Amélioration des performances d'un modèle stochastique de génération de hyétogrammes horaires: application au pourtour méditerranéen français

Abstract

Ce document est protégé par la loi sur le droit d'auteur. L'utilisation des services d'Érudit (y compris la reproduction) est assujettie à sa politique d'utilisation que vous pouvez consulter en ligne. Reçu le 23 janvier 1998, accepté le 13 janvier 1999**. SUMMARYA stochastic mode] for generating hourly hyetographs has been recently developed, in the Cemagref of Aix-en-Provence, to be coupled with a rainfall runoff conversion modelling. Thus, by simulation of very long periods (1000 years for example), we obtain a large number of hourly hyetographs and flood scénarios that are statistically studied and used in flood predetermination problems. The rainfall model studied is based on the theory that rainfall can be linked to a random and intermittent process whose évolution is described by stochastic laws. It is also based on the hypothesis of independence between variables describing hyetographs and on the hypothesis of the stationary nature of the phenomenon studied. Generating a rainfall time séries involves two steps: descriptive study of the phenomenon (nine independent variables are chosen to describe the phenomenon and thèse variables are deflned by a theoretical law of probability fltted to the observations) and création of a rainfall time séries using descriptive variables generated randomly from their law of probability. Initially developed on the Real Collobrier watershed data, the model has been applied to fifty raingauges located on the Mediterranean French seaboard. The extension of the model applying area has shown heterogeneousness in the results. Therefore, modifications hâve been made to the model to improve its performances. Among thèse modifications, three of them hâve presented notable improvements.A study of the sensitivity of the parameters has been made. Parameters of shape variables and of some other variables had only a slight influence on depth of generated rainfalls. But, the law of mean rainfall intensities clearly differentiates the stations. Then, a theoretical probability distribution for the storm intensity variable, less sensitive to the sampling problems, has been Finally, the modelling of storm persistence in a same rainfall épisode has been studied to generate some high 24 hours maximum rainfalls. Persistence modelling is entirely justified by the fact that "ordinary storms" cluster together around the "main storm" (the "main storm" is the greatest storm of an épisode and the "ordinary storms" are the other storms of the épisode). When the study of this phenomenon is extended, it can be observed that there is a certain positive dependency between occurrence probability of the "main storm" and occurrence probability of storms which corne before or after it. Two combined effects occur: within one rainy épisode, the strongest "ordinary storms" are preferentially clustered together around the "main storm", and considering the number of "ordinary storms" throughout ail the épisodes, the strongest storms close to the "main storm" are preferentially associated with the strongest "main ...

Accounting for rainfall systematic spatial variability in flash flood forecasting

“…However, the threshold intensity is overestimated. This result can be explained by an underestimate of the hydrological response by the forced distributed models with uniform rainfall (Arnaud et al, 1999;Tramblay et al, 2010): the spatial and/or temporal information for the precipitation is not negligible. The direct hydrological model (I MARINE ) performs significantly better than the FFG method.…”

“…In contrast, however, other articles highlight the importance of spatial forcing variability in generating flash floods (Arnaud et al, 1999;Le Lay and Saulnier, 2007;Tramblay et al, 2010;Lobligeois, 2014;Morin and Yakir, 2014). Distributed hydrological models show significant improvements after including the local aspect of precipitation (Michaud and Sorooshian, 1994;Zoccatelli et al, 2010); and in particular, an increase in the likelihood criterion (Nash-Sutcliffe) of up to 30% (Zoccatelli et al, 2010).…”

La modélisation stochastique des pluies horaires et leur transformation en débits pour la prédétermination des crues

“…Ce modèle part du principe que la pluie est un processus aléatoire et intermittent fait d'une succession d'états secs et pluvieux, dont l'évolution est décrite par des lois de nature stochastique. La génération du signal temporel de pluie est alors réalisée en deux étapes (ARNAUD et al, 1999). La première étape est l'étude descriptive du phénomène.…”

Storms Generator Method That Preserves Their Historical Statistical Characteristics. Application To Mexico City Basin Daily Rainfall Fields

“…Arnaud et al (1999) consider that the synthetic storm generation allows a new approach to study the asymptotic behavior of the probability distribution for the maximum rainfalls and to define design storms. Mehrotra and Sharma (2006) consider that the generated synthetic storms could be used in the reliability and risk analysis used for design and operation of hydraulic structures.…”

“…Cisneros et al (1998) developed a model that attempts to preserve the spatial structure of historical storms. Arnaud et al (1999) applied a stochastic generation model to data from the French coast, using a two-step scheme for the generation: the first step is a study of the variables that describe the phenomenon and its definition by probability distribution functions fitted to the observations, and the second is the creation of a rainfall time series using values of the descriptive variables generated randomly from their law of probability. When the model was extended to a larger area, it showed some limitations in the results; therefore, they developed an adaptation to the generation model, by fitting an exponential distribution to the values smaller than four times the mean of the variable.…”