Statistical Methods for Understanding Hydrologic Change

Rainfall is considered a highly valuable climatological resources and is a fundamental component of any water resource assessment strategy. Rainfall characterization has important repercussions, among others, on climate change scenarios, flood risk mitigation, water resources availability and drought assessment. We use a hierarchical modeling approach to investigate a collection of spatially referenced time series of extreme values. We implement a geoadditive mixed model for extremes with a temporal random effect, where we assume that the observations follow generalized extreme value (GEV) distributions whose locations are spatially dependent where the dependence is captured using the geoadditive model. The analyzed territory is the catchment area of Arno River in Tuscany in Central Italy. The dataset is composed by the time series of annual maxima of daily rainfall recorded in about 400 rain gauges, spatially distributed over an area of about 8.830 km2. The record period covers mainly the second half of 20th century.