Stochastic Rainfall Modeling at Sub-kilometer Scale
2018
Benoit, Lionel | Allard, Denis | Mariethoz, Gregoire | Université de Lausanne = University of Lausanne (UNIL) | Biostatistique et Processus Spatiaux (BioSP) ; Institut National de la Recherche Agronomique (INRA)
International audience
Показать больше [+] Меньше [-]Английский. New measurement devices allow observing rainfall with unprecedented resolution. Such observations often reveal new features of rainfall occurring at the local scale (areas of about 1-25 km(2)). In particular, the joint effects of the advection of rain storms over the ground, and the deformation of spatial rain patterns along time, generate a complex rain field dependence structure characterized by strong space-time interactions. When a high-resolution is desired, stochastic rainfall models must therefore be upgraded to account for these new features of rain fields. In this paper, we propose to improve the meta-Gaussian framework, which is typically used to model space-time rain fields, to the specific case of sub-kilometer rainfall. Particular attention is paid to the reproduction of the main features of local scale rainfall, namely: (1) a skewed distribution of rain intensities with the presence of intraevent intermittency and (2) a space-time dependency structure with strong and complex space-time interactions. The resulting model, able to generate high-resolution, continuous and space-time rain fields at the local scale, is validated and applied to a real data set collected by a network of drop-counting rain gauges recording rainfall at a 1 min frequency. The combination of these data with the proposed model results in a complete framework that allows resolving the features of high-resolution rainfall (1 min temporal resolution, 100 m spatial resolution) over a small alpine catchment in Switzerland.
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Эту запись предоставил Institut national de la recherche agronomique
