Published July 18, 2019
| Version v1
Journal article
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Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin
Creators
- 1. University of Reading
- 2. University of Chicago
- 3. Utrecht University
- 4. Red Cross Red Crescent Climate Centre
- 5. Columbia University
Description
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) groundwater 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.
Data availability
All of the data and models used in this study were obtained from collaborators of the Global Flood Partnership (GFP) and are freely available. Access to these sources is mentioned in Sect. 2.Files
hess-23-3057-2019.pdf
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Additional details
Identifiers
- DOI
- 10.5194/hess-23-3057-2019
- Other
- oai:uchicago.tind.io:13683
Funding
- Natural Environment Research Council
- NE/L002566/1