Error Structure of Metastatistical and Generalized Extreme Value Distributions for Modeling Extreme Rainfall in Austria

Abstract

Incorrect estimation of extreme values of daily precipitation can have severe consequences in hydrological and engineering applications. Recent advances in the study of extreme precipitation have shown that the Metastatistical Extreme Value Distribution (MEV) is superior to the Generalized Extreme Value Distribution (GEV) whenever the length of the available record is small compared to the average recurrence time. This paper provides a detailed examination of the relative performance of MEV and GEV for both point estimates and spatial modeling. An analysis for a large number of sample years and return periods for daily precipitation in Austria shows that the MEV exceeds the GEV if the number of sample years is smaller, and the estimated return period is larger than 35 years. This advantage disappears almost entirely if the MEV is used for spatially smooth extreme value modeling instead of the GEV. However, the computational effort is drastically reduced in comparison to spatial modeling with the GEV if a simplified version of the MEV is used.

Keywords

• extreme
• rainfall
• generalized extreme value distribution
• metastatistical extreme value distribution
• error structure
• smooth modeling