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
def create_average(Stname, var='SWE'):
data = dict_sin_snow[Stname][var][:]
# remove nans
inp_datobs = data[~np.isnan(data)]
inp_dtobs = dict_sin_snow['dt_UTC+12'][~np.isnan(data)]
if var == 'SWE':
inp_datobs = inp_datobs * 1000
# remove periods of poor data
if Stname == 'Castle Mount' and var == 'Snow Depth': # noisy snow depth data
ind = inp_dtobs < dt.datetime(2018, 12, 1)
elif Stname == 'Mueller' and var == 'SWE': # clearly wrong as snow depth >> 1 m over this period
ind = ~np.logical_and(inp_dtobs > dt.datetime(2012, 10, 20),
inp_dtobs < dt.datetime(2012, 12, 4))
elif Stname == 'Philistine' and var == 'SWE': # clearly wrong as snow depth < 1 m during this period
ind = ~np.logical_and(inp_dtobs > dt.datetime(2014, 6, 1),
inp_dtobs < dt.datetime(2014, 10, 1))
else:
ind = np.ones(inp_datobs.shape, dtype=np.bool)
dohy = convert_date_hydro_DOY(inp_dtobs)
# jd = np.asarray(convert_datetime_julian_day(inp_dtobs))
dat_jd = np.full([365], np.nan)
for i in range(365):
if i < 5:
dat_jd[i] = np.median(inp_datobs[np.logical_or(
dohy > i - 5 + 365, dohy <= i + 5)])
elif i > 360:
dat_jd[i] = np.median(inp_datobs[np.logical_or(
dohy > i - 5, dohy <= i + 5 - 365)])
else:
dat_jd[i] = np.median(inp_datobs[np.logical_and(
dohy > i - 5, dohy <= i + 5)])
dt_to_plot = [
dt.datetime(2000, 4, 1, 0, 0) + dt.timedelta(days=j)
for j in range(365)
]
return dt_to_plot, dat_jd
# ann_ts_av_melt2 = []
# ann_ts_av_acc2 = []
ann_hydro_days2 = []
ann_dt2 = []
ann_scd2 = []
configs = []
for hydro_year_to_take in hydro_years_to_take:
print('loading modis data HY {}'.format(hydro_year_to_take))
# load modis data for evaluation
modis_fsca, modis_dt, modis_mask = load_subset_modis(
catchment, output_dem, hydro_year_to_take, modis_folder,
dem_folder, modis_dem, mask_folder, catchment_shp_folder)
modis_hydro_days = convert_date_hydro_DOY(modis_dt)
modis_sc = modis_fsca >= modis_sc_threshold
# print('calculating basin average sca')
# modis
num_modis_gridpoints = np.sum(modis_mask)
ba_modis_sca = []
ba_modis_sca_thres = []
for i in range(modis_fsca.shape[0]):
ba_modis_sca.append(np.nanmean(modis_fsca[i, modis_mask]) / 100.0)
ba_modis_sca_thres.append(
np.nansum(modis_sc[i, modis_mask]).astype('d') /
num_modis_gridpoints)
# print('adding to annual series')
ann_ts_av_sca_m.append(np.asarray(ba_modis_sca))