def run_clark2009(catchment, output_dem, hydro_year_to_take, met_inp_folder,
catchment_shp_folder):
"""
wrapper to call the clark 2009 snow model for given area
:param catchment: string giving catchment area to run model on
:param output_dem: string identifying the grid to run model on
:param hydro_year_to_take: integer specifying the hydrological year to run model over. 2001 = 1/4/2000 to 31/3/2001
:return: st_swe, st_melt, st_acc, out_dt, mask. daily grids of SWE at day's end, total melt and accumulation over the previous day, and datetimes of ouput
"""
print('loading met data')
data_id = '{}_{}'.format(catchment, output_dem)
inp_met = nc.Dataset(
met_inp_folder +
'/met_inp_{}_hy{}.nc'.format(data_id, hydro_year_to_take), 'r')
inp_dt = nc.num2date(inp_met.variables['time'][:],
inp_met.variables['time'].units)
inp_doy = convert_date_hydro_DOY(inp_dt)
inp_hourdec = [datt.hour for datt in inp_dt]
inp_ta = inp_met.variables['temperature'][:]
inp_precip = inp_met.variables['precipitation'][:]
print('met data loaded')
mask = create_mask_from_shpfile(
inp_met.variables['lat'][:], inp_met.variables['lon'][:],
catchment_shp_folder + '/{}.shp'.format(catchment))
# TODO: think about masking input data to speed up
print('starting snow model')
# start with no snow.
# call main function once hourly/sub-hourly temp and precip data available.
st_swe, st_melt, st_acc = snow_main_simple(inp_ta,
inp_precip,
inp_doy,
inp_hourdec,
dtstep=3600)
out_dt = np.asarray(
make_regular_timeseries(inp_dt[0], inp_dt[-1] + dt.timedelta(days=1),
86400))
print('snow model finished')
# mask out values outside of catchment
st_swe[:, mask == False] = np.nan
st_melt[:, mask == False] = np.nan
st_acc[:, mask == False] = np.nan
return st_swe, st_melt, st_acc, out_dt, mask
ts_mb = plt.cumsum(mb_dat[:, 0])
np.where(np.asarray(mb_dt) == dt.datetime(2011, 5, 13, 00, 00))
ts_mb -= ts_mb[199]
#
init_swe = np.ones(
inp_ta.shape[1:]) * 0 # give initial value of swe as starts in spring
init_d_snow = np.ones(
inp_ta.shape[1:]) * 30 # give initial value of days since snow
# call main function once hourly/sub-hourly temp and precip data available.
st_swe, st_melt, st_acc, st_alb = snow_main_simple(inp_ta,
inp_precip,
inp_doy,
inp_hourdec,
dtstep=1800,
init_swe=init_swe,
init_d_snow=init_d_snow,
inp_sw=inp_sw,
which_melt='clark2009',
**config)
st_swe1, st_melt1, st_acc1, st_alb1 = snow_main_simple(inp_ta,
inp_precip,
inp_doy,
inp_hourdec,
dtstep=1800,
init_swe=init_swe,
init_d_snow=init_d_snow,
inp_sw=inp_sw,
which_melt='dsc_snow',
**config)
# plt.plot(plot_dt,inp_sw_obs/2, label = 'Observed radiation')
# plt.plot(year_VC, rad_VC_year, label = 'VC rad')
# plt.plot(year_VN, rad_VN_year, label = 'VN rad')
# plt.gcf().autofmt_xdate()
# months = mdates.MonthLocator() # every month
# monthsFmt = mdates.DateFormatter('%b')
# manager = plt.get_current_fig_manager()
# manager.window.showMaximized()
# plt.ylabel('radiations in W/m2')
# plt.legend()
# plt.show()
# plt.close
# call main function once hourly/sub-hourly temp and precip data available.
try :
st_swe, st_melt, st_acc, st_alb = snow_main_simple(inp_ta, inp_precip, inp_doy, inp_hourdec, dtstep=3600,init_swe=init_swe,
init_d_snow=init_d_snow, inp_sw=inp_sw, which_melt='clark2009', **config)
st_swe1, st_melt1, st_acc1, st_alb1 = snow_main_simple(inp_ta, inp_precip, inp_doy, inp_hourdec, dtstep=3600, init_swe=init_swe,
init_d_snow=init_d_snow, inp_sw=inp_sw, which_melt='dsc_snow', **config)
except :
print('No VC data', year)
try :
st_swea, st_melta, st_acca, st_alba = snow_main_simple(inp_ta_VN, inp_precip, inp_doy, inp_hourdec, dtstep=3600,init_swe=init_swe,
init_d_snow=init_d_snow, inp_sw=inp_sw_VN,which_melt='clark2009', **config)
st_swe1a, st_melt1a, st_acc1a, st_alb1a = snow_main_simple(inp_ta_VN, inp_precip, inp_doy, inp_hourdec, dtstep=3600,init_swe=init_swe,
init_d_snow=init_d_snow, inp_sw=inp_sw_VN,which_melt='dsc_snow', **config)
except :
print('No VN data', year)
# MUELLER SWE csv file
# csv_file = "C:/Users/Bonnamourar/OneDrive - NIWA/CSV SWE/Mueller SWE.csv"
# MAHANGA SWE csv file
stor_dict = {}
ii = 0
for i, tf in enumerate(tf_list):
for j, tt in enumerate(tmelt_list):
stor_dict[ii] = {}
config['mf_mean'] = tf
config['tmelt'] = tt
# call main function once hourly/sub-hourly temp and precip data available.
st_swe, st_melt, st_acc, st_alb = snow_main_simple(
inp_ta,
inp_precip,
inp_doy,
inp_hourdec,
dtstep=3600,
init_swe=init_swe,
init_d_snow=init_d_snow,
inp_sw=inp_sw,
which_melt='clark2009',
**config)
run_dict = {
'timestamp': inp_dt,
'swe': np.squeeze(st_swe),
'melt': np.squeeze(st_melt),
'acc': np.squeeze(st_acc),
'alb': np.squeeze(st_alb)
}
run_df = pd.DataFrame.from_dict(run_dict)
run_df.set_index('timestamp', inplace=True)
