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Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin
Univ Reading, Dept Geog & Environm Sci, Reading RG6 6AB, Berks, England.
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. Univ Reading, Dept Geog & Environm Sci, Reading RG6 6AB, Berks, England;Univ Reading, Dept Meteorol, Reading RG6 6BB, Berks, England;CNDS, Ctr Nat Hazards & Disaster Sci, S-75236 Uppsala, Sweden.ORCID iD: 0000-0002-1472-868x
Univ Reading, Dept Geog & Environm Sci, Reading RG6 6AB, Berks, England;European Ctr Medium Range Weather Forecasts, Shinfield Pk, Reading RG6 9AX, Berks, England.
Univ Chicago, Ctr Data Intens Sci, Chicago, IL 60637 USA.
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2019 (English)In: Hydrology and Earth System Sciences, ISSN 1027-5606, E-ISSN 1607-7938, Vol. 23, no 7, p. 3057-3080Article in journal (Refereed) Published
Abstract [en]

Extreme flooding impacts millions of people that live within the Amazon floodplain. Global hydrological models (GHMs) are frequently used to assess and inform the management of flood risk, but knowledge on the skill of available models is required to inform their use and development. This paper presents an intercomparison of eight different GHMs freely available from collaborators of the Global Flood Partnership (GFP) for simulating floods in the Amazon basin. To gain insight into the strengths and shortcomings of each model, we assess their ability to reproduce daily and annual peak river flows against gauged observations at 75 hydrological stations over a 19-year period (1997-2015). As well as highlighting regional variability in the accuracy of simulated streamflow, these results indicate that (a) the meteorological input is the dominant control on the accuracy of both daily and annual maximum river flows, and (b) ground-water and routing calibration of Lisflood based on daily river flows has no impact on the ability to simulate flood peaks for the chosen river basin. These findings have important relevance for applications of large-scale hydrological models, including analysis of the impact of climate variability, assessment of the influence of long-term changes such as land-use and anthropogenic climate change, the assessment of flood likelihood, and for flood forecasting systems.

Place, publisher, year, edition, pages
COPERNICUS GESELLSCHAFT MBH , 2019. Vol. 23, no 7, p. 3057-3080
National Category
Oceanography, Hydrology and Water Resources
Identifiers
URN: urn:nbn:se:uu:diva-390775DOI: 10.5194/hess-23-3057-2019ISI: 000475871700001OAI: oai:DiVA.org:uu-390775DiVA, id: diva2:1343492
Available from: 2019-08-16 Created: 2019-08-16 Last updated: 2019-08-16Bibliographically approved

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