def test_simple(self):
ts = get_local_testdata("era5/tas_pr_20180101-20180108.nc")
model = HMETS()
model(
ts=ts,
params=params,
start_date=dt.datetime(2018, 1, 1),
end_date=dt.datetime(2018, 1, 3),
run_name="test-hmets-era5",
area=4250.6,
elevation=843.0,
latitude=54.4848,
longitude=-123.3659,
rain_snow_fraction="RAINSNOW_DINGMAN",
tas={
"scale": 1.0,
"offset": -273.15,
"time_shift": -0.25
},
pr={
"scale": 24000.0,
"offset": 0.0,
"time_shift": -0.25
},
)
def test_simple(self):
params = (
9.5019,
0.2774,
6.3942,
0.6884,
1.2875,
5.4134,
2.3641,
0.0973,
0.0464,
0.1998,
0.0222,
-1.0919,
2.6851,
0.3740,
1.0000,
0.4739,
0.0114,
0.0243,
0.0069,
310.7211,
916.1947,
)
ts = get_local_testdata("nrcan/NRCAN_2006-2007_subset.nc")
start_date = dt.datetime(2006, 1, 1)
end_date = dt.datetime(2007, 12, 31)
model = HMETS()
model(
ts=ts,
params=params,
start_date=start_date,
end_date=end_date,
name="Salmon",
run_name="test-hmets-NRCAN",
area=4250.6,
elevation=843.0,
latitude=54.4848,
longitude=-123.3659,
rain_snow_fraction="RAINSNOW_DINGMAN",
tasmax={"linear_transform": (1.0, -273.15)},
tasmin={"linear_transform": (1.0, -273.15)},
pr={"linear_transform": (86400, 0.0)},
)
def test_simple(self):
model = HMETS()
params = (
9.5019,
0.2774,
6.3942,
0.6884,
1.2875,
5.4134,
2.3641,
0.0973,
0.0464,
0.1998,
0.0222,
-1.0919,
2.6851,
0.3740,
1.0000,
0.4739,
0.0114,
0.0243,
0.0069,
310.7211,
916.1947,
)
model(
TS,
start_date=dt.datetime(2000, 1, 1),
end_date=dt.datetime(2002, 1, 1),
area=4250.6,
elevation=843.0,
latitude=54.4848,
longitude=-123.3659,
params=params,
suppress_output=True,
)
d = model.diagnostics
np.testing.assert_almost_equal(d["DIAG_NASH_SUTCLIFFE"], -3.0132, 4)
def test_simple(self):
model = HMETS_OST()
model.configure(
get_local_testdata("ostrich-gr4j-cemaneige/OstRandomNumbers.txt"))
# Parameter bounds
low = (
0.3,
0.01,
0.5,
0.15,
0.0,
0.0,
-2.0,
0.01,
0.0,
0.01,
0.005,
-5.0,
0.0,
0.0,
0.0,
0.0,
0.00001,
0.0,
0.00001,
0.0,
0.0,
)
high = (
20.0,
5.0,
13.0,
1.5,
20.0,
20.0,
3.0,
0.2,
0.1,
0.3,
0.1,
2.0,
5.0,
1.0,
3.0,
1.0,
0.02,
0.1,
0.01,
0.5,
2.0,
)
model(
TS,
start_date=dt.datetime(1954, 1, 1),
duration=208,
area=4250.6,
elevation=843.0,
latitude=54.4848,
longitude=-123.3659,
lowerBounds=low,
upperBounds=high,
algorithm="DDS",
random_seed=0,
max_iterations=10,
)
d = model.diagnostics
np.testing.assert_almost_equal(d["DIAG_NASH_SUTCLIFFE"], -1.43474, 4)
opt_para = model.optimized_parameters
opt_func = model.obj_func
# # Random number seed: 123
# # Budget: 50
# # Algorithm: DDS
# # :StartDate 1954-01-01 00:00:00
# # :Duration 20819
# np.testing.assert_almost_equal( opt_para, [0.3243268,3.034247,407.2890,2.722774,12.18124,0.9468769], 4,
# err_msg='calibrated parameter set is not matching expected value')
# np.testing.assert_almost_equal( opt_func, -0.5779910, 4,
# err_msg='calibrated NSE is not matching expected value')
#
# Random number seed: 123 #
# Budget: 10 # This is the setup used for testing:
# Algorithm: DDS # shorter sim-period and lower budget
# :StartDate 1954-01-01 00:00:00 # First tested that example below matches
# :Duration 208 #
expected_value = [
1.777842e01,
3.317211e00,
5.727342e00,
1.419491e00,
1.382141e01,
1.637954e01,
7.166296e-01,
1.389346e-01,
2.620464e-02,
2.245525e-01,
2.839426e-02,
-2.003810e00,
9.479623e-01,
4.803857e-01,
2.524914e00,
4.117232e-01,
1.950058e-02,
4.494123e-02,
1.405815e-03,
2.815803e-02,
1.007823e00,
]
np.testing.assert_almost_equal(
opt_para,
expected_value,
4,
err_msg="calibrated parameter set is not matching expected value",
)
np.testing.assert_almost_equal(
opt_func,
1.43474,
4,
err_msg="calibrated NSE is not matching expected value",
)
# # Random number seed: 123 #
# # Budget: 50 # This is the setup in the Wiki:
# # Algorithm: DDS # https://github.com/Ouranosinc/raven/wiki/
# # :StartDate 1954-01-01 00:00:00 # Technical-Notes#example-setups-for-hmets
# # :Duration 20819 #
# np.testing.assert_almost_equal(opt_para, [5.008045E+00, 7.960246E-02, 4.332698E+00, 4.978125E-01,
# 1.997029E+00, 6.269773E-01, 1.516961E+00, 8.180383E-02,
# 6.730663E-02, 2.137822E-02, 2.097163E-02, 1.773348E+00,
# 3.036039E-01, 1.928524E-02, 1.758471E+00, 8.942299E-01,
# 8.741980E-03, 5.036474E-02, 9.465804E-03, 1.851839E-01,
# 1.653934E-01, 2.624006E+00, 8.868485E-02, 9.259195E+01,
# 8.269670E+01], 4,
# err_msg='calibrated parameter set is not matching expected value')
# np.testing.assert_almost_equal(opt_func, -6.350490E-01, 4,
# err_msg='calibrated NSE is not matching expected value')
hmets = HMETS()
hmets(
TS,
start_date=dt.datetime(1954, 1, 1),
duration=208,
area=4250.6,
elevation=843.0,
latitude=54.4848,
longitude=-123.3659,
params=model.calibrated_params,
)
np.testing.assert_almost_equal(
hmets.diagnostics["DIAG_NASH_SUTCLIFFE"], d["DIAG_NASH_SUTCLIFFE"],
4)
from . import wpsio as wio
# Defaults for this process
params_defaults = HMETS.params(
GAMMA_SHAPE=9.5019,
GAMMA_SCALE=0.2774,
GAMMA_SHAPE2=6.3942,
GAMMA_SCALE2=0.6884,
MIN_MELT_FACTOR=1.2875,
MAX_MELT_FACTOR=5.4134,
DD_MELT_TEMP=2.3641,
DD_AGGRADATION=0.0973,
SNOW_SWI_MIN=0.0464,
SNOW_SWI_MAX=0.1998,
SWI_REDUCT_COEFF=0.0222,
DD_REFREEZE_TEMP=-1.0919,
REFREEZE_FACTOR=2.6851,
REFREEZE_EXP=0.3740,
PET_CORRECTION=1.0000,
HMETS_RUNOFF_COEFF=0.4739,
PERC_COEFF=0.0114,
BASEFLOW_COEFF_1=0.0243,
BASEFLOW_COEFF_2=0.0069,
TOPSOIL=310.7211,
PHREATIC=916.1947,
)
params = LiteralInput(
"params",
"Comma separated list of model parameters",