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
# set up input and output DEM for processing
# output DEM
nztm_dem, x_centres, y_centres, lat_array, lon_array = setup_nztm_dem(
dem_file, origin=origin)
data_id = '{}_{}'.format(catchment,
output_dem) # name to identify the output data
if mask_dem == True:
# Get the masks for the individual regions of interest
if mask_created == True: # load precalculated mask
mask = np.load(mask_folder +
'/{}_{}.npy'.format(catchment, output_dem))
if origin == 'topleft':
mask = np.flipud(mask)
else: # create mask and save to npy file
# masks = get_masks() #TODO set up for multiple masks
mask = create_mask_from_shpfile(lat_array, lon_array, mask_shpfile)
if origin == 'topleft': # flip to save as bottom left
mask = np.flipud(mask)
np.save(mask_folder + '/{}_{}.npy'.format(catchment, output_dem),
mask)
if origin == 'topleft': # flip back to topleft
mask = np.flipud(mask)
# Trim down the number of latitudes requested so it all stays in memory
lats, lons, elev, northings, eastings = trim_lat_lon_bounds(
mask, lat_array, lon_array, nztm_dem, y_centres, x_centres)
_, _, trimmed_mask, _, _ = trim_lat_lon_bounds(mask,
lat_array, lon_array,
mask.copy(), y_centres,
x_centres)
else:
mask = None
if which_model == 'clark2009':
config['tmelt'] = 273.16
if which_model == 'dsc_snow':
config['tmelt'] = 274.16
for hydro_year_to_take in hydro_years_to_take:
# run model and return timeseries of daily swe, acc and melt.
met_infile = met_data_folder + '/met_inp_{}_{}_hy{}.nc'.format(
catchment, output_dem, hydro_year_to_take)
st_swe, st_melt, st_acc = snow_main(met_infile,
which_melt=which_model,
**config)
# create the extra variables required by catchment_evaluation
inp_met = nc.Dataset(met_infile, 'r')
inp_dt = nc.num2date(inp_met.variables['time'][:],
inp_met.variables['time'].units)
out_dt = np.asarray(
make_regular_timeseries(inp_dt[0], inp_dt[-1], 86400))
mask = create_mask_from_shpfile(
inp_met.variables['lat'][:], inp_met.variables['lon'][:],
catchment_shp_folder + '/{}.shp'.format(catchment))
pickle.dump(
[
st_swe.astype(dtype=np.float32),
st_melt.astype(dtype=np.float32),
st_acc.astype(dtype=np.float32), out_dt, mask, config
],
open(
output_folder + '/{}_{}_hy{}_{}.pkl'.format(
catchment, output_dem, hydro_year_to_take, which_model),
'wb'), -1)