Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-124
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
20 May 2016
Review status
This discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
Global evaluation of runoff from ten state-of-the-art hydrological models
Hylke E. Beck1, Albert I. J. M. van Dijk2, Ad de Roo1, Emanuel Dutra3, Gabriel Fink4, Rene Orth5, and Jaap Schellekens6 1European Commission, Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra (VA), Italy
2Fenner School of Environment & Society, Australian National University (ANU), Canberra, Australia
3European Centre for Medium-Range Weather Forecasts (ECMWF), Redding, UK
4Center for Environmental Systems Research (CESR), University of Kassel, Kassel, Germany
5Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
6Inland Water Systems Unit, Deltares, Delft, The Netherlands
Abstract. Observed runoff data from 966 medium sized catchments (1000 to 5000 km2) around the globe were used to comprehensively evaluate the daily runoff estimates (1979–2012) of six global hydrological models (GHMs) and four land surface models (LSMs) produced as part of Tier-1 of the eartH2Observe project. The models were all driven by the WATCH Forcing Data ERA-Interim (WFDEI) meteorological dataset, but used different datasets for non-meteorologic inputs and were run at various spatial and temporal resolutions, although all data were re-sampled to a common 0.5° spatial and daily temporal resolution. For the evaluation, we used a broad range of performance metrics related to important aspects of the hydrograph. We found pronounced inter-model performance differences, underscoring the importance of hydrological model uncertainty in addition to climate input uncertainty, for example in studies assessing the hydrological impacts of climate change. The (uncalibrated) GHMs were found to perform, on average, better than the (uncalibrated) LSMs in snow-dominated regions, while the ensemble mean was found to perform only slightly worse than the best (calibrated) model. The inclusion of less reliable models did not appreciably degrade the ensemble performance. Overall, more effort should be devoted on calibrating and regionalizing the parameters of macro-scale models. We further found that, despite adjustments using gauge observations, the WFDEI precipitation data still contain substantial biases that propagate into the simulated runoff. The early bias in the spring snowmelt peak exhibited by most models is probably primarily due to the widespread precipitation underestimation at high northern latitudes.

Citation: Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from ten state-of-the-art hydrological models, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-124, in review, 2016.
Hylke E. Beck et al.
Hylke E. Beck et al.
Hylke E. Beck et al.

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Short summary
Runoff measurements for 966 catchments around the globe were used to assess the quality of the daily runoff estimates of ten hydrological models run as part of Tier-1 of the eartH2Observe project. We found pronounced inter-model performance differences, underscoring the importance of hydrological model uncertainty in addition to climate input uncertainty, for example in studies assessing the hydrological impacts of climate change.
Runoff measurements for 966 catchments around the globe were used to assess the quality of the...
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