def single_location_comparison(location=[31.65, 77.34],
station='Banjar',
min_year=2000,
max_year=2011):
"""Plot model outputs for given coordinates over time."""
aphro_ds = aphrodite.collect_APHRO(location,
minyear=min_year,
maxyear=max_year)
cru_ds = cru.collect_CRU(location, minyear=min_year, maxyear=max_year)
era5_ds = era5.collect_ERA5(location, minyear=min_year, maxyear=max_year)
gpm_ds = gpm.collect_GPM(location, minyear=min_year, maxyear=max_year)
wrf_ds = beas_sutlej_wrf.collect_BC_WRF(location,
minyear=min_year,
maxyear=max_year)
gauge_ds = beas_sutlej_gauges.gauge_download(station,
minyear=min_year,
maxyear=max_year)
# cmip_ds = cmip5.collect_CMIP5()
# cordex_ds = cordex.collect_CORDEX()
# model_ts = model_prep([lat, lon], data_filepath='single_loc_test.csv', \
# model_filepath=model_filepath)
timeseries = [gauge_ds, gpm_ds, era5_ds, wrf_ds, aphro_ds, cru_ds]
tims.benchmarking_subplots(timeseries, reference_dataset=gauge_ds)
dataset_stats(timeseries, ref_ds=gauge_ds)
def multi_location_comparison():
"""Plot model outputs for multiple locations over time."""
gauge_ds = beas_sutlej_gauges.all_gauge_data(minyear=2000,
maxyear=2011,
threshold=3653)
locations = [[31.424, 76.417], [31.357, 76.878], [31.52, 77.22],
[31.67, 77.06], [31.454, 77.644], [31.238, 77.108],
[31.65, 77.34], [31.88, 77.15], [31.77, 77.31],
[31.80, 77.19]]
aphro_sets = []
cru_sets = []
era5_sets = []
gpm_sets = []
wrf_sets = []
for loc in locations:
aphro_ds = aphrodite.collect_APHRO(loc, minyear=2000, maxyear=2011)
aphro_sets.append(aphro_ds.tp)
cru_ds = cru.collect_CRU(loc, minyear=2000, maxyear=2011)
cru_sets.append(cru_ds.tp)
era5_ds = era5.collect_ERA5(loc, minyear=2000, maxyear=2011)
era5_sets.append(era5_ds.tp)
gpm_ds = gpm.collect_GPM(loc, minyear=2000, maxyear=2011)
gpm_sets.append(gpm_ds.tp)
wrf_ds = beas_sutlej_wrf.collect_BC_WRF(loc,
minyear=2000,
maxyear=2011)
wrf_sets.append(wrf_ds.tp)
# Merge datasets
aphro_mer_ds = xr.concat(aphro_sets, dim='lon')
aphro_mer_ds.attrs['plot_legend'] = 'APHRODITE'
cru_mer_ds = xr.concat(cru_sets, dim='lon')
cru_mer_ds.attrs['plot_legend'] = 'CRU'
era5_mer_ds = xr.concat(era5_sets, dim='lon')
era5_mer_ds.attrs['plot_legend'] = 'ERA5'
gpm_mer_ds = xr.concat(gpm_sets, dim='lon')
gpm_mer_ds.attrs['plot_legend'] = 'TRMM'
wrf_mer_ds = xr.concat(wrf_sets, dim='lon')
wrf_mer_ds.attrs['plot_legend'] = 'BC WRF'
timeseries = [
gpm_mer_ds, era5_mer_ds, wrf_mer_ds, aphro_mer_ds, cru_mer_ds
]
pdf.mult_gauge_loc_plot(gauge_ds, timeseries)
def gauge_stats():
"""Print mean, standard deviations and slope for datasets."""
bs_station_df = pd.read_csv('_Data/bs_only_gauge_info.csv')
'''
mlm_val_stations = ['Bhakra', 'Suni' 'Pandoh', 'Janjehl', 'Bhuntar',
'Rampur']
val_stations = ['Banjar', 'Larji', 'Bhuntar', 'Sainj',
'Bhakra', 'Kasol', 'Suni', 'Pandoh', 'Janjehl', 'Rampur']
'''
r2_list = []
rmse_list = []
for s in tqdm(bs_station_df):
gauge_ds = beas_sutlej_gauges.gauge_download(s,
minyear=2000,
maxyear=2011)
gauge_maxy = gauge_ds.time.max().values
gauge_miny = gauge_ds.time.min().values
miny = gauge_miny - 0.0001
maxy = gauge_maxy + 0.0001
location = bs_station_df[s].values
aphro_ds = aphrodite.collect_APHRO(location,
minyear=miny,
maxyear=maxy)
cru_ds = cru.collect_CRU(location, minyear=miny, maxyear=maxy)
era5_ds = era5.collect_ERA5(location, minyear=miny, maxyear=maxy)
gpm_ds = gpm.collect_GPM(location, minyear=miny, maxyear=maxy)
wrf_ds = beas_sutlej_wrf.collect_BC_WRF(location,
minyear=miny,
maxyear=maxy)
timeseries = [era5_ds, gpm_ds, aphro_ds, cru_ds, wrf_ds]
r2s, rmses = dataset_stats(timeseries, ref_ds=gauge_ds, ret=True)
r2_list.append(r2s)
rmse_list.append(rmses)
avg_r2 = np.array(r2_list).mean(axis=0)
avg_rmse = np.array(rmse_list).mean(axis=0)
return avg_r2, avg_rmse
def basin_comparison(model_filepath, location):
""" Plot model outputs for given basin over time."""
aphro_ds = aphrodite.collect_APHRO(location, minyear=2000, maxyear=2011)
cru_ds = cru.collect_CRU(location, minyear=2000, maxyear=2011)
era5_ds = era5.collect_ERA5(location, minyear=2000, maxyear=2011)
gpm_ds = gpm.collect_GPM(location, minyear=2000, maxyear=2011)
wrf_ds = beas_sutlej_wrf.collect_BC_WRF(location,
minyear=2000,
maxyear=2011)
# cmip_ds = cmip5.collect_CMIP5()
# cordex_ds = cordex.collect_CORDEX()
# cmip_bs = select_basin(cmip_ds, location)
# cordex_bs = select_basin(cordex_ds, location)
# model_bs = model_prep(location, model_filepath)
basins = [aphro_ds, cru_ds, era5_ds, gpm_ds, wrf_ds]
dataset_stats(basins)
def multi_dataset_map(location, seasonal=False):
"""
Create maps of raw values from multiple datasets
- ERA5
- GPM PR
- APHRODITE
- CRU
- Bannister corrected WRF
"""
# Load data
aphro_ds = aphrodite.collect_APHRO(location, minyear=2000, maxyear=2011)
cru_ds = cru.collect_CRU(location, minyear=2000, maxyear=2011)
era5_ds = era5.collect_ERA5(location, minyear=2000, maxyear=2011)
gpm_ds = gpm.collect_GPM(location, minyear=2000, maxyear=2011)
wrf_ds = beas_sutlej_wrf.collect_BC_WRF(location,
minyear=2000,
maxyear=2011)
dataset_list = [gpm_ds, era5_ds, wrf_ds, aphro_ds, cru_ds]
# Slice and take averages
avg_list = []
for ds in dataset_list:
ds_avg = ds.tp.mean(dim='time')
if seasonal is True:
ds_annual_avg = ds_avg
ds_jun = ds.tp[5::12]
ds_jul = ds.tp[6::12]
ds_aug = ds.tp[7::12]
ds_sep = ds.tp[8::12]
ds_monsoon = xr.merge([ds_jun, ds_jul, ds_aug, ds_sep])
ds_monsoon_avg = ds_monsoon.tp.mean(dim='time')
ds_dec = ds.tp[11::12]
ds_jan = ds.tp[0::12]
ds_feb = ds.tp[1::12]
ds_mar = ds.tp[2::12]
ds_west = xr.merge([ds_dec, ds_jan, ds_feb, ds_mar])
ds_west_avg = ds_west.tp.mean(dim='time')
ds_avg = xr.concat(
[ds_annual_avg, ds_monsoon_avg, ds_west_avg],
pd.Index(["Annual", "Monsoon (JJAS)", "Winter (DJFM)"],
name='t'))
avg_list.append(ds_avg)
datasets = xr.concat(
avg_list,
pd.Index(["TRMM", "ERA5", "BC_WRF", "APHRO", "CRU"], name="Dataset"))
# Plot maps
g = datasets.plot(x="lon",
y="lat",
col="t",
row="Dataset",
cbar_kwargs={"label": "Total precipitation (mm/day)"},
cmap="YlGnBu",
subplot_kws={"projection": ccrs.PlateCarree()})
for ax in g.axes.flat:
ax.coastlines()
gl = ax.gridlines(draw_labels=True)
gl.top_labels = False
gl.right_labels = False
ax.set_extent([75, 83.5, 29, 34])
ax.add_feature(cf.BORDERS)
ax.set_xlabel("Longitude")
ax.set_ylabel("Latitude")
plt.show()