to correct units in file
flowacc = flowacc / conv *cellsize**2 #in m2
#slope, degrees
slope, _, _, _, _ = read_AsciiGrid(fpath + 'slope_16m.asc')
#twi
twi, _, _, _, _ = read_AsciiGrid(fpath + 'twi_16m.asc')
#Maastotietokanta water bodies: 1=waterbody
stream, _, _, _, _ = read_AsciiGrid(fpath +'vesielementit_1_0.asc')
stream[stream == 0.0] = np.NaN
stream[np.isfinite(stream)]=1.0
#maastotietokanta peatlandmask
#peatm, _, _, _, _ = read_AsciiGrid(fpath + 'suo_mtk.asc')
peatm = np.ones([nrows, ncols])*np.NaN
#peatm[np.isfinite(peatm)]=1.0
#maastotietokanta kalliomaski
#rockm, _, _, _, _ = read_AsciiGrid(fpath +'kallioalue_mtk.asc')
#rockm[np.isfinite(rockm)]=1.0
rockm = peatm.copy()
""" stand data (MNFI)"""
#stand volume [m3ha-1]
vol, _, _, _, _ = read_AsciiGrid(fpath +'tilavuus.asc', setnans=False)
vol=vol*cmask
#indexes for cells not recognized in mNFI
ix_n=np.where((vol>=32727) | (vol==-9999) ) #no satellite cover or not forest land: assign arbitrary values
ix_p=np.where((vol>=32727) & (peatm==1))#open peatlands: assign arbitrary values
ix_w=np.where((vol>=32727) & (stream==1)) #waterbodies: leave out
cmask[ix_w]=np.NaN #*********** NOTE: leave waterbodies out of catchment mask !!!!!!!!!!!!!!!!!!!!!!
vol[ix_n]=nofor['vol']; vol[ix_p]=opeatl['vol']; vol[ix_w]=np.NaN
#basal area [m2 ha-1]
ba, _, _, _, _ = read_AsciiGrid(fpath +'ppa.asc')
ba[ix_n]=nofor['ba']; ba[ix_p]=opeatl['ba']; ba[ix_w]=np.NaN
#tree height [m]
height, _, _, _, _ = read_AsciiGrid(fpath +'keskipituus.asc')
height=0.1*height #m
height[ix_n]=nofor['height']; height[ix_p]=opeatl['height']; height[ix_w]=np.NaN
#canopy closure [-]
cf, _, _, _, _ = read_AsciiGrid(fpath +'latvuspeitto.asc')
cfd, _, _, _, _ = read_AsciiGrid(fpath +'lehtip_latvuspeitto.asc')
cf=1e-2*cf; cfd=1e-2*cfd; #in fraction
cf[ix_n]=nofor['cf']; cf[ix_p]=opeatl['cf']; cf[ix_w]=np.NaN
#stand age [yrs]
age, _, _, _, _ = read_AsciiGrid(fpath +'ika.asc')
age[ix_n]=nofor['age']; age[ix_p]=opeatl['age']; age[ix_w]=np.NaN
#leaf biomasses and one-sided LAI
bmleaf_pine, _, _, _, _ = read_AsciiGrid(fpath +'bm_manty_neulaset.asc')
bmleaf_spruce, _, _, _, _ = read_AsciiGrid(fpath +'bm_kuusi_neulaset.asc')
bmleaf_decid, _, _, _, _ = read_AsciiGrid(fpath +'bm_lehtip_neulaset.asc')
# bmleaf_pine[ix_n]=np.NaN; bmleaf_spruce[ix_n]=np.NaN; bmleaf_decid[ix_n]=np.NaN;
LAI_pine=1e-3*bmleaf_pine*SLA['pine'] #1e-3 converts 10kg/ha to kg/m2
LAI_pine[ix_n]=nofor['LAIpine']; LAI_pine[ix_p]=opeatl['LAIpine']; age[ix_w]=np.NaN
LAI_spruce=1e-3*bmleaf_spruce*SLA['spruce'] #1e-3 converts 10kg/ha to kg/m2
LAI_spruce[ix_n]=nofor['LAIspruce']; LAI_spruce[ix_p]=opeatl['LAIspruce']; age[ix_w]=np.NaN
LAI_conif = LAI_spruce + LAI_pine
LAI_decid=1e-3*bmleaf_decid*SLA['decid'] #1e-3 converts 10kg/ha to kg/m2
LAI_decid[ix_n]=nofor['LAIdecid']; LAI_decid[ix_p]=opeatl['LAIdecid']; age[ix_w]=np.NaN
bmroot_pine, _, _, _, _ = read_AsciiGrid(fpath +'bm_manty_juuret.asc')
bmroot_spruce, _, _, _, _ = read_AsciiGrid(fpath +'bm_kuusi_juuret.asc')
bmroot_decid, _, _, _, _ = read_AsciiGrid(fpath +'bm_lehtip_juuret.asc')
bmroot=1e-2*(bmroot_pine + bmroot_spruce + bmroot_decid) #1000 kg/ha
bmroot[ix_n]=nofor['bmroot']; bmroot[ix_p]=opeatl['bmroot']; age[ix_w]=np.NaN
"""
gtk soilmap: read and re-classify into 4 texture classes
#GTK-pintamaalaji grouped to 4 classes (Samuli Launiainen, Jan 7, 2017)
#Codes based on maalaji 1:20 000 AND ADD HERE ALSO 1:200 000
"""
CoarseTextured = [195213,195314,19531421,195313,195310]
MediumTextured = [195315,19531521,195215,195214,195601,195411,195112,195311,195113,195111,195210,195110,195312]
FineTextured = [19531521, 195412,19541221,195511,195413,195410,19541321,195618]
Peats = [195512,195513,195514,19551822,19551891,19551892]
Water =[195603]
gtk_s, _, _, _, _ = read_AsciiGrid(fpath +'soil.asc')
r,c=np.shape(gtk_s);
soil=np.ravel(gtk_s); del gtk_s
soil[np.in1d(soil, CoarseTextured)]=1.0 #; soil[f]=1; del f
soil[np.in1d(soil, MediumTextured)]=2.0
soil[np.in1d(soil, FineTextured)]=3.0
soil[np.in1d(soil, Peats)]=4.0
soil[np.in1d(soil, Water)]=-1.0