Partitioning into hazard subregions for regional peaks‐over‐threshold modeling of heavy precipitation. Issue 5 (31st May 2017)

• Record Type: Journal Article
• Authors: Carreau, J.; Naveau, P.; Neppel, L.
• Is Part Of: Water resources research. Volume 53:Issue 5(2017)
• Page Start: 4407
• Page End: 4426
• Publication Date: 2017-05-31
• Subjects: generalized Pareto distribution -- probability weighted moment -- spatial interpolation -- regional frequency analysis -- French Mediterranean; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2017WR020758
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: The French Mediterranean is subject to intense precipitation events occurring mostly in autumn. These can potentially cause flash floods, the main natural danger in the area. The distribution of these events follows specific spatial patterns, i.e., some sites are more likely to be affected than others. The peaks‐over‐threshold approach consists in modeling extremes, such as heavy precipitation, by the generalized Pareto (GP) distribution. The shape parameter of the GP controls the probability of extreme events and can be related to the hazard level of a given site. When interpolating across a region, the shape parameter should reproduce the observed spatial patterns of the probability of heavy precipitation. However, the shape parameter estimators have high uncertainty which might hide the underlying spatial variability. As a compromise, we choose to let the shape parameter vary in a moderate fashion. More precisely, we assume that the region of interest can be partitioned into subregions with constant hazard level. We formalize the model as a conditional mixture of GP distributions. We develop a two‐step inference strategy based on probability weighted moments and put forward a cross‐validation procedure to select the number of subregions. A synthetic data study reveals that the inference strategy is consistent and not very sensitive to the selected number of subregions. An application on daily precipitation data from the French Mediterranean shows that the conditional mixture of GPs outperforms two interpolation approaches (with constant or smoothly varying shape parameter). Key Points: Regional peaks‐over‐threshold formalized as a conditional mixture model Inference strategy based on probability weighted moments and nonparametric estimators Selection of the number of subregions with a cross‐validation procedure