%0 Journal Article
%@ 2326-8298
%A Northrop, Paul J
%D 2024
%F discovery:10174912
%I Annual Reviews Inc.
%J Annual Review of Statistics and Its Application
%K Climate change, flood risk assessment, point process, Poisson cluster process, stochastic-mechanistic model, stochastic rainfall generation
%P 1-27
%T Stochastic models of rainfall
%U https://discovery.ucl.ac.uk/id/eprint/10174912/
%V 11
%X Rainfall is the main input to most hydrological systems. To assess flood risk  for a catchment area, hydrologists use models that require long series of subdaily, perhaps even subhourly, rainfall data, ideally from locations that cover  the area. If historical data are not sufficient for this purpose, an alternative  is to simulate synthetic data from a suitably calibrated model. We review  stochastic models that have a mechanistic structure, intended to mimic physical features of the rainfall processes, and are constructed using stationary  point processes.We describe models for temporal and spatial-temporal rainfall and consider how they can be fitted to data. We provide an example  application using a temporal model and an illustration of data simulated from  a spatial-temporal model.We discuss how these models can contribute to the  simulation of future rainfall that reflects our changing climate.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.