%T Stochastic models of rainfall
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%P 1-27
%D 2024
%L discovery10174912
%I Annual Reviews Inc.
%J Annual Review of Statistics and Its Application
%V 11
%A Paul J Northrop
%K Climate change, flood risk assessment, point process, Poisson cluster process, stochastic-mechanistic model, stochastic rainfall generation
%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.