An ANN-Derived Model for Estimating Hourly Storm Patterns with Daily Precipitation Based on Climate Change-Induced Rainstorms

In this study, we aim to propose a framework for developing a smart model for estimating hourly based storm patterns subject to daily storm patterns, titled the SM_ESP_HRDY model; here, a storm pattern comprises a set of cumulative dimensionless rainfalls. A number of AR5 climate-induced rainstorms (1979–2099) are simulated at grid resolution within the study area (Miaoli City, Northern Taiwan) to develop and demonstrate the model. The results obtained via model development reveal that the AR5-simulated events could be separated into 1-day, 2-day, and 3-day rainy events, which could be classified into three types. Thus, within the proposed SM_ESP_HRDY model, the corresponding ANN-derived relationship between hourly and daily cumulative rainfall is established for the three storm patterns across three rainy events. Accordingly, the results for the demonstration of the model indicate that the estimated hourly storm patterns match the validations well, with low bias, especially for the 2-day and 3-day rainy events, with finer temporal rainfall resolution. As a result, the proposed SM_ESP_HRDY model can provide accurate and reliable hourly storm pattern estimates based on the daily rainfall series.