An Effective Trend Surface Fitting Framework for Spatial Analysis of Extreme Events

Abstract

The estimation of exceedance probabilities for extreme climatic events is critical for infrastructure design and risk assessment. Climatic events occur over a greater space than they are measured with point-scale in situ gauges. In extreme value theory, the block maxima approach for spatial analysis of extremes depends on properly modeling the spatially varying Generalized Extreme Value marginal parameters (i.e., trend surfaces). Fitting these trend surfaces can be challenging since there are numerous spatial and temporal covariates that are potentially relevant for any given event type and region. Traditionally, covariate selection is based on assumptions regarding the topmost relevant drivers of the event. This work demonstrates the benefit of utilizing elastic-net regression to support automatic selection from a relatively large set of physically relevant covariates during trend surface estimation. The trend surfaces presented are based on 24-hr annual maximum precipitation for northeastern Colorado and the Texas-Louisiana Gulf Coast.