Contents

Checking the hydrological model

We found a high simulated peak and we dont understand where this peak comes from. The hydrological model output is stored in monthly resolution and does not contain all variables and states (to be able to handle storage). We therefore have re-simulated the model for the largest simulated event, so we could store and analyse multiple variables and states on a daily resolution. Note that the simulations are exactly the same, the only difference is the way the output is stored.

Import

##Load pacakages
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
import cartopy
import cartopy.crs as ccrs
import os
import matplotlib.ticker as mticker

##This is so variables get printed within jupyter
from IPython.core.interactiveshell import InteractiveShell 
InteractiveShell.ast_node_interactivity = "all"

We load the catchment masks

##Load the catchment masks
masks=xr.open_dataset(r'/home/tike//EXPLORE/subCatchmentsAmazon.nc')
# masks_Ec=xr.open_dataset(dirname+'../EXPLORE/subCatchmentsAmazon_Ec.nc')

And we check which files are available for the re-simulated data

###Check which files are available for the re-simulated data
dirname = r'/home/tike/s13r13/netcdf/'
for filename in os.listdir(dirname):
    print (filename)

satDegLow_dailyTot_output.nc
directRunoff_dailyTot_output.nc
storGroundwater_dailyTot_output.nc
discharge_dailyTot_output.nc
totalWaterStorageThickness_dailyTot_output.nc
storLowTotal_dailyTot_output.nc
surfaceWaterStorage_dailyTot_output.nc
totalRunoff_dailyTot_output.nc
interflowTotal_dailyTot_output.nc
satDegUpp_dailyTot_output.nc
gwRecharge_dailyTot_output.nc
baseflow_dailyTot_output.nc
pr_d_ECEarth_PD_s13r13_2037.nc
storUppTotal_dailyTot_output.nc
waterBodyStorage_dailyTot_output.nc
totalEvaporation_dailyTot_output.nc
runoff_dailyTot_output.nc

We then load the relevant variables and states:

Discharge = xr.open_dataset(dirname + 'discharge_dailyTot_output.nc') #Streamflow
Discharge # m3/s

Runoff = xr.open_dataset(dirname + 'runoff_dailyTot_output.nc') 
Runoff #m/d

Direct_runoff = xr.open_dataset(dirname + 'directRunoff_dailyTot_output.nc') 
Direct_runoff #m/d

Baseflow = xr.open_dataset(dirname + 'baseflow_dailyTot_output.nc') 
Baseflow #m/d

Groundwater_recharge = xr.open_dataset(dirname + 'gwRecharge_dailyTot_output.nc') 
Groundwater_recharge #m/d


Groundwater_storage = xr.open_dataset(dirname + 'storGroundwater_dailyTot_output.nc') 
Groundwater_storage  #m -> it says daily?

Storage = xr.open_dataset(dirname + 'totalWaterStorageThickness_dailyTot_output.nc') 
Storage #m 

### Variables not used
# totalRunoff_dailyTot_output.nc
# satDegLow_dailyTot_output.nc
# storLowTotal_dailyTot_output.nc
# surfaceWaterStorage_dailyTot_output.nc
# interflowTotal_dailyTot_output.nc
# satDegUpp_dailyTot_output.nc
# storUppTotal_dailyTot_output.nc
# waterBodyStorage_dailyTot_output.nc
# totalEvaporation_dailyTot_output.nc
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • lat: 360
    • lon: 720
    • time: 1826
    • time
      (time)
      datetime64[ns]
      2035-01-01 ... 2039-12-31
      standard_name :
      time
      long_name :
      Days since 1901-01-01
      array(['2035-01-01T00:00:00.000000000', '2035-01-02T00:00:00.000000000',
       '2035-01-03T00:00:00.000000000', ..., '2039-12-29T00:00:00.000000000',
       '2039-12-30T00:00:00.000000000', '2039-12-31T00:00:00.000000000'],
      dtype='datetime64[ns]')
    • lat
      (lat)
      float32
      89.75 89.25 88.75 ... -89.25 -89.75
      long_name :
      latitude
      units :
      degrees_north
      standard_name :
      latitude
      array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75], dtype=float32)
    • lon
      (lon)
      float32
      -179.75 -179.25 ... 179.25 179.75
      standard_name :
      longitude
      long_name :
      longitude
      units :
      degrees_east
      array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
      dtype=float32)
    • discharge
      (time, lat, lon)
      float32
      ...
      standard_name :
      discharge
      long_name :
      discharge
      units :
      m3.s-1
      [473299200 values with dtype=float32]
  • description :
    Niko Wanders (n.wanders@uu.nl, nwanders@princeton.edu)
    institution :
    Utrecht University, Princeton University
    title :
    PCR-GLOBWB model output
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • lat: 360
    • lon: 720
    • time: 1826
    • time
      (time)
      datetime64[ns]
      2035-01-01 ... 2039-12-31
      standard_name :
      time
      long_name :
      Days since 1901-01-01
      array(['2035-01-01T00:00:00.000000000', '2035-01-02T00:00:00.000000000',
       '2035-01-03T00:00:00.000000000', ..., '2039-12-29T00:00:00.000000000',
       '2039-12-30T00:00:00.000000000', '2039-12-31T00:00:00.000000000'],
      dtype='datetime64[ns]')
    • lat
      (lat)
      float32
      89.75 89.25 88.75 ... -89.25 -89.75
      long_name :
      latitude
      units :
      degrees_north
      standard_name :
      latitude
      array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75], dtype=float32)
    • lon
      (lon)
      float32
      -179.75 -179.25 ... 179.25 179.75
      standard_name :
      longitude
      long_name :
      longitude
      units :
      degrees_east
      array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
      dtype=float32)
    • land_surface_runoff
      (time, lat, lon)
      float32
      ...
      standard_name :
      land_surface_runoff
      long_name :
      land_surface_runoff
      units :
      m.day-1
      [473299200 values with dtype=float32]
  • description :
    Niko Wanders (n.wanders@uu.nl, nwanders@princeton.edu)
    institution :
    Utrecht University, Princeton University
    title :
    PCR-GLOBWB model output
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • lat: 360
    • lon: 720
    • time: 1826
    • time
      (time)
      datetime64[ns]
      2035-01-01 ... 2039-12-31
      standard_name :
      time
      long_name :
      Days since 1901-01-01
      array(['2035-01-01T00:00:00.000000000', '2035-01-02T00:00:00.000000000',
       '2035-01-03T00:00:00.000000000', ..., '2039-12-29T00:00:00.000000000',
       '2039-12-30T00:00:00.000000000', '2039-12-31T00:00:00.000000000'],
      dtype='datetime64[ns]')
    • lat
      (lat)
      float32
      89.75 89.25 88.75 ... -89.25 -89.75
      long_name :
      latitude
      units :
      degrees_north
      standard_name :
      latitude
      array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75], dtype=float32)
    • lon
      (lon)
      float32
      -179.75 -179.25 ... 179.25 179.75
      standard_name :
      longitude
      long_name :
      longitude
      units :
      degrees_east
      array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
      dtype=float32)
    • direct_runoff
      (time, lat, lon)
      float32
      ...
      standard_name :
      direct_runoff
      long_name :
      direct_runoff
      units :
      m.day-1
      [473299200 values with dtype=float32]
  • description :
    Niko Wanders (n.wanders@uu.nl, nwanders@princeton.edu)
    institution :
    Utrecht University, Princeton University
    title :
    PCR-GLOBWB model output
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • lat: 360
    • lon: 720
    • time: 1826
    • time
      (time)
      datetime64[ns]
      2035-01-01 ... 2039-12-31
      standard_name :
      time
      long_name :
      Days since 1901-01-01
      array(['2035-01-01T00:00:00.000000000', '2035-01-02T00:00:00.000000000',
       '2035-01-03T00:00:00.000000000', ..., '2039-12-29T00:00:00.000000000',
       '2039-12-30T00:00:00.000000000', '2039-12-31T00:00:00.000000000'],
      dtype='datetime64[ns]')
    • lat
      (lat)
      float32
      89.75 89.25 88.75 ... -89.25 -89.75
      long_name :
      latitude
      units :
      degrees_north
      standard_name :
      latitude
      array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75], dtype=float32)
    • lon
      (lon)
      float32
      -179.75 -179.25 ... 179.25 179.75
      standard_name :
      longitude
      long_name :
      longitude
      units :
      degrees_east
      array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
      dtype=float32)
    • baseflow
      (time, lat, lon)
      float32
      ...
      standard_name :
      baseflow
      long_name :
      baseflow
      units :
      m.day-1
      [473299200 values with dtype=float32]
  • description :
    Niko Wanders (n.wanders@uu.nl, nwanders@princeton.edu)
    institution :
    Utrecht University, Princeton University
    title :
    PCR-GLOBWB model output
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • lat: 360
    • lon: 720
    • time: 1826
    • time
      (time)
      datetime64[ns]
      2035-01-01 ... 2039-12-31
      standard_name :
      time
      long_name :
      Days since 1901-01-01
      array(['2035-01-01T00:00:00.000000000', '2035-01-02T00:00:00.000000000',
       '2035-01-03T00:00:00.000000000', ..., '2039-12-29T00:00:00.000000000',
       '2039-12-30T00:00:00.000000000', '2039-12-31T00:00:00.000000000'],
      dtype='datetime64[ns]')
    • lat
      (lat)
      float32
      89.75 89.25 88.75 ... -89.25 -89.75
      long_name :
      latitude
      units :
      degrees_north
      standard_name :
      latitude
      array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75], dtype=float32)
    • lon
      (lon)
      float32
      -179.75 -179.25 ... 179.25 179.75
      standard_name :
      longitude
      long_name :
      longitude
      units :
      degrees_east
      array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
      dtype=float32)
    • groundwater_recharge
      (time, lat, lon)
      float32
      ...
      standard_name :
      groundwater_recharge
      long_name :
      groundwater_recharge
      units :
      m.day-1
      [473299200 values with dtype=float32]
  • description :
    Niko Wanders (n.wanders@uu.nl, nwanders@princeton.edu)
    institution :
    Utrecht University, Princeton University
    title :
    PCR-GLOBWB model output
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • lat: 360
    • lon: 720
    • time: 1826
    • time
      (time)
      datetime64[ns]
      2035-01-01 ... 2039-12-31
      standard_name :
      time
      long_name :
      Days since 1901-01-01
      array(['2035-01-01T00:00:00.000000000', '2035-01-02T00:00:00.000000000',
       '2035-01-03T00:00:00.000000000', ..., '2039-12-29T00:00:00.000000000',
       '2039-12-30T00:00:00.000000000', '2039-12-31T00:00:00.000000000'],
      dtype='datetime64[ns]')
    • lat
      (lat)
      float32
      89.75 89.25 88.75 ... -89.25 -89.75
      long_name :
      latitude
      units :
      degrees_north
      standard_name :
      latitude
      array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75], dtype=float32)
    • lon
      (lon)
      float32
      -179.75 -179.25 ... 179.25 179.75
      standard_name :
      longitude
      long_name :
      longitude
      units :
      degrees_east
      array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
      dtype=float32)
    • groundwater_storage
      (time, lat, lon)
      float32
      ...
      standard_name :
      groundwater_storage
      long_name :
      non_fossil_groundwater_storage
      units :
      m
      [473299200 values with dtype=float32]
  • description :
    Niko Wanders (n.wanders@uu.nl, nwanders@princeton.edu)
    institution :
    Utrecht University, Princeton University
    title :
    PCR-GLOBWB model output
Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • lat: 360
    • lon: 720
    • time: 1826
    • time
      (time)
      datetime64[ns]
      2035-01-01 ... 2039-12-31
      standard_name :
      time
      long_name :
      Days since 1901-01-01
      array(['2035-01-01T00:00:00.000000000', '2035-01-02T00:00:00.000000000',
       '2035-01-03T00:00:00.000000000', ..., '2039-12-29T00:00:00.000000000',
       '2039-12-30T00:00:00.000000000', '2039-12-31T00:00:00.000000000'],
      dtype='datetime64[ns]')
    • lat
      (lat)
      float32
      89.75 89.25 88.75 ... -89.25 -89.75
      long_name :
      latitude
      units :
      degrees_north
      standard_name :
      latitude
      array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75], dtype=float32)
    • lon
      (lon)
      float32
      -179.75 -179.25 ... 179.25 179.75
      standard_name :
      longitude
      long_name :
      longitude
      units :
      degrees_east
      array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
      dtype=float32)
    • total_thickness_of_water_storage
      (time, lat, lon)
      float32
      ...
      standard_name :
      total_thickness_of_water_storage
      long_name :
      total_thickness_of_water_storage
      units :
      m
      [473299200 values with dtype=float32]
  • description :
    Niko Wanders (n.wanders@uu.nl, nwanders@princeton.edu)
    institution :
    Utrecht University, Princeton University
    title :
    PCR-GLOBWB model output

In addition, we load the precipitation and bias corrected precipitation data

##Load precipitation data for the flood
Start_flood1,Year_flood1,Ensemble_flood1, variable = [13,2037,13,'pr']
Precipitation = xr.open_dataset(r'/home/tike/Ec_data/' + variable + '/S' + '%02d' % Start_flood1 + '/' + variable + '_m_ECEarth_PD_s' + '%02d' % Start_flood1 + 'r'  + '%02d' % Ensemble_flood1 + '_' + '%04d' %Year_flood1 + '.nc')
Precipitation = Precipitation['pr'].sel(time='2037-07-16')/31 ##m/d
Precipitation.attrs={'long_name': 'EC-Earth precipitation',
 'units': 'm/d',
 'grid_type': 'gaussian',
 'short_name': 'pr'}
Precipitation
## Bias corrected precipitation
Precip_biascor = xr.open_dataset(dirname + 'pr_d_ECEarth_PD_s13r13_2037.nc') #Streamflow
Precipitation_biascor = Precip_biascor['pr'].sel(time=slice('2037-07-01','2037-08-01')).mean('time')
Precipitation_biascor.attrs={'long_name': 'Bias corrected precipitation',
 'units': 'm/d',
 'short_name': 'pr'}
Precipitation_biascor
Show/Hide data repr Show/Hide attributes
xarray.DataArray
'pr'
  • time: 1
  • lat: 160
  • lon: 320
  • 0.0014859589 0.0014912853 0.0014966117 ... 0.0001591007 0.00015935538
    array([[[0.00148596, 0.00149129, 0.00149661, ..., 0.00146513,
         0.00147207, 0.00147902],
        [0.00126342, 0.00126648, 0.00126954, ..., 0.00122361,
         0.00123688, 0.00125015],
        [0.00116569, 0.00118065, 0.00119562, ..., 0.00110212,
         0.00112331, 0.0011445 ],
        ...,
        [0.00015527, 0.00015763, 0.00016   , ..., 0.00015064,
         0.00015218, 0.00015373],
        [0.00016086, 0.00016127, 0.00016167, ..., 0.00015947,
         0.00015993, 0.00016039],
        [0.00015961, 0.00015946, 0.00015931, ..., 0.00015885,
         0.0001591 , 0.00015936]]], dtype=float32)
    • lon
      (lon)
      float64
      0.0 1.125 2.25 ... 357.8 358.9
      standard_name :
      longitude
      long_name :
      longitude
      units :
      degrees_east
      axis :
      X
      array([  0.   ,   1.125,   2.25 , ..., 356.625, 357.75 , 358.875])
    • lat
      (lat)
      float64
      89.14 88.03 86.91 ... -88.03 -89.14
      standard_name :
      latitude
      long_name :
      latitude
      units :
      degrees_north
      axis :
      Y
      array([ 89.141519,  88.029429,  86.910771,  85.790629,  84.669924,  83.548947,
        82.427818,  81.306595,  80.18531 ,  79.063982,  77.942624,  76.821243,
        75.699844,  74.578432,  73.457008,  72.335576,  71.214136,  70.09269 ,
        68.97124 ,  67.849784,  66.728326,  65.606864,  64.485399,  63.363932,
        62.242462,  61.120991,  59.999518,  58.878044,  57.756569,  56.635092,
        55.513614,  54.392135,  53.270655,  52.149175,  51.027694,  49.906212,
        48.784729,  47.663246,  46.541763,  45.420279,  44.298794,  43.177309,
        42.055824,  40.934338,  39.812852,  38.691366,  37.56988 ,  36.448393,
        35.326906,  34.205418,  33.083931,  31.962443,  30.840955,  29.719467,
        28.597979,  27.476491,  26.355002,  25.233514,  24.112025,  22.990536,
        21.869047,  20.747558,  19.626069,  18.50458 ,  17.383091,  16.261601,
        15.140112,  14.018622,  12.897133,  11.775643,  10.654153,   9.532664,
         8.411174,   7.289684,   6.168194,   5.046704,   3.925215,   2.803725,
         1.682235,   0.560745,  -0.560745,  -1.682235,  -2.803725,  -3.925215,
        -5.046704,  -6.168194,  -7.289684,  -8.411174,  -9.532664, -10.654153,
       -11.775643, -12.897133, -14.018622, -15.140112, -16.261601, -17.383091,
       -18.50458 , -19.626069, -20.747558, -21.869047, -22.990536, -24.112025,
       -25.233514, -26.355002, -27.476491, -28.597979, -29.719467, -30.840955,
       -31.962443, -33.083931, -34.205418, -35.326906, -36.448393, -37.56988 ,
       -38.691366, -39.812852, -40.934338, -42.055824, -43.177309, -44.298794,
       -45.420279, -46.541763, -47.663246, -48.784729, -49.906212, -51.027694,
       -52.149175, -53.270655, -54.392135, -55.513614, -56.635092, -57.756569,
       -58.878044, -59.999518, -61.120991, -62.242462, -63.363932, -64.485399,
       -65.606864, -66.728326, -67.849784, -68.97124 , -70.09269 , -71.214136,
       -72.335576, -73.457008, -74.578432, -75.699844, -76.821243, -77.942624,
       -79.063982, -80.18531 , -81.306595, -82.427818, -83.548947, -84.669924,
       -85.790629, -86.910771, -88.029429, -89.141519])
    • time
      (time)
      datetime64[ns]
      2037-07-16T12:00:00
      standard_name :
      time
      bounds :
      time_bnds
      array(['2037-07-16T12:00:00.000000000'], dtype='datetime64[ns]')
  • long_name :
    EC-Earth precipitation
    units :
    m/d
    grid_type :
    gaussian
    short_name :
    pr
Show/Hide data repr Show/Hide attributes
xarray.DataArray
'pr'
  • lat: 360
  • lon: 720
  • 0.0011171534 0.0011171527 0.0011103832 ... 0.0006968823 0.00069688243
    array([[0.00111715, 0.00111715, 0.00111038, ..., 0.00111862, 0.00111991,
        0.00111991],
       [0.00108417, 0.00107514, 0.0010679 , ..., 0.00108463, 0.00108655,
        0.00108733],
       [0.0010632 , 0.00105866, 0.00105412, ..., 0.00107279, 0.00107091,
        0.00106958],
       ...,
       [0.00063694, 0.0006386 , 0.00064018, ..., 0.00063148, 0.00063315,
        0.00063511],
       [0.00066979, 0.00066769, 0.0006662 , ..., 0.00066196, 0.00066592,
        0.00066873],
       [0.00069784, 0.00069784, 0.00069871, ..., 0.00069583, 0.00069688,
        0.00069688]], dtype=float32)
    • lat
      (lat)
      float32
      89.75 89.25 88.75 ... -89.25 -89.75
      long_name :
      latitude
      units :
      degrees_north
      standard_name :
      latitude
      array([ 89.75,  89.25,  88.75, ..., -88.75, -89.25, -89.75], dtype=float32)
    • lon
      (lon)
      float32
      -179.75 -179.25 ... 179.25 179.75
      standard_name :
      longitude
      long_name :
      longitude
      units :
      degrees_east
      array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75],
      dtype=float32)
  • long_name :
    Bias corrected precipitation
    units :
    m/d
    short_name :
    pr

We load the masks and pre-process the discharge and runoff variables

mask_Amazon = (masks['Amazon']==1).squeeze('time')# USE squeeze!! instead of drop
mask_Xingu = (masks['Xingu']==1).squeeze('time')# USE squeeze!!
mask_Tapajos = (masks['Tapajos']==1).squeeze('time')# USE squeeze!!

discharge = Discharge['discharge'].sel(time=slice('2037-07','2037-08')).where(mask_Amazon).mean('time')
discharge.attrs={'long_name': 'Discharge',
 'units': 'm3/s'}

runoff = Direct_runoff['direct_runoff'].sel(time=slice('2037-07','2037-08')).where(mask_Amazon).mean('time')
runoff.attrs={'long_name': 'Direct runoff',
 'units': 'm/d'}

/home/tike/miniconda3/envs/exp/lib/python3.8/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis=axis, dtype=dtype)

And then we plot!

fig, axs = plt.subplots(2, 2,figsize=(10,7),subplot_kw={'projection': ccrs.Mercator()})

discharge.plot.pcolormesh(ax=axs[0, 0],transform=ccrs.PlateCarree(),cmap=plt.cm.Blues)
runoff.plot.pcolormesh(ax=axs[0, 1],transform=ccrs.PlateCarree(),cmap=plt.cm.Blues)
Precipitation.plot(ax=axs[1, 0], robust = True, transform=ccrs.PlateCarree(),cmap=plt.cm.Blues)
Precipitation_biascor.plot.pcolormesh(ax=axs[1, 1],  transform=ccrs.PlateCarree(),cmap=plt.cm.Blues)


titles = ['Discharge', 'Runoff', 'Raw Precipitation', 'Corrected Precipitation']

for i, ax in enumerate(axs.flat):
    ax.set_title(titles[i])
    ax.set_extent([-85, -45, 15, -20])
    ax.coastlines(resolution='110m')
    ax.add_feature(cartopy.feature.BORDERS, linestyle=':')
    gl = ax.gridlines(
        crs=ccrs.PlateCarree(),
        draw_labels=True,
        linewidth=1,
        color='gray',
        alpha=0.5,
        linestyle='--')
    gl.xlabels_top  = False
#     if i not in [0,3,6,9]:
#         gl.ylabels_left = False
#     if i not in [9,10,11]:
#         gl.xlabels_bottom = False
    gl.ylabels_right = False
    gl.ylocator = mticker.FixedLocator(np.arange(-20,30,10))
    gl.xlocator = mticker.FixedLocator(np.arange(-90,-30,10))

    
# Runoff.where((masks['Amazon']==1).drop('time')).plot(ax=axs[1, 0],transform=ccrs.PlateCarree(),cmap=plt.cm.Blues)
plt.savefig('../Graphs/paper/Flood_source.png', dpi=300)

<matplotlib.collections.QuadMesh at 0x2acb57650c70>

<matplotlib.collections.QuadMesh at 0x2acb57650d30>

<matplotlib.collections.QuadMesh at 0x2acb5774feb0>

<matplotlib.collections.QuadMesh at 0x2acb57817070>

Text(0.5, 1.0, 'Discharge')

<cartopy.mpl.feature_artist.FeatureArtist at 0x2acb57860d90>

<cartopy.mpl.feature_artist.FeatureArtist at 0x2acb5262f760>

Text(0.5, 1.0, 'Runoff')

<cartopy.mpl.feature_artist.FeatureArtist at 0x2acb5262faf0>

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Text(0.5, 1.0, 'Raw Precipitation')

<cartopy.mpl.feature_artist.FeatureArtist at 0x2acb57633d00>

<cartopy.mpl.feature_artist.FeatureArtist at 0x2acb5262fe20>

Text(0.5, 1.0, 'Corrected Precipitation')

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<cartopy.mpl.feature_artist.FeatureArtist at 0x2acb52656460>
../_images/Credibility_impact_model_16_16.png

We also plot the fluxes and states averaged over the Amazon basin

Direct_runoff['direct_runoff'].sel(time=slice('2037-07','2037-08')).where(mask_Amazon).mean(['lat','lon']).plot(label='direct_runoff')
Runoff['land_surface_runoff'].sel(time=slice('2037-07','2037-08')).where(mask_Amazon).mean(['lat','lon']).plot(label='land_surface_runoff')
Baseflow['baseflow'].sel(time=slice('2037-07','2037-08')).where(mask_Amazon).mean(['lat','lon']).plot(label='baseflow')
Groundwater_recharge['groundwater_recharge'].sel(time=slice('2037-07','2037-08')).where(mask_Amazon).mean(['lat','lon']).plot(label='groundwater_recharge')

plt.legend()
plt.show()
plt.savefig('../Graphs/paper/Flood_source_fluxes.svg', dpi=300)

Groundwater_storage['groundwater_storage'].sel(time=slice('2037-07','2037-08')).where(mask_Amazon).mean(['lat','lon']).plot(label='groundwater_storage')
Storage['total_thickness_of_water_storage'].sel(time=slice('2037-07','2037-08')).where(mask_Amazon).mean(['lat','lon']).plot(label='total_thickness_of_water_storage')
plt.legend()
plt.savefig('../Graphs/paper/Flood_source_storage.svg', dpi=300)

[<matplotlib.lines.Line2D at 0x2b15e0c6f9a0>]

[<matplotlib.lines.Line2D at 0x2b15e0ca1970>]

[<matplotlib.lines.Line2D at 0x2b15e0c9bee0>]

[<matplotlib.lines.Line2D at 0x2b15e0ca1e80>]

<matplotlib.legend.Legend at 0x2b15e0c9b5e0>
../_images/Credibility_impact_model_18_5.png 
[<matplotlib.lines.Line2D at 0x2b15e0cd3400>]

[<matplotlib.lines.Line2D at 0x2b15e0cffd60>]

<matplotlib.legend.Legend at 0x2b15e0b7a8b0>
../_images/Credibility_impact_model_18_9.png