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There are many situations where a user is only interested in a subset of the dataset spatial domain.

For example, when comparing modelled river flow against observations, it is reasonable to being able to extract the timeseries at those point coordinates rather than dealing with many GB of data.

Similarly, when the focus is on a specific catchment it is likely that you want only that part of the spatial domain.

In summary, there two operations that are very popular on CEMS-Flood datasets:

Area cropping
Time series extraction

There are different ways to perform those operations:

From the CDS API (less data is downloaded)

Locally (full control on the process)

GloFAS

Example script to crop and extract time series from different GloFAS products:

## === retrieve GloFAS Medium-Range Reforecast ===

## === subset India, Pakistan, Nepal and Bangladesh region === 


import cdsapi
from datetime import datetime, timedelta


def get_monthsdays():

    start, end = datetime(2019, 1, 1), datetime(2019, 12, 31)
    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

MONTHSDAYS = get_monthsdays()

if __name__ == '__main__':
    c = cdsapi.Client()
    
    # user inputs
	BBOX = [40.05 ,59.95, 4.95, 95.05] # North West South East
    YEARS  = ['%d'%(y) for y in range(1999,2019)]
    LEADTIMES = ['%d'%(l) for l in range(24,1128,24)]
    
    # submit request
    for md in MONTHSDAYS:

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

        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': BBOX,# < - subset
                'hyear': YEARS,
                'hmonth': month ,
                'hday': day ,
                'leadtime_hour': LEADTIMES,
            },
            f'glofas_reforecast_{month}_{day}.grib')

## === retrieve GloFAS Seasonal Forecast ===

## === subset South America/Amazon region === 

import cdsapi


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

    YEARS  = ['%d'%(y) for y in range(2020,2022)]


    MONTHS = ['%02d'%(m) for m in range(1,13)]

    LEADTIMES = ['%d'%(l) for l in range(24,2976,24)]
    
    for year in YEARS:

        for month in MONTHS:
            
            c.retrieve(
                'cems-glofas-seasonal',
                {   
                'variable': 'river_discharge_in_the_last_24_hours',
                'format': 'grib',
                'year': year,
                'month': '12' if year == '2020' else month,
                'leadtime_hour': LEADTIMES,
				'area': [ 10.95, -90.95, -30.95, -29.95 ]

                },
                f'glofas_seasonal_{year}_{month}.grib')

## === retrieve GloFAS Seasonal Reforecast ===

## === subset South America/Amazon region === 

import cdsapi

if __name__ == '__main__':


    c = cdsapi.Client()

    YEARS  = ['%d'%(y) for y in range(1981,2021)]

    MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']

    LEADTIMES = ['%d'%(l) for l in range(24,2976,24)]
    
    for year in YEARS:
        for month in MONTHS:

            c.retrieve(
                'cems-glofas-seasonal-reforecast',
                {
                    'system_version': 'version_2_2',
                    'variable':'river_discharge_in_the_last_24_hours',
                    'format':'grib',
                    'hydrological_model':'htessel_lisflood',
                    'hyear': year,
                    'hmonth': month,
                    'leadtime_hour': LEADTIMES,
                    'area': [ 10.95, -90.95, -30.95, -29.95 ]
                }, 
                f'glofas_seasonal_reforecast_{year}_{month}.grib')

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.

Copernicus Emergency Management Service - CEMS > Extract subset of CEMS-Flood Data > image2021-5-5_18-6-35.png

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)}")

Copernicus Emergency Management Service - CEMS > Extract subset of CEMS-Flood Data > image2021-5-5_22-21-6.png