Mieux connaître la distribution spatiale des pluies améliore-t-il la modélisation des crues ? Diagnostic sur 181 bassins versants français

Abstract : Hydrologic models are essential tools to compute the catchment rainfall-runoff response required for river management and flood forecast purposes. Precipitation dominates the high frequency hydrological response, and its simulation is thus dependent on the way rainfall is represented. In this context, the sensitivity of runoff hydrographs to the spatial variability of forcing data is a major concern of researchers. However, results from the abundant literature are contrasted and it is still difficult to reach a clear consensus.Weather radar is considered to be helpful for hydrological forecasting since it provides rainfall estimates with high temporal and spatial resolution. However, it has long been shown that quantitative errors inherent to the radar rainfall estimates greatly affect rainfall-runoff simulations. As a result, the benefit from improved spatial resolution of rainfall estimates is often limited for hydrological applications compared to the use of traditional ground networks.Recently, Météo-France developed a rainfall reanalysis over France at the hourly time step over a 10-year period combining radar data and raingauge measurements: weather radar data were corrected and adjusted with both hourly and daily raingauge data. Here we propose a framework to evaluate the improvement in streamflow simulation gained by using this new high resolution product.First, a model able to cope with different spatial resolutions, from lumped to semi-distributed, was developed and validated. Second, the impact of spatial rainfall resolution input on streamflow simulation was investigated. Then, the usefulness of spatial radar data measurements for rainfall estimates was compared with an exclusive use of ground raingauge measurements and evaluated through hydrological modelling in terms of streamflow simulation improvements. Finally, semi-distributed modelling with the TGR model was performed for flood forecasting and compared with the lumped forecasting GRP model currently in use in the French flood forecast services. The originality of our work is that it is based on actual measurements from a large set of 181 French catchments representing a variety of size and climate conditions, which allows to draw reliable conclusions.
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Florent Lobligeois. Mieux connaître la distribution spatiale des pluies améliore-t-il la modélisation des crues ? Diagnostic sur 181 bassins versants français. Sciences de la Terre. AgroParisTech, 2014. Français. ⟨NNT : 2014AGPT0013⟩. ⟨tel-01134990⟩

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