Spatialization of the annual maximum daily rainfall in Southeastern Brazil

Abstract

Extreme rainfall can lead to heavy damage and losses, such as landslides, floods and agricultural productivity as well as the loss of human and animal lives. To mitigate these losses, water resources management policies are needed, among other goals, to study and predict the frequency of such events in a given region to minimize their harmful effects. The present study investigated the Generalized Extreme Value (GEV) probability distribution applied to the annual maximum daily precipitation data from rainfall stations in the southeastern Brazil. A total of 1,921 rainfall stations were considered, among which the stations with at least 15 years of uninterrupted observations were selected. Subsequently, the stationarity and adherence were tested. GEV probability distribution parameters were then estimated. The results enabled satisfactory spatial interpolation by ordinary kriging and the generation of maps of the distribution parameters. The semivariogram model with the best fit to the three GEV distribution parameters was the exponential model.