SERGE: A spatially-explicit generator of local rainfall in southern Africa

Abstract

This article describes the features of SERGE, a stochastic, spatially explicit tool for the simulation of daily rainfall in arid environments. Rainfall data (either raw or produced by a rainfall generator) are frequently available on a daily basis but at low spatial resolution. Furthermore, although the rainfall characteristics of a given small area may vary little when averaged over the long term, rainfall does vary substantially on a daily time scale. It is exactly this short-term, small-scale variation that is of interest to modellers in many applications. A tool is needed therefore that generates spatio-temporal rainfall estimates based on only temporal data. To fill this gap, we developed SERGE using an ad hoc approach. Based on known characteristics of rainfall at a point, SERGE projects spatially homogeneous daily rainfall produced by a rainfall generator into spatially heterogeneous estimates by distributing clouds of fixed size and random position. Our algorithm preserves the long-term rainfall characteristics at each point, but introduces spatial autocorrelation of variable length. SERGE provides a simple and flexible tool for the simulation of spatio-temporal rainfall to be integrated into other models. SERGE is intended for modellers wanting to investigate the effect of spatially variable rainfall in their system. Given the importance of spatial variability in arid environments, this should be of interest to scientists in the fields of ecology, range management, agriculture, climate change, and hydrology.