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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 two scenarios to perform those operations:

  • Remotely - Using the CDS API to perform the operation remotely on the CDS compute nodes and retrieve only the reduced data.
  • Locally - Using the CDS API to retrieve the entire data and perform the operation locally.


For the exercise on extracting time series on the local machine, we are going to use the latitude and longitude coordinates from a tiny subset of the GRDC dataset. Copy the content of the box into an empty file named "GRDC.csv", the file should reside in your working folder.

GRDC dataset 
grdc_no,wmo_reg,sub_reg,river,station,country,lat,long,area,altitude
6118010,6,18,CLAIE,SAINT-JEAN-BREVELAY,FR,47.824831,-2.703308,137.0,99.98
6118015,6,18,YVEL,LOYAT (PONT D129),FR,47.993815,-2.368849,315.0,88.26
6118020,6,18,"ARON, RUISSEAU D'",GRAND-FOUGERAY (LA BERNADAISE),FR,47.71222,-1.690835,118.0,68.0
6118025,6,18,"CANUT, RUISSEAU DE",SAINT-JUST (LA RIVIERE COLOMBEL),FR,47.775309,-1.979609,37.0,80.22
6118030,6,18,AFF,PAIMPONT (PONT DU SECRET),FR,47.981631,-2.143762,30.2,119.01
6118050,6,18,COET-ORGAN,QUISTINIC (KERDEC),FR,47.904164,-3.201265,47.7,94.42
6118060,6,18,EVEL,GUENIN,FR,47.899928,-2.975167,316.0,95.16
6118070,6,18,STER-GOZ,BANNALEC (PONT MEYA),FR,47.906833,-3.752172,69.7,85.08
6118080,6,18,MOROS,CONCARNEAU (PONT D22),FR,47.882934,-3.875375,20


Then, retrieve the following datasets into the working folder.

Get the GloFAS Historical data 
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'cems-glofas-historical',
    {
        'variable': 'river_discharge_in_the_last_24_hours',
        'format': 'grib',
        'hydrological_model': 'lisflood',
        'product_type': 'intermediate',
        'hyear': '2021',
        'hmonth': 'january',
        'hday': [
            '01', '02', '03',
            '04', '05', '06',
            '07', '08', '09',
            '10', '11', '12',
            '13', '14', '15',
            '16', '17', '18',
            '19', '20', '21',
            '22', '23', '24',
            '25', '26', '27',
            '28', '29', '30',
            '31',
        ],
        'system_version': 'version_3_1',
    },
    'glofas_historical.grib')


Get the EFAS reforecast data 
import cdsapi

c = cdsapi.Client()

c.retrieve(
    'efas-reforecast',
    {
        'format': 'grib',
        'product_type': 'ensemble_perturbed_reforecasts',
        'variable': 'river_discharge_in_the_last_6_hours',
        'model_levels': 'surface_level',
        'hyear': '2007',
        'hmonth': 'march',
        'hday': [
            '04', '07',
        ],
        'leadtime_hour': [
            '0', '12', '18',
            '6',
        ],
    },
    'efas_reforecast.grib')

GloFAS

CDS API

Time series extraction:


Extract timeseries: GloFAS Medium-range Reforecast 
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')

Area cropping:


Area cropping: GloFAS Medium-range reforecast  
## === 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')
Area cropping: GloFAS Seasonal Forecast 
## === 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')
Area cropping: GloFAS Seasonal Reforecast 
## === 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')

Local machine



Time series extraction:


Script 
import xarray as xr
import pandas as pd

parameter = "dis24"

ds = xr.open_dataset("glofas_historical.grib", engine="cfgrib",backend_kwargs={'time_dims':['time']})
df = pd.read_csv("GRDC.csv")

total = len(df)

rows = []
count = 0
for lon, lat, id in zip(df.long, df.lat, df.grdc_no):
    extracted = ds.sel(longitude=lon, latitude=lat, method="nearest")[parameter]
    df_temp = extracted.drop_vars(["surface"]).to_dataframe().reset_index()
    df_temp["grdc"] = str(id)
    df_temp = df_temp.set_index(["grdc", "time"])
    rows.append(df_temp)
    count += 1
    print(f"progress: {count/total*100} %")

out = pd.concat(rows)
out.to_csv("extracted.csv", index="grdc")


Area cropping:


Script 
import xarray as xr

# Rhine's basin bounding box
bbox = [50.972204, 46.296530, 5.450796, 11.871059]  # N,S,W,E

ds = xr.open_dataset("glofas_historical.grib", engine="cfgrib")

ds_cropped = ds.sel(
    longitude=slice(bbox[2], bbox[3]), latitude=slice(bbox[0], bbox[1])
)

ds_cropped.to_netcdf("glofas_historical_cropped.nc")

EFAS


Coordinates precision

When transforming from lat/lon (source coordinates) to projected LAEA (target coordinates), you need to consider that the number of decimal places of the source coordinates affects the target coordinates precision:

An interval of 0.001 degrees corresponds to about 100 metres in LAEA.

An interval of 0.00001 degrees corresponds to about 1 metre in LAEA.

CDS API

to update once cropping works....

Time series extraction:


Area cropping:

Local machine


Time series extraction:


Important - Download upstream area

EFAS's x and  y coordinates, when converted from GRIB to NetCDF, are not projected coordinates but matrix indexes (i, j), It is necessary to download the upstream area static file that contains the projected coordinates and replace it in EFAS.


Script 
import xarray as xr
import pandas as pd
from pyproj import Transformer,CRS

parameter = "dis06"

ds = xr.open_dataset("efas_reforecast.grib", engine="cfgrib")
df = pd.read_csv("GRDC.csv")
uparea = xr.open_dataset("ec_uparea4.0.nc") # the upstream area

# replace x, y
ds["x"] = uparea["x"]
ds["y"] = uparea["y"]

# define reprojection parameters
laea_proj = CRS.from_proj4("+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs")
transformer = Transformer.from_crs('epsg:4326', laea_proj, always_xy=True)

total = len(df)

rows = []
count = 0
for lon, lat, id in zip(df.long, df.lat, df.grdc_no):
    x1, y1 = transformer.transform(lon, lat)
    extracted = ds.sel(x=x1, y=y1, number = 1, method="nearest")[parameter]
    df_temp = extracted.drop_vars(["surface", "number"]).to_dataframe().reset_index()
    df_temp["grdc"] = str(id)
    df_temp = df_temp.drop("step", axis=1)
    df_temp = df_temp.set_index(["grdc","time"])
    rows.append(df_temp)
    count += 1
    print(f"progress: {count/total*100} %")

out = pd.concat(rows)
out.to_csv("extracted.csv", index="grdc")


Area cropping:


Script 
import xarray as xr
from pyproj import Transformer, CRS
import numpy as np

# Rhine's basin bounding box
bbox = [50.972204, 46.296530, 5.450796, 11.871059]  # N,S,W,E

ds = xr.open_dataset("efas_reforecast.grib", engine="cfgrib")
uparea = xr.open_dataset("ec_uparea4.0.nc")

# replace x, y
ds["x"] = uparea["x"]
ds["y"] = uparea["y"]

# define reprojection parameters
laea_proj = CRS.from_proj4(
    "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs"
)
transformer = Transformer.from_crs("epsg:4326", laea_proj, always_xy=True)

we = bbox[2:]
ns = bbox[:2]

we_xy, ns_xy = transformer.transform(we, ns)

we_xy = [np.floor(we_xy[0]), np.ceil(we_xy[1])]
ns_xy = [np.ceil(ns_xy[0]), np.floor(ns_xy[1])]

ds_cropped = ds.sel(
    x=slice(we_xy[0], we_xy[1]), y=slice(ns_xy[0], ns_xy[1])
)

ds_cropped.to_netcdf("efas_forecast_cropped.nc")
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