Prediction of extreme rainfall events in 21st century - The results based on Bayesian Markov Chain Monte Carlo

The escalation of climate change caused more extreme rainfall, increasing flood damage in Japan since around 2010. Therefore, it is crucial to examine climate change's impact on extreme rainfall events over the past 100 years and assess whether the existing return level standards effectively address the requirements for mitigating urban flood risks in the present scenario. The annual maximum hourly rainfall data from six major cities in Japan are divided into two distinct periods: 1921-1970 and 1971-2020. These datasets were then subjected to fitting with the Generalized Extreme Value distribution using two methods: Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo (MCMC). The results demonstrate that the MCMC method provides more precise and accurate estimates, resulting in a narrower range of uncertainty for the high period of interest. Furthermore, the return levels for the 100-year return period in Nagoya and Fukuoka, based on the data for 1971-2020, are 24.4 mm/h and 13.4 mm/h greater than the corresponding standard values, respectively. This suggests significant changes in extreme rainfall patterns in certain cities in Japan and highlights the need for Nagoya and Fukuoka to update their criteria for return levels based on the latest rainfall data using the MCMC method.