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Rainfall is an important driver of many Earth surface and subsurface processes such as floods, groundwater recharge, or plants growth. Models are used to investigate the physical response of different environmental aspects to a range of possible rainfall events. To provide meaningful outputs, such models require realistic inputs. However, a major challenge in these models is the representation of the chaotic behavior of rainfall as well as its high temporal and spatial variability. The primary sources of information about rainfall are rainfall measurements, numerical weather models and climate models. Because these sources of information are incomplete and uncertain, stochastic models have been developed to augment available data using statistical methods. Applications to rainfall modeling include interpolation of rainfall measurements, downscaling of numerical model outputs, or weather generators. In this study, we focus on geostatistical stochastic rainfall generation models, which aim at characterizing and reproducing the spatial structure of rainfall. To this end, the different steps of the geostatistical rainfall modeling are reviewed. The spatial structure of the rain is first characterized by variogram analysis of rainfall data. Then, geostatistical models are discussed that match the observed rain structure. Finally, geostatistical simulations are applied to the inferred models for the generation of synthetic rain fields. WIREs Water 2017, 4:e1199. doi: 10.1002/wat2.1199 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods
Benoît et al. (Wed,) studied this question.