Error Structure of Metastatistical and Generalized Extreme Value Distributions for Modeling Extreme Rainfall in Austria
Error Structure of Metastatistical and Generalized Extreme Value Distributions for Modeling Extreme Rainfall in Austria
复制标题
用于模拟奥地利极端降雨的元统计和广义极值分布的误差结构
作者：
H. Schellander;Alexander Lieb;T. Hell
对日降水极值的不正确估计会在水文和工程应用中造成严重后果。近年来极端降水研究的最新进展表明，当可获得的记录长度小于平均重现时间时，亚转移极值分布（Metastatistical extreme Value Distribution， MEV）优于广义极值分布（Generalized extreme Value Distribution， GEV）。本文详细介绍了MEV和GEV在点估计和空间建模方面的相对性能。对奥地利日降水的大量样本年和回归期的分析表明，当样本年数较少时，MEV超过GEV，且估计的回归期大于35 a。如果将MEV用于空间平滑极值建模而不是GEV，则这种优势几乎完全消失。然而，如果使用简化版本的MEV，则与使用GEV进行空间建模相比，计算工作量大大减少。
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.