Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

Jamal Zaherpour*, Simon N. Gosling, Nick Mount, Hannes Müller Schmied, Ted I.E. Veldkamp, Rutger Dankers, Stephanie Eisner, Dieter Gerten, Lukas Gudmundsson, Ingjerd Haddeland, Naota Hanasaki, Hyungjun Kim, Guoyong Leng, Junguo Liu, Yoshimitsu Masaki, Taikan Oki, Yadu Pokhrel, Yusuke Satoh, Jacob Schewe, Yoshihide Wada

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models' ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model - a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.

Original languageEnglish
Article number065015
Pages (from-to)1-18
Number of pages18
JournalEnvironmental Research Letters
Issue number6
Publication statusPublished - 12 Jun 2018


This work has been conducted under the framework of the Inter-Sectoral Impact Model Intercomparison Project, phase 2a (ISIMIP2a), so our thanks go to the modellers who submitted results to this project. The ISIMIP2a was funded by the German Ministry of Education and Research, with project funding reference number 01LS1201A. Data is available from [51]. We also thank the Global Runoff Data Centre (GRDC) for making available the observed runoff data. JZ was supported by the Islamic Development Bank and a 2018 University of Nottingham Faculty of Social Sciences Research Outputs Award. IH was supported by grant no. 243803/E10 from the Norwegian Research Council. JS was supported within the framework of the Leibniz Competition (SAW-2013 PIK-5) and by the EU FP7 HELIX project (grant no. 603864). JL was supported by the National Natural Science Foundation of China (41625001, 41571022), the Beijing Natural Science Foundation Grant (8151002), and the Southern University of Science and Technology (Grant no. G01296001). GL was supported by the Office of Science of the US Department of Energy as part of the Integrated Assessment Research Program. PNNL is operated by Battelle Memorial Institute for the US DOE under contract DE-AC05-76RLO1830. NH and YM were supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan. HK and TK were supported by Japan Society for the Promotion of Science KAKENHI (16H06291).

FundersFunder number
Environment Research and Technology Development Fund
U.S. Department of EnergyDE-AC05-76RLO1830
Office of Science
Seventh Framework Programme603864
Japan Society for the Promotion of Science London
Japan Society for the Promotion of Science16H06291
National Natural Science Foundation of China41571022, 41625001
Bundesministerium für Bildung und Forschung01LS1201A
Islamic Development Bank243803/E10
Natural Science Foundation of Beijing Municipality8151002
Seventh Framework Programme
Norges forskningsrådSAW-2013 PIK-5
Ministry of the Environment, Government of Japan
Southern University of Science and TechnologyG01296001


    • extreme events
    • global hydrological models
    • human impacts
    • land surface models
    • model evaluation
    • model validation


    Dive into the research topics of 'Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts'. Together they form a unique fingerprint.

    Cite this