Northrop, Paul J;
(2024)
Stochastic models of rainfall.
Annual Review of Statistics and Its Application
, 11
pp. 1-27.
10.1146/annurev-statistics-040622-023838.
Preview |
Text
StochasticModelsOfRainfall.pdf - Accepted Version Download (1MB) | Preview |
Abstract
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.
| Type: | Article |
|---|---|
| Title: | Stochastic models of rainfall |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1146/annurev-statistics-040622-023838 |
| Publisher version: | https://doi.org/10.1146/annurev-statistics-040622-... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Climate change, flood risk assessment, point process, Poisson cluster process, stochastic-mechanistic model, stochastic rainfall generation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10174912 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
