Articles | Volume 23, issue 7
https://doi.org/10.5194/hess-23-3057-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-3057-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jul 2019
Research article |
| 18 Jul 2019
| 18 Jul 2019
Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin
Department of Geography & Environmental Science, University of
Reading, Reading, RG6 6AB, UK
Hannah L. Cloke
Department of Geography & Environmental Science, University of
Reading, Reading, RG6 6AB, UK
Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
Department of Earth Sciences, Uppsala University, Uppsala, 752 36,
Sweden
Centre of Natural Hazards and Disaster Science, CNDS, Uppsala, 752 36, Sweden
Ervin Zsoter
European Centre for Medium-Range Weather Forecasts, Shinfield Park,
Reading, RG6 9AX, UK
Department of Geography & Environmental Science, University of
Reading, Reading, RG6 6AB, UK
Zachary Flamig
University of Chicago Center for Data Intensive Science, Chicago, USA
Jannis M. Hoch
Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht, the Netherlands
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
Juan Bazo
Red Cross Red Crescent Climate Centre, 2521 CV The Hague, the
Netherlands
Universidad Tecnológica del Perú (UTP), Lima, Peru
Erin Coughlan de Perez
International Research Institute for Climate and Society, Columbia
University, Palisades, NY 10964, USA
Red Cross Red Crescent Climate Centre, 2521 CV The Hague, the
Netherlands
Elisabeth M. Stephens
Department of Geography & Environmental Science, University of
Reading, Reading, RG6 6AB, UK
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2 citations as recorded by crossref.
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Latest update: 21 Nov 2024
Short summary
This study presents an intercomparison analysis of eight global hydrological models (GHMs), assessing their ability to simulate peak river flows in the Amazon basin. Results indicate that the meteorological input is the most influential component of the hydrological modelling chain, with the recent ERA-5 reanalysis dataset significantly improving the ability to simulate flood peaks in the Peruvian Amazon. In contrast, calibration of the Lisflood routing model was found to have no impact.
This study presents an intercomparison analysis of eight global hydrological models (GHMs),...
