Bartlett-Lewis based stochastic rainfall model: An improvement to effectively reproduce sub-hourly rainfall extremes

Despite significant advancements in the Poisson cluster-based Bartlett-Lewis model for effectively reproducing rainfall extremes, there is still room for further refinement. This study proposes a refined model, referred to as RBL7, introducing module k with a modified equation for rainfall disaggregation. This adjustment allows the power of the sine function to vary inversely with rain cell duration, thereby capturing the realistic characteristics of rainfall extremes, which often come with high intensity over short durations. Furthermore, an improved calibration approach is also proposed for the first module of the RBL7 model. This involves a hybrid optimization technique combining Particle Swarm Optimization (PSO) and fmincon methods, iterately executed until the objective function reaches the pre-assigned threshold. While the calibration of the RBL7 model relies solely on observed rainfall aggregated at hourly and longer timescales, it effectively reproduces rainfall extremes from uncalibrated sub-hourly to supra-hourly aggregation intervals, outperforming existing models using sine-2 and rectangular pulse shapes. Additionally, this refined model maintains its capability to capture rainfall standard statistics, i.e., mean, variance, covariance, skewness, and proportion of wet period, at various timescales ranging from 5 minutes to a month. These findings highlight the robustness of the RBL7 model in simulating rainfall characteristics, especially extreme values at sub-hourly aggregation intervals.

 

Acknowledgement

This study was supported by Korea Environment Industry & Technology Institute (KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis Program (or Project), funded by Korea Ministry of Environment(MOE)(RS-2023-00218873).