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G-RUN ENSEMBLE: A Multi-Forcing Observation‐Based Global Runoff Reanalysis

Ghiggi, G. and Humphrey, V. and Seneviratne, S. I. and Gudmundsson, L. (2021) G-RUN ENSEMBLE: A Multi-Forcing Observation‐Based Global Runoff Reanalysis. Water Resources Research, 57 (5). Art. No. e2020WR028787. ISSN 0043-1397. doi:10.1029/2020WR028787.

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River discharge is an Essential Climate Variable (ECV) and is one of the best monitored components of the terrestrial water cycle. Nonetheless, gauging stations are distributed unevenly around the world, leaving many white spaces on global freshwater resources maps. Here, we use a machine learning algorithm and historical weather data to upscale sparse in situ river discharge measurements. We provide a global reanalysis of monthly runoff rates for periods covering decades to the past century at a resolution of 0.5° (about 55 km), and with up to 525 ensemble members based on 21 different atmospheric forcing data sets. This global runoff reconstruction, named Global RUNoff ENSEMBLE (G‐RUN ENSEMBLE), is evaluated using independent observations from large river basins and benchmarked against other publicly available runoff data sets over the period 1981–2010. The accuracy of the data set is evaluated on observed river flow from basins not used for model calibration and is found to compare favorably against state‐of‐the‐art global hydrological model simulations. The G‐RUN ENSEMBLE estimates the global mean runoff volume to range between 3.2 × 10⁴ and 3.8 × 10⁴ km³ yr⁻¹. This publicly available data set ( has a wide range of applications, including regional water resources assessments, climate change attribution studies, hydro‐climatic process studies as well as the evaluation, calibration and refinement of global hydrological models.

Item Type:Article
Related URLs:
URLURL TypeDescription ItemData
Ghiggi, G.0000-0002-0818-0865
Humphrey, V.0000-0002-2541-6382
Seneviratne, S. I.0000-0001-9528-2917
Gudmundsson, L.0000-0003-3539-8621
Additional Information:© 2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Issue Online: 06 May 2021; Version of Record online: 06 May 2021; Accepted manuscript online: 29 April 2021; Manuscript accepted: 23 April 2021; Manuscript received: 09 September 2020. L. Gudmundsson and S. I. Seneviratne acknowledge partial support from the European Union's Horizon 2020 Research and Innovation Program (grant agreement 821003 (4C)). V. Humphrey was supported by a Postdoc Mobility fellowship of the Swiss National Science Foundation (P400P2_180784). The authors thank Martin Hirschi and Richard Wartenburger for the help in downloading and preprocessing some of the data sets used in this study. The authors also acknowledge Hylke Beck for kindly providing the MSWEPv2.2 precipitation data set. The authors declare no conflicts of interest relevant to this study. Data Availability Statement: The G‐RUN ENSEMBLE reconstructions and their associated realizations are publicly available at under Creative Commons Attribution 4.0 International License (CC_BY_4.0). Preliminary data repository available at:, DOI has been reserved but will be activated once the manuscript will be published. Full data (>400 GB) will be uploaded before final publication when it will be possible to add the full manuscript reference and DOI to the netCDF‐4 attributes.
Funding AgencyGrant Number
European Research Council (ERC)821003
Swiss National Science Foundation (SNSF)P400P2_180784
Subject Keywords:data‐driven; freshwater; hydrology; reconstruction; runoff; water cycle
Issue or Number:5
Record Number:CaltechAUTHORS:20210507-132208120
Persistent URL:
Official Citation:Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2021). G‐RUN ENSEMBLE: A multi‐forcing observation‐based global runoff reanalysis. Water Resources Research, 57, e2020WR028787.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109011
Deposited By: Tony Diaz
Deposited On:07 May 2021 20:34
Last Modified:12 May 2021 14:49

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