Spatial Extreme Value Analysis of Extreme Rainfall Using the Extremal-t Process - Husna Mir’atin Nuroini; Sutikno; Purhadi (Department of Statistics ITS, 2025)
Indonesia’s diverse topography, consisting of coasts, lowlands, highlands, and mountains, results in a wide range of weather and climate conditions, enabling various hydrological phenomena such as extreme rainfall, hurricanes, high temperatures, and storms. In recent years, global warming has emerged as a major environmental concern, with one of its significant impacts being climate change. This, in turn, increases the frequency and intensity of extreme hydrological events, potentially causing floods, transportation and communication disruptions, infrastructure damage, agricultural losses, and threats to human life. This study aims to identify the best model and estimate the return levels of extreme rainfall in Ngawi Regency from March 1990 to November 2022 using spatial extreme value analysis with max-stable processes and the extremal-t process. Daily rainfall data from 1990 - 2018 were used for model training, while data from 2018 - 2022 were allocated for model testing to validate predictive performance. Parameter estimation was conducted using Maximum Likelihood Estimation (MLE) and Maximum Pairwise Likelihood Estimation (MPLE), solved through the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton numerical iteration method. The analysis shows that the best trend surface model has average rainfall and variance influenced by latitude, while the distribution shape is unaffected by latitude or longitude, indicating isotropy. Furthermore, the return level prediction demonstrates higher accuracy when applied over a three-year period.