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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 6
Hydrol. Earth Syst. Sci., 21, 2881-2903, 2017
https://doi.org/10.5194/hess-21-2881-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 2881-2903, 2017
https://doi.org/10.5194/hess-21-2881-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Jun 2017

Research article | 12 Jun 2017

Global evaluation of runoff from 10 state-of-the-art hydrological models

Hylke E. Beck1,3, Albert I. J. M. van Dijk2, Ad de Roo3, Emanuel Dutra4,5, Gabriel Fink6, Rene Orth7, and Jaap Schellekens8 Hylke E. Beck et al.
  • 1Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
  • 2Fenner School of Environment & Society, Australian National University (ANU), Canberra, Australia
  • 3European Commission, Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra (VA), Italy
  • 4European Centre for Medium-Range Weather Forecasts (ECMWF), Redding, UK
  • 5Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
  • 6Center for Environmental Systems Research (CESR), University of Kassel, Kassel, Germany
  • 7Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
  • 8Inland Water Systems Unit, Deltares, Delft, the Netherlands

Abstract. Observed streamflow data from 966 medium sized catchments (1000–5000km2) 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-accurate models did not appreciably degrade the ensemble performance. Overall, we argue that 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.

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Runoff measurements for 966 catchments around the globe were used to assess the quality of the daily runoff estimates of 10 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.
Runoff measurements for 966 catchments around the globe were used to assess the quality of the...
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