def thorn(year, latitude, df, model):
data = df.loc[df['yil'] == year][model].values
# lat = 37
lat = deg2rad(latitude)
mmdlh = monthly_mean_daylight_hours(lat, year)
evapo = np.array(thornthwaite(data, mmdlh))
return evapo
def main(ref_raster_filepath, year, dst_filepath, align, buffer_dist):
logger = logging.getLogger(__name__)
suhi.settings.METEOSWISS_S3_CLIENT_KWARGS = {
'endpoint_url': environ.get('S3_ENDPOINT_URL')
}
with rio.open(ref_raster_filepath) as src:
# get the (valid data) extent of the raster
ref_raster_extent_geom = [
geometry.shape(geom)
for geom, val in features.shapes(src.dataset_mask(),
transform=src.transform)
if val != 0
][0]
# get the monthly mean temperature data array of the reference year for
# the raster extent
monthly_T_da = suhi.open_meteoswiss_s3_ds(
year,
'TabsD',
geom=ref_raster_extent_geom.buffer(buffer_dist),
crs=src.crs.to_string(),
roi=False)['TabsD']
attrs = monthly_T_da.attrs.copy()
monthly_T_da = monthly_T_da.resample(time='1MS').mean(dim='time')
# select the hottest month
hottest_month_i = monthly_T_da.mean(dim=['x', 'y']).argmax()
hottest_month_T_da = monthly_T_da.isel(time=hottest_month_i)
# hottest_month_T_da.attrs['pyproj_srs'] = attrs['pyproj_srs']
# potential evapotranspiration (Thornthwaite, 1948)
year_heat_index = monthly_T_da.groupby('time').apply(
lambda temp: (temp / 5)**1.514).sum(dim='time')
year_alpha = .49239 + .01792 * year_heat_index - \
.0000771771 * year_heat_index**2 + .000000675 * year_heat_index**3
pe_da = xr.full_like(hottest_month_T_da, np.nan)
pe_da = pe_da.where(
~(hottest_month_T_da >= 26.5),
-415.85 + 32.24 * hottest_month_T_da - .43 * hottest_month_T_da**2)
pe_da = pe_da.where(
~((hottest_month_T_da > 0) & (hottest_month_T_da < 26.5)),
16 * ((10 * (hottest_month_T_da / year_heat_index))**year_alpha))
pe_da = pe_da.where(~(hottest_month_T_da <= 0), 0)
pe_da *= pyeto.monthly_mean_daylight_hours(JONCTION_LAT)[
hottest_month_i.item()] / 12
pe_da.attrs['pyproj_srs'] = attrs['pyproj_srs']
if align:
# align it to the reference raster
ref_ds = salem.open_xr_dataset(ref_raster_filepath)
pe_da = suhi.align_ds(pe_da, ref_ds)
# write the raster
pe_da.rio.set_crs(pe_da.attrs['pyproj_srs']).rio.to_raster(dst_filepath)
logger.info("dumped reference evapotranspiration raster to %s",
dst_filepath)
def thorn_clima(year, latitude, df, model, senario):
data = df.loc[(df['Yıl'] == year) & (df['Model'] == model) &
(df['Senaryo'] == senario), 'Ortalama_Sıcaklık'].values
# lat = 37
if len(data) != 12:
data = data[0:12]
lat = deg2rad(latitude)
mmdlh = monthly_mean_daylight_hours(lat, year)
evapo = np.array(thornthwaite(data, mmdlh))
return data, evapo
def main(lat, temp_mean, precip_mean, max_tsmd):
'''get evapotranspiration''' # taken from Richards pyeto, see doc in same folder for more informatios
pyeto_lat = pyeto.deg2rad(lat) # converts the degree to radians
mean_month_list = [temp_mean[i] for i in temp_mean]
monthly_mean_daylight = pyeto.monthly_mean_daylight_hours(pyeto_lat)
eto = pyeto.thornthwaite(mean_month_list, monthly_mean_daylight)
'''make a "month_number" : "evapo" dic'''
eto_dict = {}
for n,i in enumerate(eto):
eto_dict[n+1] = eto[n]
print "\nWaterbalance with maximum deficientcy of", max_tsmd
acc_TSMD = {}
acc_factor = 0
budget = {}
'''The following part is still a bit hacky.
Two for loops that first calculates the the water buget for every mongth
and then starts a second loop to substract the value of the month before from the current month
'''
print "month \t rain \t eto \t\t TSMD"
for month, rain, etom in zip(range(1,13),precip_mean, eto_dict):
TSMD = precip_mean[rain] - ( eto_dict[etom] * 0.75 )
if TSMD <= max_tsmd:
TSMD = max_tsmd
print month,"\t", precip_mean[rain], "\t", eto_dict[etom],"\t", TSMD
budget[month] = TSMD if TSMD < 0 else 0
for month, rain, etom in zip(range(1,13),precip_mean, eto_dict):
TSMD = precip_mean[rain] - ( eto_dict[etom] * 0.75 )
if month == 1:
acc_TSMD[month] = TSMD + budget[12]
else:
acc_TSMD[month] = TSMD + budget[month -1]
if acc_TSMD[month] > 0 :
acc_TSMD[month] = 0
if acc_TSMD[month] < max_tsmd:
acc_TSMD[month] = max_tsmd
print "\nWater budget"
p(budget)
print ""
return acc_TSMD
import pandas as pd
import numpy as np
import os
from pyeto import thornthwaite, monthly_mean_daylight_hours, deg2rad
os.chdir(r"C:\Users\PC3\Desktop\Hydro")
df = pd.read_csv('data.csv')
df['Date'] = pd.to_datetime(df['Date'])
df['Year'] = df['Date'].dt.year
df = df.set_index('Date')
df_m = df.resample("M").mean()
data = []
lat = deg2rad(41.3)
for i in range(2015,2019):
mmdlh = monthly_mean_daylight_hours(lat, i)
monthly_t = df_m['Temp'][(df_m.Year == i)]
PET = thornthwaite(monthly_t.values.tolist(), mmdlh)
data.append(PET)
print("a")
def test_monthly_mean_daylight_hours(self):
# Test against values for latitude 20 deg N from Bautista et al (2009)
# Calibration of the equations of Hargreaves and Thornthwaite to
# estimate the potential evapotranspiration in semi-arid and subhumid
# tropical climates for regional applications. Atmosfera 22(4), 331-
# 348.
test_mmdlh = [
10.9, # Jan
11.3, # Feb
11.9, # Mar
12.5, # Apr
12.9, # May
13.2, # Jun
13.1, # Jul
12.7, # Aug
12.1, # Sep
11.5, # Oct
11.0, # Nov
10.8, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(20.0))
# Values were only quoted to 1 decimal place so check they are accurate
# to within 12 minutes (0.2 hours)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], test_mmdlh[m], delta=0.15)
# Test against values for latitude 46 deg N from Mimikou M. and
# Baltas E., Technical hydrology, Second edition, NTUA, 2002.
# cited in PAPADOPOULOU E., VARANOU E., BALTAS E., DASSAKLIS A., and
# MIMIKOU M. (2003) ESTIMATING POTENTIAL EVAPOTRANSPIRATION AND ITS
# SPATIAL DISTRIBUTION IN GREECE USING EMPIRICAL METHODS.
test_mmdlh = [
8.9, # Jan
10.1, # Feb
11.6, # Mar
13.3, # Apr
14.7, # May
15.5, # Jun
15.2, # Jul
13.9, # Aug
12.3, # Sep
10.7, # Oct
9.2, # Nov
8.5, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(46.0))
# Values were only quoted to 1 decimal place so check they are accurate
# to within 12 minutes (0.2 hours)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], test_mmdlh[m], delta=0.15)
# Test against values obtained for Los Angles, California,
# latitude 34 deg 05' N, from
# http://aa.usno.navy.mil/data/docs/Dur_OneYear.php
latitude = pyeto.deg2rad(34.0833333)
la_mmdlh = [
10.182, # Jan
10.973, # Feb
11.985, # Mar
13.046, # Apr
13.940, # May
14.388, # Jun
14.163, # Jul
13.404, # Aug
12.374, # Sep
11.320, # Oct
10.401, # Nov
9.928, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(latitude)
# Check that the 2 methods are almost the same (within 15 minutes)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], la_mmdlh[m], delta=0.25)
# Test with year set to a non-leap year
non_leap = pyeto.monthly_mean_daylight_hours(latitude, 2015)
for m in range(12):
self.assertEqual(mmdlh[m], non_leap[m])
# Test with year set to a leap year
leap = pyeto.monthly_mean_daylight_hours(latitude, 2016)
for m in range(12):
if m == 0:
self.assertEqual(leap[m], non_leap[m])
elif m == 1: # Feb
# Because Feb extends further into year in a leap year it
# should have a slightly longer mean day length in northern
# hemisphere
self.assertGreater(leap[m], non_leap[m])
else:
# All months after Feb in a lieap year will be composed of
# diffent Julian days (days of the year) compared to a
# non-leap year so will have different mean daylengths.
self.assertNotEqual(leap[m], non_leap[m])
# Test with bad latitude
with self.assertRaises(ValueError):
_ = pyeto.monthly_mean_daylight_hours(
pyeto.deg2rad(90.01))
with self.assertRaises(ValueError):
_ = pyeto.monthly_mean_daylight_hours(
pyeto.deg2rad(-90.01))
# Test limits of latitude
_ = pyeto.monthly_mean_daylight_hours(
pyeto.deg2rad(90.0))
_ = pyeto.monthly_mean_daylight_hours(
pyeto.deg2rad(-90.0))
def test_monthly_mean_daylight_hours(self):
# Test against values for latitude 20 deg N from Bautista et al (2009)
# Calibration of the equations of Hargreaves and Thornthwaite to
# estimate the potential evapotranspiration in semi-arid and subhumid
# tropical climates for regional applications. Atmosfera 22(4), 331-
# 348.
test_mmdlh = [
10.9, # Jan
11.3, # Feb
11.9, # Mar
12.5, # Apr
12.9, # May
13.2, # Jun
13.1, # Jul
12.7, # Aug
12.1, # Sep
11.5, # Oct
11.0, # Nov
10.8, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(20.0))
# Values were only quoted to 1 decimal place so check they are accurate
# to within 12 minutes (0.2 hours)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], test_mmdlh[m], delta=0.15)
# Test against values for latitude 46 deg N from Mimikou M. and
# Baltas E., Technical hydrology, Second edition, NTUA, 2002.
# cited in PAPADOPOULOU E., VARANOU E., BALTAS E., DASSAKLIS A., and
# MIMIKOU M. (2003) ESTIMATING POTENTIAL EVAPOTRANSPIRATION AND ITS
# SPATIAL DISTRIBUTION IN GREECE USING EMPIRICAL METHODS.
test_mmdlh = [
8.9, # Jan
10.1, # Feb
11.6, # Mar
13.3, # Apr
14.7, # May
15.5, # Jun
15.2, # Jul
13.9, # Aug
12.3, # Sep
10.7, # Oct
9.2, # Nov
8.5, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(46.0))
# Values were only quoted to 1 decimal place so check they are accurate
# to within 12 minutes (0.2 hours)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], test_mmdlh[m], delta=0.15)
# Test against values obtained for Los Angles, California,
# latitude 34 deg 05' N, from
# http://aa.usno.navy.mil/data/docs/Dur_OneYear.php
latitude = pyeto.deg2rad(34.0833333)
la_mmdlh = [
10.182, # Jan
10.973, # Feb
11.985, # Mar
13.046, # Apr
13.940, # May
14.388, # Jun
14.163, # Jul
13.404, # Aug
12.374, # Sep
11.320, # Oct
10.401, # Nov
9.928, # Dec
]
mmdlh = pyeto.monthly_mean_daylight_hours(latitude)
# Check that the 2 methods are almost the same (within 15 minutes)
for m in range(12):
self.assertAlmostEqual(mmdlh[m], la_mmdlh[m], delta=0.25)
# Test with year set to a non-leap year
non_leap = pyeto.monthly_mean_daylight_hours(latitude, 2015)
for m in range(12):
self.assertEqual(mmdlh[m], non_leap[m])
# Test with year set to a leap year
leap = pyeto.monthly_mean_daylight_hours(latitude, 2016)
for m in range(12):
if m == 0:
self.assertEqual(leap[m], non_leap[m])
elif m == 1: # Feb
# Because Feb extends further into year in a leap year it
# should have a slightly longer mean day length in northern
# hemisphere
self.assertGreater(leap[m], non_leap[m])
else:
# All months after Feb in a lieap year will be composed of
# diffent Julian days (days of the year) compared to a
# non-leap year so will have different mean daylengths.
self.assertNotEqual(leap[m], non_leap[m])
# Test with bad latitude
with self.assertRaises(ValueError):
_ = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(90.01))
with self.assertRaises(ValueError):
_ = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(-90.01))
# Test limits of latitude
_ = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(90.0))
_ = pyeto.monthly_mean_daylight_hours(pyeto.deg2rad(-90.0))
