The Uncertainty of Extreme Precipitation Events in West Java Region, Indonesia
Yan Firdaus Permadhi (a,d), Catherine Bradshaw (b,c), Ari Kurniadi (d), Hussein Rappel (a)

a) Department of Engineering, Faculty of Environment, Science and Economy, University of Exeter, United Kingdom
b) The Global Systems Institute, University of Exeter, North Park Road, Exeter, EX4 4QE, United Kingdom
c) Met Office Hadley Centre, Fitzroy Road, Exeter EX1 3PB, United Kingdom
d) Badan Meteorologi Klimatologi dan Geofisika (BMKG), Indonesia

Abstract

Climate change is increasing the frequency and intensity of extreme weather events worldwide, with West Java, Indonesia, particularly vulnerable to intense precipitation. These events contribute significantly to hydrometeorological disasters, such as floods and landslides, making understanding their characteristics and associated uncertainties essential. This study quantifies the uncertainty of extreme precipitation events in West Java using Bayesian Inference approach combined with Markov Chain Monte Carlo (MCMC) methods for estimating parameters of the Generalized Extreme Value (GEV) distributions. We analyze daily CHIRPS data, concentrating on the December to February (DJF) period, when historically West Java experiences the highest frequency of extreme rainfall events and related disasters. Our results reveal significant uncertainty in estimating extreme precipitation across diverse topographies, including urban, mountainous, and coastal areas represented by Bogor, Bandung, and Jatiwangi. The spatial patterns of the derived GEV parameters closely align with observed precipitation patterns, providing a clearer understanding of extreme rainfall dynamics in the region. To enhance the robustness of future research, we recommend integrating high-resolution regional climate models with the statistical methods used in this study. Extending the temporal scope beyond the DJF period could offer a more comprehensive view of extreme event variability. Further investigation of the primary drivers of uncertainty, along with long-term climate projections, will be crucial in assessing the subsequent impacts of climate change on extreme precipitation events in West Java.

Keywords: Extreme precipitation, Uncertainty, GEV distribution, Bayesian inference, MCMC

Topic: Interdisciplinary Earth Science and Technology