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Chapitre D'ouvrage Année : 2009

Can discharge assimilation methods be used to improve flood forecasting when few data are available?

Résumé

Forecasting floods is a major issue for public safety all over the world. Due to the difficulties inherent in the flood forecasting exercise, data assimilation techniques have been developed to cope with model errors. Unfortunately, these techniques require recent (real or near real-time) observations which may not be readily available in regions lacking automatic measurements networks. This paper investigates the impact of data assimilation techniques on discharge forecasts and model performance when few (but not zero) discharge measurements are available for the data assimilation. A parsimonious rainfallrunoff model is applied to a set of 178 French catchments. We explore the time properties of different discharge data assimilation schemes. Life times of the updates and model performance are assessed as a function of the time between the last available discharge observation and the forecast. State updating proves to have an added value to the forecasting system, even when data availability is limited.

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Dates et versions

hal-02593013 , version 1 (15-05-2020)

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Lionel Berthet, Maria-Helena Ramos, Charles Perrin, Vazken Andréassian, Cécile Loumagne. Can discharge assimilation methods be used to improve flood forecasting when few data are available?. IAHS Red Books Publications : News approaches to hydrological prediction in data sparse regions, 333, IAHS, pp.94-100, 2009, 978-1-907161-04-9. ⟨hal-02593013⟩
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