Summary:
The thesis is devoted to the characterization of the space-time structure of heavy rainfall events in the Cévennes-Vivarais area (France). The region is prone to catastrophic storms whose magnitude governs social and economic consequences at different space and time scales. The magnitude of an event cannot be univocally related to a probability of occurrence. The determination of the occurrence probability of storms is problematic because of their extreme character, of their complex space-time development and of the lack of rainfall data at the spatial and temporal scales of interest. We propose to adopt scale-invariant approaches in order to estimate the heavy rainfall frequency assessment. These approaches allow to extrapolate the high resolution rainfall distribution based on low resolution rainfall intensity data. The model estimation being heavily dependent of the data accuracy, the first step consists in the characterization of the error committed in the point and spatial rainfall estimated by tipping-bucket raingage networks. We then explore the extreme rainfall behavior in the region, detecting the range where extremes are scale-invariant. In this range, we present a regional Intensity-Duration-Frequency model for point rainfall maxima taking into account the heterogeneity of extremes in the region. We demonstrate that the rainfall network does not allow to detect scale-invariant properties of extreme rainfall fields, and then we adopt a semi-empirical method based on the concept of " dynamic scaling " to build regional Intensity-Duration-Area-Frequency curves. Finally, we apply this model for the determination of the severity diagrams for three significant storms in the Cévennes-Vivarais region, with the aim to identify the critical space-time scales of each event. Based on severity diagrams, we then evaluate, for the same events, the performances of a mesoscale meteorological model.
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