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e.g. Example scripts for EFAS and GloFAS


Set up the environment


# create a local virtual environment, you can call it as you wish, here 'myenv' is used.
conda create -n myenv python=3.8 

# add repository channel
conda config --add channels conda-forge

# activate the local environment. 
conda activate myenv

# install the required packages
conda install -c conda-forge/label/main xarray cfgrib eccodes 
conda install cartopy netcdf4 matplotlib

# and the cdsapi
pip install cdsapi

EFAS

Retrieve data

These exercises require EFAS model output parameters and auxiliary data. You can retrieve them using the CDS API, as described in the code blocks below:

Model outputs

import cdsapi

c = cdsapi.Client()

# Climatology river discharge
c.retrieve('efas-historical', {
  "format": "netcdf",
  "hday": ["15","16","17","18"],
  "hmonth": "november",
  "hyear": "2020",
  "model_levels": "surface_level",
  "system_version": "version_4_0",
  "time": ["00:00","06:00","18:00"],
  "variable": "river_discharge_in_the_last_6_hours"
},
'clim_2020111500.nc')

# Forecast river discharge
c.retrieve(
    'efas-forecast',
    {
        'format': 'netcdf',
        'originating_centre': 'ecmwf',
        'product_type': 'ensemble_perturbed_forecasts',
        'variable': 'river_discharge_in_the_last_6_hours',
        'model_levels': 'surface_level',
        'year': '2020',
        'month': '11',
        'day': '15',
        'time': '00:00',
        'leadtime_hour': [
            '6', '12', '18',
            '24', '30', '36',
            '42', '48', '54',
            '60', '66', '72',
        ],
    },
    'eue_2020111500.nc')

# Forecast soil moisture
c.retrieve(
    'efas-forecast',
    {
        'format': 'netcdf',
        'originating_centre': 'ecmwf',
        'product_type': 'high_resolution_forecast',
        'variable': [
            'soil_depth', 'volumetric_soil_moisture',
        ],
        'model_levels': 'soil_levels',
        'year': '2019',
        'month': '01',
        'day': '30',
        'time': '00:00',
        'leadtime_hour': [
            '6', '12', '18',
            '24', '30', '36',
            '42', '48', '54',
            '60', '66', '72',
        ],
        'soil_level': [
            '1', '2', '3',
        ],
    },
    'eud_2019013000.nc')


Auxiliary data

From the CDS Static files link, download the following NetCDFs:

thmin1.nc, thmin2.nc, thmin3.nc, thmax1.nc, thmax2.nc, thmax3.nc


When the marsurl will be in production one can download the auxiliary data simply requesting them through a CDS API request:


import cdsapi

c = cdsapi.Client()

c.retrieve(
    'efas-forecast,
    {
        'format': 'netcdf',
        'variable': [
            'field_capacity', 'wilting_point',
        ],
        'soil_level': [
            '1', '2', '3',
        ],
    },
    'auxiliary.zip')

And then unzip the auxiliary.zip

$ unzip auxiliary.zip

Archive:  auxiliary.zip
 extracting: thmax_1.nc
 extracting: thmin_1.nc
 extracting: thmax_2.nc
 extracting: thmin_2.nc
 extracting: thmax_3.nc
 extracting: thmin_3.nc


Plot map discharge


import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cf
import numpy as np
import pandas as pd
import xarray as xr

var_name = "dis06"

ds = xr.open_dataset('../data/clim_2020111500.nc')

cmap = plt.cm.get_cmap('jet').copy()
cmap.set_under('white')

# set the coordinate reference system for EFAS
crs = ccrs.LambertAzimuthalEqualArea(central_longitude=10,central_latitude=52,false_easting=4321000,false_northing=3210000)

# define the filter for visualizing only discharge above that value
vmin = 20

# selecting a date
ds = ds[var_name].isel(time=1)

# Plot map discharge > 20 m/s 
fig, ax = plt.subplots(1,1,subplot_kw={'projection': crs}, figsize=(20,20) )
ax.gridlines(crs=crs, linestyle="-")
ax.coastlines()
ax.add_feature(cf.BORDERS)
sc = ds[var_name].plot(ax=ax,cmap=cmap,vmin=vmin,add_colorbar=False)
ax.set_title(f'{ds[var_name].long_name}> {vmin} $m^{3}s^{-1}$')
cbar = plt.colorbar(sc, shrink=.5,)
cbar.set_label(ds[var_name].GRIB_name)


Plot Discharge Timeseries

import numpy as np
import xarray as xr



Plot Soil Wetness Index Map

import xarray as xr
from matplotlib.pyplot import colorbar
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cf
import numpy as np
import pandas as pd

# Load the wilting point and field capacity datasets
thmin1 = xr.open_dataset('thmin1.nc')
thmin2 = xr.open_dataset('thmin2.nc')
thmin3 = xr.open_dataset('thmin3.nc')
thmax1 = xr.open_dataset('thmax1.nc')
thmax2 = xr.open_dataset('thmax2.nc')
thmax3 = xr.open_dataset('thmax3.nc')

# load the EFAS volumetric soil moisture dataset
ds = xr.open_dataset('eud_2019013000.nc')


# plotting function
def make_cmap(colors, position=None):
    '''
    make_cmap takes a list of tuples which contain RGB values. 
    The RGB values are [0 to 255] 
    make_cmap returns a cmap with equally spaced colors.
    Arrange your tuples so that the first color is the lowest value for the
    colorbar and the last is the highest.
    position contains values from 0 to 1 to dictate the location of each color.
    '''
    import matplotlib as mpl
    import numpy as np
    import sys
    bit_rgb = np.linspace(0,1,256)
    if position == None:
        position = np.linspace(0,1,len(colors))
    else:
        if len(position) != len(colors):
            sys.exit("position length must be the same as colors")
        elif position[0] != 0 or position[-1] != 1:
            sys.exit("position must start with 0 and end with 1")
    for i in range(len(colors)):
        colors[i] = (bit_rgb[colors[i][0]],
                     bit_rgb[colors[i][1]],
                     bit_rgb[colors[i][2]])
    cdict = {'red':[], 'green':[], 'blue':[]}
    for pos, color in zip(position, colors):
        cdict['red'].append((pos, color[0], color[0]))
        cdict['green'].append((pos, color[1], color[1]))
        cdict['blue'].append((pos, color[2], color[2]))

    cmap = mpl.colors.LinearSegmentedColormap('my_colormap',cdict,256)
    return cmap


# There are 3 layers of Volumetric Soil Moisture. We need to get the mean across all timesteps
mean_vsw = ds.vsw.mean('step') # Mean Volumetric Soil Moisture for all time steps
th1 = mean_vsw[0,:,:]          # Mean Volumetric Soil Moisture for Layer 1
th2 = mean_vsw[1,:,:]          # Mean Volumetric Soil Moisture for Layer 2

# We need to have the values for the first and second soil depths
sd1 = ds.sod[0,0,:,:].values
sd2 = ds.sod[0,1,:,:].values - ds.sod[0,0,:,:].values

data=(thmin1.wiltingpoint.values * sd1 + thmin2.wiltingpoint.values * sd2)/(sd1 + sd2)
thmin = xr.DataArray(data,dims=(ds.y.name,ds.x.name),name='thmin')
ds['thmin'] = thmin

data=(thmax2.fieldcapacity.values * sd1 + thmax2.fieldcapacity.values * sd2)/(sd1 + sd2)
thmax = xr.DataArray(data,dims=(ds.y.name,ds.x.name),name='thmax')
ds['thmax'] = thmax

data=((th1 * sd1) + (th2 * sd2))/(sd1 +sd2)
smtot = xr.DataArray(data,dims=(ds.y.name,ds.x.name),name='smtot')
ds['smtot'] = smtot 

data=(((smtot - thmin)/(thmax - thmin)))
SM = xr.DataArray(data,dims=(ds.y.name,ds.x.name),name='SM')
ds['SM'] = SM




colors = [(64,0,3), (133,8,3), (249,0,0), (250,106,0), (249,210,0), (189,248,255), (92,138,255), (0,98,255), (0,0,255), (0,0,88)]
### Create an array or list of positions from 0 to 1.
position = [0, 0.2, 0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]

cmap = make_cmap(colors, position=position)

crs = ccrs.LambertAzimuthalEqualArea(central_longitude=10,central_latitude=52,false_easting=4321000,false_northing=3210000)

fig, ax = plt.subplots(1,1,subplot_kw={'projection': crs}, figsize=(15,15) )
ax.gridlines(crs=crs, linestyle="-")
ax.coastlines()
ax.add_feature(cf.BORDERS)
sc = ds["SM"].plot(ax=ax,cmap=cmap,add_colorbar=False)
cbar = plt.colorbar(sc, shrink=.5,)
cbar.set_label("SM")



Plot Snow depth water equivalent map



GLOFAS
GloFAS Medium-range reforecast (download time series)
The script shows how to retrieve the control reforecasts product from year 1999 to 2018, relative to the date 2019-01-03, for two station coordinates, one on the river network of the Thames and the other one on the Po river. 
import cdsapi
from datetime import datetime, timedelta



def get_monthsdays(start =[2019,1,1],end=[2019,12,31]):
    # reforecast time index
    start, end = datetime(*start),datetime(*end)
    days = [start + timedelta(days=i) for i in range((end - start).days + 1)]
    monthday = [d.strftime("%B-%d").split("-")  for d in days if d.weekday() in [0,3] ]   

    return monthday

# get date index
MONTHSDAYS = get_monthsdays() 

if __name__ == '__main__':
    c = cdsapi.Client()


    # set station coordinates (lat,lon)
    COORDS = {
            "Thames":[51.35,-0.45],
            "Po":[44.85, 11.65]
            }
        
    # select all years
    YEARS  = ['%d'%(y) for y in range(1999,2019)]
    
    # select all leadtime hours
    LEADTIMES = ['%d'%(l) for l in range(24,1128,24)]
    
    # loop over date index
    for md in MONTHSDAYS:

        month = md[0].lower()
        day = md[1]
        
		# loop over coordinates
        for station in COORDS:

            station_point_coord = COORDS[station]*2 # coordinates input for the area keyword
            
            c.retrieve(
                'cems-glofas-reforecast',
                {
                    'system_version': 'version_2_2',
                    'variable': 'river_discharge_in_the_last_24_hours',
                    'format': 'grib',
                    'hydrological_model': 'htessel_lisflood',
                    'product_type': 'control_reforecast',
                    'area':station_point_coord, 
                    'hyear': YEARS,
                    'hmonth': month ,
                    'hday': day ,
                    'leadtime_hour': LEADTIMES,
                },
                f'glofas_reforecast_{station}_{month}_{day}.grib')


Plot retrieved data:

import xarray as xr
import numpy as np
import matplotlib.pyplot as plt

YEARS = range(1999,2019)

# read dataset
ds = xr.open_dataset("glofas_reforecast_Po_january_03.grib",engine="cfgrib")
da = ds.isel(latitude=0,longitude=0).dis24

# plotting parameters
n = len(YEARS) 
colors = plt.cm.jet(np.linspace(0,1,n))
ls = ["-","--",":"]

# plot
fig,ax = plt.subplots(1,1,figsize=(16,8))

steps = list(range(24,dsa.shape[1]*24+24,24))

for i,year in enumerate(YEARS):
    y = da.sel(time=f"{year}-01-03").values
    ax.plot(steps,y,label=f"{year}-01-03",color=colors[i],ls=ls[i%3])
    plt.legend(bbox_to_anchor=(0.9,-0.1),ncol=7)

ax.set_xlim([0,steps[-1]])
ax.set_xlabel("leadtime hours")
ax.set_ylabel("m**3 s**-1")

plt.title(f"Po river discharge at {float(ds.latitude.values),round(float(ds.longitude.values),2)}")



GloFAS Medium-range reforecast (download time series for an event)
This script shows how to retrieve a point time series reforecast on the river Thames for a single forecast reference time, specifically the 11th of July 2007.
import cdsapi
from datetime import datetime, timedelta



def get_monthsdays(start =[2019,1,1],end=[2019,12,31]):
    # reforecast time index
    start, end = datetime(*start),datetime(*end)
    days = [start + timedelta(days=i) for i in range((end - start).days + 1)]
    monthday = [d.strftime("%B-%d").split("-")  for d in days if d.weekday() in [0,3] ]   

    return monthday



if __name__ == '__main__':
    c = cdsapi.Client()


    # station coordinates (lat,lon)
    COORDS = {
            "Thames":[51.35,-0.45]
            }
    
    # select date index corresponding to the event 
    MONTHSDAYS = get_monthsdays(start =[2019,7,11],end=[2019,7,11])

    YEAR  = '2007' 

    LEADTIMES = ['%d'%(l) for l in range(24,1128,24)]
    
    # loop over date index (just 1 in this case)
    for md in MONTHSDAYS:

        month = md[0].lower()
        day = md[1]

        # loop over station coordinates
        for station in COORDS:

            station_point_coord = COORDS[station]*2 # coordinates input for the area keyword

            c.retrieve(
                'cems-glofas-reforecast',
                {
                    'system_version': 'version_2_2',
                    'variable': 'river_discharge_in_the_last_24_hours',
                    'format': 'grib',
                    'hydrological_model': 'htessel_lisflood',
                    'product_type': ['control_reforecast','ensemble_perturbed_reforecasts'],
                    'area':station_point_coord, 
                    'hyear': YEAR,
                    'hmonth': month ,
                    'hday': day ,
                    'leadtime_hour': LEADTIMES,
                },
                f'glofas_reforecast_{station}_{month}_{day}.grib')



In July 2007 a series of flooding events hit the UK, in particular in some areas of the upper Thames catchment up to 120 mm of rain fell between 19th and 20th of July.
Plot retrieved data:
import xarray as xr
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime,timedelta

forecast_reference_time = datetime(2017,7,11)

# read perturbed ensembles (dataType:pf)
ds = xr.open_dataset("glofas_reforecast_Thames_july_11.grib",engine="cfgrib",backend_kwargs={'filter_by_keys':{'dataType': 'pf'}})
da = ds.isel(latitude=0,longitude=0).dis24

# read control (dataType:cf)
cds = xr.open_dataset("glofas_reforecast_Thames_july_11.grib",engine="cfgrib",backend_kwargs={'filter_by_keys':{'dataType': 'cf'}})
cda = cds.isel(latitude=0,longitude=0).dis24


start = forecast_reference_time
end = start + timedelta(45)
time = pd.date_range(start,end)

# plotting parameters
ls = ["-","--",":"]
locator = mdates.AutoDateLocator(minticks=2, maxticks=46)
formatter = mdates.DateFormatter("%b-%d")

# plot

fig,ax = plt.subplots(1,1,figsize=(16,8))
ax.plot(time,cda.values,label=f"control",color="red")
for i,number in enumerate(range(0,10)):
    y = da.isel(number=number).values
    ax.plot(time,y,label=f"ensemble {number}",color=colors[i],ls=ls[i%3])
    plt.legend(bbox_to_anchor=(1,1),ncol=2)

ax.axvspan(datetime(2017,7,19), datetime(2017,7,21), alpha=0.3, color='blue') # highlight event
ax.set_xlabel("date")
ax.set_ylabel("m**3 s**-1")
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
ax.set_xlim([time[0],time[-1]])
plt.annotate("peak\nrainfall\nevent",(datetime(2017,7,19,3),85),rotation=0)
plt.xticks(rotation=70)
plt.title(f"Forecast reference time: 11-07-2007 - Thames river discharge at {float(ds.latitude.values),round(float(ds.longitude.values-360),2)}")