# Calculate ET for Amazon sub-basins
results_dict = {}
for basin in basin_shapes:
#print(basin)
"""For each catchment get mask, calc P, R and dS_dt and use to calculate
catchment ET"""
areas = {}
out = {}
basin_name = substring_before(basin, '_')
# Get basin mask
pre_lat = get_lats(pre)
pre_lon = get_lons(pre)
pre_mask = extract.get_mask(pre_lat, pre_lon, basin)
onedeg_mask = extract.get_mask(grace_lat, grace_lon, basin)
# Get area of basin in m2
area = np.sum(pre_mask * pre_pixel_size_grid)
areas[basin_name] = area
print(basin_name + ' basin = ' + str(area) + ' m2')
# First calculate pre over catchment for May 2002 to Dec 2018
Nt = pre.shape[0]
P_basin = np.nan * np.zeros((Nt))
shp_path = '/nfs/a68/gyjcab/datasets/shapefiles/'
for nt in range(Nt):
masked_data = np.ma.array(pre.data[nt + 0, :, :],
mask=~pre_mask) # +9 to get from jan 2003
masked_weights = np.ma.array(pre_pixel_size_grid, mask=~pre_mask)
# generate mask to extract data from basin
lats = get_lats(chirps_pr)
lons = get_lons(chirps_pr)
basin_shapes = ['purus_dissolved', 'madeira_dissolved',
'negro_dissolved', 'xingu_dissolved',
'jari_dissolved', 'japura_dissolved',
'branco_dissolved','aripuana_dissolved',
'solimoes_dissolved5', 'tapajos_dissolved3',
'amazon_obidos_dissolved']
results_dict = {}
for basin in basin_shapes:
print(basin)
mask = extract.get_mask(lats, lons, basin)
temp = 1*mask
plt.figure()
pixel_size_grid = get_pixel_size(lons)
pixel_size_grid = np.array([pixel_size_grid]*len(lons)).transpose()
chirps_vals = []
for yr in range(chirps_pr.shape[0]):
data = chirps_pr[yr, :, :]
amazon_mean = get_area_weighted_avg(data, mask, pixel_size_grid)
chirps_vals.append(amazon_mean)
chirps_df = pd.DataFrame()
basin_name = basin.split('_', 1)[0]
print(basin_name)
data = catchment_et[basin_name]
datemin = datetime(2003, 1, 1, 0, 0)
datemax = datetime(2019, 12, 31, 0, 0)
idx = pd.date_range(datemin, datemax, freq='MS')
data = data.reindex(idx, fill_value=np.nan)
print(data.head(5))
print(data.tail(5))
data.ET.iloc[np.where(data.ET < 0)] = np.nan
startyr = data.index[0].year
endyr = data.index[-1].year
nyear = endyr - startyr + 1
# --------------- Read in basin information
pre_mask = extract.get_mask(pre_lat, pre_lon, basin)
# Error in dS/dt
# Use error values from Wiese et al (2016) for Amazon
sigma_merr = 6.1 # GRACE measurement error
sigma_lerr = 0.9 # residual leakage error after applying CRI filter and
# and gain factors from CLM model
## Combine errors in quadrature
sigma_S = math.sqrt((sigma_merr)**2 + (sigma_lerr)**2)
# Convert to sigma_dSdt
sigma_dSdt = (sigma_S * math.sqrt(2))
print(sigma_dSdt)
