Mapping the Hazard of Extreme Rainfall by Peaks over Threshold Extreme Value Analysis and Spatial Regression Techniques

• Published in: Journal of applied meteorology and climatology Vol. 45; no. 1; pp. 108 - 124
• Main Authors: Beguería, Santiago, Vicente-Serrano, Sergio M.
• Format: Journal Article
• Language: English
• Published: Boston, MA American Meteorological Society 01.01.2006
• Subjects: Air masses; Climate; Climate change; Climate models; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Exact sciences and technology; External geophysics; Extreme weather; Hazards; Interpolation; Mapping; Mathematical independent variables; Meteorology; Natural hazards: prediction, damages, etc; Parameter estimation; Precipitation; Probabilities; Rain; Regression analysis; Series convergence; Spatial analysis; Spatial distribution; Valleys; Water in the atmosphere (humidity, clouds, evaporation, precipitation); Iberian Peninsula; Southern Europe; Spain; Ebro River Valley; Cluster analysis; time series analysis; rainfall; atmospheric precipitation; Europe; regression analysis; Spatial analysis; Long term variation; cartography; interpolation; Pareto distribution; Return period; extreme value; Severe weather; Risk management; Natural hazards
• ISSN: 1558-8424; 1558-8432
• DOI: 10.1175/JAM2324.1

The occurrence of rainfalls of high magnitude constitutes a primary natural hazard in many parts of the world, and the elaboration of maps showing the hazard of extreme rainfalls has great theoretical and practical interest. In this work a procedure based on extreme value analysis and spatial interpolation techniques is described. The result is a probability model in which the distribution parameters vary smoothly in space. This methodology is applied to the middle Ebro Valley (Spain), a climatically complex area with great contrasts because of the relief and exposure to different air masses. The database consists of 43 daily precipitation series from 1950 to 2000. Because rainfall tends to occur highly clustered in time in the area, a declustering process was applied to the data, and the series of daily cluster maxima were used hereinafter. The mean excess plot and error minimizing were used to find an optimum threshold value to retain the highest records (peaks-over-threshold approach), and a Poisson–generalized Pareto model was fitted to the resulting series. The at-site parameter estimates (location, scale, and shape) were regressed upon a set of location and relief variables, enabling the construction of a spatially explicit probability model. The advantages of this method to obtain maps of extreme precipitation hazard are discussed in depth.