Spatial-temporal rain field generation for the Guangdong-Hong Kong-Macau Greater Bay Area considering climate change

Abstract

A stochastic rainfall generator is required to provide rainfall inputs for the analysis and mitigation of such hydrological or geologic hazards as floods and rain-induced landslides. This paper presents a new spatial-temporal rainstorm generator for generating simultaneous rainfall processes at numerous locations considering the spatial correlation among these locations and interpolating the point processes into an areal rain field. The generator is able to include the effect of climate change by adjusting the parameters of the marginal distributions of variables constituting rainfall events. A case study on the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), one of the regions that are most prone to storm-related disasters in the world, is presented. The performance of the proposed generator is excellent in reproducing the historical statistical characteristics of regional rainfall. The model is adapted to climate change through extrapolation of the variation trend of the model parameters in the observation period to explore possible future scenarios of regional rainfall in GBA. The simulation results indicate a significant increase in rainfall extremes, especially for short-duration rainfall, at the end of 21st century in GBA.