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  • 1.
    Towner, Jamie
    et al.
    Univ Reading, Dept Geog & Environm Sci, Reading RG6 6AB, Berks, England.
    Cloke, Hannah L.
    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.
    Zsoter, Ervin
    Univ Reading, Dept Geog & Environm Sci, Reading RG6 6AB, Berks, England;European Ctr Medium Range Weather Forecasts, Shinfield Pk, Reading RG6 9AX, Berks, England.
    Flamig, Zachary
    Univ Chicago, Ctr Data Intens Sci, Chicago, IL 60637 USA.
    Hoch, Jannis M.
    Univ Utrecht, Dept Phys Geog, POB 80115, NL-3508 TC Utrecht, Netherlands;POB 177, NL-2600 MH Delft, Netherlands.
    Bazo, Juan
    Red Cross Red Crescent Climate Ctr, NL-2521 CV The Hague, Netherlands;UTP, Lima, Peru.
    de Perez, Erin Coughlan
    Columbia Univ, Int Res Inst Climate & Soc, Palisades, NY 10964 USA;Red Cross Red Crescent Climate Ctr, NL-2521 CV The Hague, Netherlands.
    Stephens, Elisabeth M.
    Univ Reading, Dept Geog & Environm Sci, Reading RG6 6AB, Berks, England.
    Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin2019In: Hydrology and Earth System Sciences, ISSN 1027-5606, E-ISSN 1607-7938, Vol. 23, no 7, p. 3057-3080Article in journal (Refereed)
    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.

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