Model-Based Estimation of Probable Maximum Precipitation (PMP) Under Climate Change and Its Frequency Analysis

The use of numerical weather prediction (NWP) models in estimating extremely low exceedance probability precipitation depths, such as Probable Maximum Precipitation (PMP), has been drawing considerable attention owing to its benefits. However, there are some remaining important issues that need to be addressed in the approach, such as including the effects of climate change or the probabilistic assessment of the estimate. This study tackled such issues by performing the model-based maximization of extreme precipitation, considering the effects of climate change, to ultimately estimate PMP in the target watershed. This study focused on the recently occurred meso-scale convective systems, called "Senjo-Kousuitai" in Japanese, in northern Japan for the precipitation maximization exercise. The Weather Research and Forecasting (WRF) model version 4.1.2 was used to simulate and maximize the target event's precipitation in the Akagawa River Basin and Arakawa River Basin. As a result, the precipitation over the basin was successfully intensified, reaching the extremely low exceedance probability precipitation depth based on the radar-based observation records. This study also succeeded in quantifying the climate change effects on the maximum precipitation depths. The probabilistic assessment of the estimated maximum precipitation was performed based on the atmospheric drivers of precipitation and the large-ensemble climate simulation database (d4PDF). Overall, this study proposed a useful way to address the current important issues in model-based PMP estimation.