Investigation of rainfall disaggregation with flexible timescales based on point process models

Abstract

Analyzing high-temporal-resolution rainfall data with disaggregation tools is vital for understanding precipitation variability, especially in climate change studies with limited access to such data. To facilitate such study for tropical region, we proposed a Point-Process Modelling and Rainfall Disaggregation (PPMRD) framework for rainfall disaggregation with flexible time scales. The framework was tested on two tropical sites in Singapore, using six point-process models and four disaggregation schemes (24 h to 5 min, 3 h to 5 min, 24 h to 1 h, and 6 h to 1 h). Most models excelled fundamental statistics and extreme value analysis. The Bartlett-Lewis Rectangular Pulse Seven parameter (BLRP7) model was found to be the best performer for simulating and disaggregating tropical rainfall. The study findings emphasized the utility of disaggregation models in generating fine-resolution time series for analyzing rainfall extremes. However, it is crucial to underscore the importance of employing ensembles to comprehensively address uncertainties arising from the stochastic nature of rainfall. The study also emphasized the importance of model selection in the proposed framework, as the disaggregation method depends on the generator’s performance. The PPMRD surpasses traditional methods by handling subdaily upper-level series disaggregation to minute-level rainfall. This advancement offers greater capabilities and flexibility, promising benefits for generating high-temporal-resolution rainfall data in hydrological modeling and climate change impact studies.