def region_model_repository(self):
"""
Returns
-------
- RegionModelRepository - configured with 'Tistel-ptgsk' etc.
"""
id_list = [1225]
# parameters can be loaded from yaml_config Model parameters..
pt_params = api.PriestleyTaylorParameter(
) # *params["priestley_taylor"])
gs_params = api.GammaSnowParameter() # *params["gamma_snow"])
ss_params = api.SkaugenParameter()
ae_params = api.ActualEvapotranspirationParameter(
) # *params["act_evap"])
k_params = api.KirchnerParameter() # *params["kirchner"])
p_params = api.PrecipitationCorrectionParameter(
) # TODO; default 1.0, is it used ??
ptgsk_rm_params = pt_gs_k.PTGSKParameter(pt_params, gs_params,
ae_params, k_params,
p_params)
ptssk_rm_params = pt_ss_k.PTSSKParameter(pt_params, ss_params,
ae_params, k_params,
p_params)
# create the description for 2 models of tistel,ptgsk, ptssk
tistel_grid_spec = self.grid_spec #
cfg_list = [
RegionModelConfig("Tistel-ptgsk", pt_gs_k.PTGSKModel,
ptgsk_rm_params, tistel_grid_spec,
"unregulated", "FELTNR", id_list),
RegionModelConfig("Tistel-ptssk", pt_ss_k.PTSSKModel,
ptssk_rm_params, tistel_grid_spec,
"unregulated", "FELTNR", id_list)
]
rm_cfg_dict = {x.name: x for x in cfg_list}
return GisRegionModelRepository(rm_cfg_dict)
def _create_std_ptgsk_param(self):
ptp = api.PriestleyTaylorParameter(albedo=0.85, alpha=1.23)
ptp.albedo = 0.9
ptp.alpha = 1.26
aep = api.ActualEvapotranspirationParameter(ae_scale_factor=1.5)
aep.ae_scale_factor = 1.1
gsp = api.GammaSnowParameter(
winter_end_day_of_year=99,
initial_bare_ground_fraction=0.04,
snow_cv=0.44,
tx=-0.3,
wind_scale=1.9,
wind_const=0.9,
max_water=0.11,
surface_magnitude=33.0,
max_albedo=0.88,
min_albedo=0.55,
fast_albedo_decay_rate=6.0,
slow_albedo_decay_rate=4.0,
snowfall_reset_depth=6.1,
glacier_albedo=0.44
) # TODO: This does not work due to boost.python template arity of 15, calculate_iso_pot_energy=False)
gsp.calculate_iso_pot_energy = False
gsp.snow_cv = 0.5
gsp.initial_bare_ground_fraction = 0.04
kp = api.KirchnerParameter(c1=-2.55, c2=0.8, c3=-0.01)
kp.c1 = 2.5
kp.c2 = -0.9
kp.c3 = 0.01
spcp = api.PrecipitationCorrectionParameter(scale_factor=0.9)
ptgsk_p = pt_gs_k.PTGSKParameter(ptp, gsp, aep, kp, spcp)
ptgsk_p.ae.ae_scale_factor = 1.2 # sih: just to demo ae scale_factor can be set directly
return ptgsk_p
def std_ptgsk_parameters(self):
pt_params = api.PriestleyTaylorParameter(
) # *params["priestley_taylor"])
gs_params = api.GammaSnowParameter() # *params["gamma_snow"]
ae_params = api.ActualEvapotranspirationParameter(
) # *params["act_evap"])
k_params = api.KirchnerParameter() # *params["kirchner"])
p_params = api.PrecipitationCorrectionParameter(
) # TODO; default 1.0, is it used ??
return api.pt_gs_k.PTGSKParameter(pt_params, gs_params, ae_params,
k_params, p_params)
def test_region_model_repository(self):
id_list = [1225]
epsg_id = 32632
# parameters can be loaded from yaml_config Model parameters..
pt_params = api.PriestleyTaylorParameter(
) # *params["priestley_taylor"])
gs_params = api.GammaSnowParameter() # *params["gamma_snow"])
ss_params = api.SkaugenParameter()
ae_params = api.ActualEvapotranspirationParameter(
) # *params["act_evap"])
k_params = api.KirchnerParameter() # *params["kirchner"])
p_params = api.PrecipitationCorrectionParameter(
) # TODO; default 1.0, is it used ??
ptgsk_rm_params = api.pt_gs_k.PTGSKParameter(
pt_params, gs_params, ae_params, k_params, p_params)
ptssk_rm_params = api.pt_ss_k.PTSSKParameter(
pt_params, ss_params, ae_params, k_params, p_params)
# create the description for 4 models of tistel,ptgsk, ptssk, full and optimized
tistel_grid_spec = GridSpecification(epsg_id=epsg_id,
x0=362000.0,
y0=6765000.0,
dx=1000,
dy=1000,
nx=8,
ny=8)
cfg_list = [
RegionModelConfig("tistel-ptgsk", PTGSKModel, ptgsk_rm_params,
tistel_grid_spec, "unregulated", "FELTNR",
id_list),
RegionModelConfig("tistel-ptgsk-opt", PTGSKOptModel,
ptgsk_rm_params, tistel_grid_spec,
"unregulated", "FELTNR", id_list),
RegionModelConfig("tistel-ptssk", PTSSKModel, ptssk_rm_params,
tistel_grid_spec, "unregulated", "FELTNR",
id_list)
]
rm_cfg_dict = {x.name: x for x in cfg_list}
rmr = GisRegionModelRepository(
rm_cfg_dict
) # ok, now we have a Gis RegionModelRepository that can handle all named entities we pass.
cm1 = rmr.get_region_model(
"tistel-ptgsk") # pull out a PTGSKModel for tistel
cm2 = rmr.get_region_model("tistel-ptgsk-opt")
# Does not work, fail on ct. model:
cm3 = rmr.get_region_model(
"tistel-ptssk") # pull out a PTGSKModel for tistel
# cm4= rmr.get_region_model("tistel-ptssk",PTSSKOptModel)
self.assertIsNotNone(cm3)
self.assertIsNotNone(cm1)
self.assertIsNotNone(cm2)
self.assertIsNotNone(
cm2.catchment_id_map
) # This one is needed in order to properly map catchment-id to internal id
self.assertEqual(cm2.catchment_id_map[0], id_list[0])
rhh_ts = api.TimeSeries(tah, rhh_dv, point_fx=instant)
radph = api.RadiationParameter(0.2, 1.0)
radch = api.RadiationCalculator(radph)
radrh = api.RadiationResponse()
# pmph=api.PenmanMonteithParameter(lai,height_ws,height_t)
pmph = api.PenmanMonteithParameter(height_veg, height_ws, height_t, rl, True)
pmch = api.PenmanMonteithCalculator(pmph)
pmphst = api.PenmanMonteithParameter(height_veg, height_ws, height_t, rl,
False)
pmchst = api.PenmanMonteithCalculator(pmphst)
pmrh = api.PenmanMonteithResponse()
pmrhst = api.PenmanMonteithResponse()
#PriestleyTaylor
ptp = api.PriestleyTaylorParameter(0.2, 1.26)
ptc = api.PriestleyTaylorCalculator(0.2, 1.26)
ptr = api.PriestleyTaylorResponse
ET_ref_sim_h = []
ET_sim_st_h = []
timeofday = []
ET_pt_sim_h = []
Rso_sim_h = []
LW_sim_h = []
SW_sim_h = []
Ra_sim_h = []
Rnet_sim_h = []
for i in range(nhour - 1):
def run_pm(ws_Th, ws_eah, ws_Rsh, ws_windspeedh,ws_rhh, rnet, height_veg=0.12,dt=1, n=30,rl = 144.0,height_ws = 3, height_t = 1.68, elevation = 1462.4, method='asce-ewri'):
"""Run Penman-Monteith evapotranspiration model from SHyFT"""
import numpy as np
import math
import shyft
from shyft import api
utc = api.Calendar()
c_MJm2d2Wm2 = 0.086400
c_MJm2h2Wm2 = 0.0036
#for radiation model
latitude = 40.4
slope_deg = 0.0
aspect_deg = 0.0
# n = 30 # nr of time steps: 1 year, daily data
t_starth = utc.time(2000, 6, 1,16,0,0,0) # starting at the beginning of the year 1970
step = api.deltahours(dt)
# Let's now create Shyft time series from the supplied lists of precipitation and temperature.
# First, we need a time axis, which is defined by a starting time, a time step and the number of time steps.
ta = api.TimeAxis(t_starth, step, n) # days
# First, we convert the lists to shyft internal vectors of double values:
temph_dv = api.DoubleVector.from_numpy(ws_Th)
eah_dv = api.DoubleVector.from_numpy(ws_eah)
rsh_dv = api.DoubleVector.from_numpy(ws_Rsh)
windspeedh_dv = api.DoubleVector.from_numpy(ws_windspeedh)
rhh_dv = api.DoubleVector.from_numpy(ws_rhh)
# Finally, we create shyft time-series as follows:
# (Note: This step is not necessarily required to run the single methods.
# We could also just work with the double vector objects and the time axis)
instant = api.point_interpretation_policy.POINT_INSTANT_VALUE
average = api.point_interpretation_policy.POINT_AVERAGE_VALUE
temph_ts = api.TimeSeries(ta, temph_dv, point_fx=instant)
eah_ts = api.TimeSeries(ta, eah_dv, point_fx=instant)
rsh_ts = api.TimeSeries(ta, rsh_dv, point_fx=instant)
windspeedh_ts = api.TimeSeries(ta, windspeedh_dv, point_fx=instant)
#recalculated inputs:
rhh_ts = api.TimeSeries(ta, rhh_dv, point_fx=instant)
radph = api.RadiationParameter(0.26,1.0)
radch = api.RadiationCalculator(radph)
radrh =api.RadiationResponse()
if method=='asce-ewri':
full_model = False
else:
full_model = True
pmph=api.PenmanMonteithParameter(height_veg,height_ws,height_t, rl, full_model)
pmch=api.PenmanMonteithCalculator(pmph)
pmrh =api.PenmanMonteithResponse()
#PriestleyTaylor
ptp = api.PriestleyTaylorParameter(0.2,1.26)
ptc = api.PriestleyTaylorCalculator(0.2, 1.26)
ptr = api.PriestleyTaylorResponse
ET_ref_sim_h= []
for i in range(n-1):
pmch.reference_evapotranspiration(pmrh, step,rnet[i], temph_ts.v[i],temph_ts.v[i],
rhh_ts.v[i], elevation, windspeedh_ts.v[i])
ET_ref_sim_h.append(pmrh.et_ref)
return ET_ref_sim_h
