dem_folder = ''
modis_dem = 'modis_si_dem_250m'
output_folder = r'C:\Users\conwayjp\OneDrive - NIWA\projects\DSC Snow\MODIS'
# set up lists
ann_ts_av_sca_m = []
ann_ts_av_sca_thres_m = []
ann_dt_m = []
ann_scd_m = []
for year_to_take in years_to_take:
print('loading modis data {}'.format(year_to_take))
# load modis data for evaluation - trims to extent of catchment
modis_fsca, modis_dt, modis_mask = load_subset_modis_annual(
catchment, output_dem, year_to_take, modis_folder, dem_folder,
modis_dem, mask_folder, catchment_shp_folder)
modis_sc = modis_fsca >= modis_sc_threshold
# print('calculating basin average sca')
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(np.double) /
num_modis_gridpoints)
# print('adding to annual series')
ann_ts_av_sca_m.append(np.asarray(ba_modis_sca))
mask_folder = 'C:/Users/conwayjp/OneDrive - NIWA/Temp/DSC-Snow/Masks'
catchment_shp_folder = 'C:/Users/conwayjp/OneDrive - NIWA/Data/GIS_DATA/Hydrology/Catchments'
modis_folder = 'C:/Users/conwayjp/OneDrive - NIWA/Data/MODIS_snow/NSDI_SI_cloudfilled'
dem_folder = 'C:/Users/conwayjp/OneDrive - NIWA/Data/GIS_DATA/Topography/DEM_NZSOS/'
modis_dem = 'modis_si_dem_250m'
met_inp_folder = 'D:/DSC-Snow/input_data_hourly'
dsc_snow_dem_folder = 'C:/Users/conwayjp/OneDrive - NIWA/projects/DSC Snow/Projects-DSC-Snow/runs/input_DEM'
output_folder = 'C:/Users/conwayjp/OneDrive - NIWA/projects/DSC Snow/Projects-DSC-Snow/runs/output/clutha_nztm250m_erebus'
for year_to_take in years_to_take:
print('loading modis data {}'.format(year_to_take))
# load modis data for evaluation
modis_fsca, modis_dt, modis_mask = load_subset_modis_annual(catchment, year_to_take, modis_folder, modis_dem, mask_folder)
# set up output array
nt = modis_fsca.shape[0]
ny = modis_fsca.shape[1]
nx = modis_fsca.shape[2]
ny_out = ny // rl # integer divide to ensure fits
nx_out = nx // rl
modis_fsca_rs = np.zeros((nt, ny_out, nx_out))
for i in range(nt):
modis_sub = modis_fsca[i,]
fsca_rs = resample_to_fsca(modis_sub, rl=rl)
modis_fsca_rs[i] = fsca_rs
# load model data
def calc_modis_catchment_metrics(catchment, years_to_take, mask_folder, modis_folder, modis_dem, output_folder):
"""
reads in and computes statistics on MODIS data for specific catchments. Requires that mask has been created using
nz_snow_tools/util/generate_mask.py
saves output to a pickle file
:param catchment, # string identifying catchment modelled
:param years_to_take
:param modis_sc_threshold = 50 # value of fsca (in percent) that is counted as being snow covered
:param mask_folder folder with numpy masks
:param modis_folder folder with cloudfilled modis data
:param modis_dem name of modis grid 'modis_nz_dem_250m'
:param output_folder folder to put output pickle file
"""
# set up lists
ann_ts_av_sca_m = []
ann_ts_av_sca_thres_m = []
ann_dt_m = []
ann_scd_m = []
for year_to_take in years_to_take:
print('loading modis data {}'.format(year_to_take))
# load modis data for evaluation - trims to extent of catchment
modis_fsca, modis_dt, modis_mask = load_subset_modis_annual(catchment, year_to_take, modis_folder, modis_dem, mask_folder)
modis_sc = modis_fsca >= modis_sc_threshold
# print('calculating basin average sca')
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(np.double) / num_modis_gridpoints)
# print('adding to annual series')
ann_ts_av_sca_m.append(np.asarray(ba_modis_sca))
ann_ts_av_sca_thres_m.append(np.asarray(ba_modis_sca_thres))
# print('calc snow cover duration')
modis_scd = np.sum(modis_sc, axis=0) # count days with snow over threshold
modis_scd[modis_mask == 0] = -999 # set areas outside catchment to -999
modis_scd[np.logical_and(np.isnan(modis_fsca[0]), modis_mask == 1)] = -1 # set areas of water to -1
# add to annual series
ann_scd_m.append(modis_scd)
ann_dt_m.append(modis_dt)
# empty arrays so we don't run out of memory (probably not needed)
modis_fsca = None
modis_dt = None
modis_mask = None
modis_sc = None
modis_scd = None
ann = [ann_ts_av_sca_m, ann_ts_av_sca_thres_m, ann_dt_m, ann_scd_m]
outfile = '/summary_MODIS_{}_{}_{}_{}_thres{}.pkl'.format(years_to_take[0], years_to_take[-1], catchment, modis_dem, modis_sc_threshold)
pickle.dump(ann, open(output_folder + outfile, 'wb'), protocol=3)
# repack into single timeseries
sca = []
dts_m = []
for ts_av_sca_m, dt_m in zip(ann_ts_av_sca_m, ann_dt_m):
sca.extend(ts_av_sca_m)
dts_m.extend(dt_m)
# dts_full = make_regular_timeseries(dt.datetime(years_to_take[0], 1, 1), dt.datetime(years_to_take[-1], 12, 31), 86400)
outfile_nc = '/ts_MODIS_{}_{}_{}_{}_thres{}.nc'.format(years_to_take[0], years_to_take[-1], catchment, modis_dem, modis_sc_threshold)
ds = nc.Dataset(output_folder + outfile_nc,'w')
ds.created = strftime("%Y-%m-%d %H:%M:%S", gmtime())
ds.comment= 'catchment averaged fractional snow covered area generated with https://github.com/bodekerscientific/nz_snow_tools/eval/catchment_evaluation_modis.py'
ds.createDimension('time', )
t = ds.createVariable('time', 'f8', ('time',))
t.long_name = "time"
t.units = 'days since 1900-01-01 00:00:00'
t[:] = nc.date2num(dts_m, units=t.units)
fsca_var = ds.createVariable('fsca', 'f8', ('time',))
fsca_var.setncatts({'long_name': 'fractional snow covered area'})
fsca_var[:] = np.asarray(sca)
ds.close()