EGU2020-15755, updated on 14 Jan 2022
EGU General Assembly 2020
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

GloFAS-ERA5 operational global river discharge reanalysis 1979-present

Shaun Harrigan1, Ervin Zsoter1, Lorenzo Alfieri2, Christel Prudhomme1,3,4, Peter Salamon2, Fredrik Wetterhall1, Christopher Barnard1, Hannah Cloke5,6,7, and Florian Pappenberger1
Shaun Harrigan et al.
  • 1European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK (
  • 2Disaster Risk Management Unit, European Commission Joint Research Centre (JRC), Ispra, Italy
  • 3Centre for Ecology and Hydrology (CEH), Wallingford, UK
  • 4Department of Geography and Environment, University of Loughborough, Loughborough, UK
  • 5Department of Geography and Environmental Science, University of Reading, Reading, UK
  • 6Department of Meteorology, University of Reading, Reading, UK
  • 7Department of Earth Sciences, Uppsala University, Uppsala, Sweden

Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of Earth system observations with advanced Numerical Weather Prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, known as a reanalysis. While the current generation of NWP output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly, and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; by coupling surface and sub-surface runoff from the HTESSEL land surface model used within ECMWF’s latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model.

This work presents the new GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1° at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 days behind real time). The reanalysis was evaluated against a global network of 1801 river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling-Gupta Efficiency Skill Score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for large scale hydrology applications ranging from monitoring global flood and drought conditions, understanding hydroclimatic variability and change, initialising hydrological forecasts, and as raw input to post-processing and machine learning methods that can add further value.

Data availibility: The dataset is openly available from the Copernicus Climate Change Service (C3S) Climate Data Store (C3S):!/dataset/cems-glofas-historical?tab=overview and further details and the evaluation of the dataset can be found in the accompanying data description paper: 

Data paper: Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979–present, Earth Syst. Sci. Data Discuss.,, 2020.

How to cite: Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979-present, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15755,, 2020.


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  • CC1: Comment on EGU2020-15755, Omar Müller, 12 May 2020

    Dear Shaun Harrigan,

    Very interesting work. Unfortunately I was not able to participate on the chat, so I was looking at the paper. I just want to confirm if the calibration of LISFLOOD implies a bias correction to match the observation values, or just adjust the LISFLOOD parameters to match the observed discharge variability?



    • AC1: Reply to CC1, Shaun Harrigan, 12 May 2020

      Dear Omar, 

      Thank you very much for your comment and interest in our work. The full version on LISFLOOD is not used. Rather it is a global version, outlined in Hirpa et al. (2018). In this version, the inputs are surface and sub-surface runoff from the HTESSEL land surface model, and the job LISFLOOD is threefold: 1.) route the runoff into and through the river network, 2.) add a baseflow component, and 3.) respresent lakes and reservoirs. 

      The details of the calibration of LISFLOOD parameters can also be found in Hirpa et al. (2018). In summary, eight parameters were calibrated (routing parameters,  groundwater parameters, and lake and reservoir parameters) against a network of 1287 river discharge stations. To answer your question, the parameters mainly correct for errors in timing/variability. However, there is a groundwater loss parameter that can be calibrated to account for too much runoff (although must be careful how this is done as these types of parameters found in model can be used to "hide" biases "under the carpet"). 

      Correcting for large systematic biases is more challenging through hydrological model parameter calibration alone. In the current operational (i.e. version 2.1) set up of the model, if there is too little water coming from HTESSEL, then the calibration of LISFLOOD parameters cannot "create" water to fix this bias. We have to rely on the data assimilation of HTESSEL that can create/add water according to soil moisture and snow observations. 

      I hope this answers your question,




      Hirpa, F. A., Salamon, P., Beck, H. E., Lorini, V., Alfieri, L., Zsoter, E. and Dadson, S. J.: Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data, J. Hydrol., 566, 595–606, doi:10.1016/j.jhydrol.2018.09.052, 2018.