Data Summary

Global Runoff Data Centre (GRDC)

The Global Runoff Data Centre (GRDC) is an international data centre operating under the auspices of the World Meteorological Organization (WMO). Established in 1988 to support the research on global and climate change and integrated water resources management, it holds the most substantive collection of quality assured river discharge data on global scale.

There is no API available for the GRDC, but the data can be downloaded from the GRDC Data PORTAL.

Unlike ERA5 and GRUN, the GRDC data is not available in a gridded format. The discharge (streamflow) data is available at station level.

Check out the FAQ to define the way to download the data.

ERA5-Land

ERA5-Land Monthly Averages

• Covers the period from January 1950 to 2-3 months before the present
• ERA5-Land runs at enhanced resolution (9 km) - a regular latitude/longitude grid of 0.1°x0.1° via the CDS catalogue

This document summarizes the units for each variable available in the ERA5-Land monthly_averaged_reanalysis dataset via the CDS API.

Check the Documentation

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📏 Units for ERA5-Land Monthly Averaged Variables

Variable	Unit	Description
2m_temperature	Kelvin (K)	Air temperature at 2 meters above the surface. To convert to Celsius, subtract 273.15.
total_precipitation	m/day	Total precipitation accumulated over the day, expressed in meters of water equivalent per day. Multiply by 1000 to convert to mm/day. Multiply by the number of days in the month for total monthly precipitation.
surface_runoff	m/day	Surface runoff accumulated over the day, in meters of water equivalent. Multiply by 1000 for mm/day. Multiply by number of days in the month for monthly total.
snow_depth_water_equivalent	m	Instantaneous depth of snow in meters of water equivalent.
potential_evaporation	m/day	Potential evaporation per day in meters. Multiply by 1000 for mm/day. Multiply by number of days in the month for monthly total.
total_evaporation	m/day	Actual evapotranspiration per day in meters. Multiply by 1000 for mm/day. Multiply by number of days in the month for monthly total.

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🔄 Notes on Data Interpretation

• Accumulated Variables:

  • Values are daily means in meters/day.
  • To compute total for a month: monthly_total = daily_mean × number_of_days_in_month
  • Example: 0.004 m/day × 30 days = 0.12 m

• Instantaneous Variables:

  • Do not need accumulation over time.

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For more details, refer to the ERA5-Land documentation.

Extracting Data from file

Understanding stepType: Handling Instantaneous vs Accumulated Variables in ERA5-Land Monthly Averages

In ERA5-Land monthly reanalysis data, variables are processed differently depending on whether they are **instantaneous ** or accumulated. This affects how they are averaged and stored in GRIB files.

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🔄 Key stepType Values

stepType	Meaning
avgid	Monthly average of hourly data that was originally instantaneous (e.g., 2m temperature).
avgas	Monthly average of hourly data that was originally accumulated, then scaled to a rate (e.g., total precipitation → precipitation rate → monthly average).

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🧠 Why It Matters

• These different stepTypes must be handled separately when reading GRIB files using tools like cfgrib.
• Attempting to read a GRIB file with mixed stepTypes without filtering will cause errors.

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✅ How to Extract Data

Use cfgrib with filter_by_keys to open each type separately:

import xarray as xr

# For instantaneous variables (e.g., 2m_temperature)
ds_instant = xr.open_dataset("data.grib", engine="cfgrib",
                             backend_kwargs={"filter_by_keys": {"stepType": "avgid"}})

# For accumulated variables (e.g., total_precipitation)
ds_accum = xr.open_dataset("data.grib", engine="cfgrib",
                           backend_kwargs={"filter_by_keys": {"stepType": "avgas"}})

You can then merge the datasets if they share compatible dimensions.

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For more, see the CDS GRIB-to-netCDF changes.

GRUN

The dataset contains a gridded global reconstruction of monthly runoff timeseries. The data are at monthly resolution covering the period 1902-2014 on a 0.5 degrees (WGS84) grid in units of mm/day The data are provided in a NetCDFv4 file, that is downloadable from the GRUN repository.

GRUN Dataset Publication

In-situ streamflow observations from the GSIM dataset are used to train a machine learning algorithm that predicts monthly runoff rates based on antecedent precipitation and temperature from the Global Soil Wetness Project Phase 3 (GSWP3) meteorological forcing dataset.