Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios |
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| Authors: | J E Reynolds S Halldin J Seibert CY Xu T Grabs |
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| Institution: | 1. Department of Earth Sciences, Uppsala University , Uppsala, Sweden;2. Centre of Natural Hazards and Disaster Science (CNDS) , Uppsala, Sweden;3. Swedish Meteorological and Hydrological Institute , Norrk?ping, Sweden eduardo.reynolds@smhi.se https://orcid.org/0000-0002-7390-7290;5. Centre for Societal Risk Research (CSR), Karlstad University , Karlstad, Sweden;6. Centre of Natural Hazards and Disaster Science (CNDS) , Uppsala, Sweden;7. Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences , Uppsala, Sweden;8. Department of Geography, University of Zurich , Zurich, Switzerland https://orcid.org/0000-0002-6314-2124;9. Department of Geosciences, University of Oslo , Oslo, Norway;10. Department of Earth Sciences, Uppsala University , Uppsala, Sweden |
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| Abstract: | ABSTRACT Discharge observations and reliable rainfall forecasts are essential for flood prediction but their availability and accuracy are often limited. However, even scarce data may still allow adequate flood forecasts to be made. Here, we explored how far using limited discharge calibration data and uncertain forcing data would affect the performance of a bucket-type hydrological model for simulating floods in a tropical basin. Three events above thresholds with a high and a low frequency of occurrence were used in calibration and 81 rainfall scenarios with different degrees of uncertainty were used as input to assess their effects on flood predictions. Relatively similar model performance was found when using calibrated parameters based on a few events above different thresholds. Flood predictions were sensitive to rainfall errors, but those related to volume had a larger impact. The results of this study indicate that a limited number of events can be useful for predicting floods given uncertain rainfall forecasts. |
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| Keywords: | floods rainfall forecasts rainfall–runoff modelling event-based calibration ungauged basins value of information |
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https://orcid.org/0000-0002-7390-7290;5. Centre for Societal Risk Research (CSR), Karlstad University , Karlstad, Sweden;6. Centre of Natural Hazards and Disaster Science (CNDS) , Uppsala, Sweden;7. Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences , Uppsala, Sweden;8. Department of Geography, University of Zurich , Zurich, Switzerland