Chida, Tapiwa Edward, Ramesh, Nadarajah ORCID: https://orcid.org/0000-0001-6373-2557, Onof, Christian and Yip, Iris (2025) Spatial and temporal dependence framework in multi-site precipitation modelling. Stochastic Environmental Research and Risk Assessment. ISSN 1436-3240 (Print), 1436-3259 (Online) (doi:10.1007/s00477-025-03037-6)

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Abstract

Multi-site stochastic models consist of a rich class of models that can be utilised to analyse environmental data and provide a range of possible inputs to hydrological models to quantify uncertainty and assess risk in environmental systems. We develop a class of multi-site hidden Markov models that incorporate a copula to capture the characteristics of the daily precipitation process across a network of stations. The construction of the likelihood function of the proposed multi-site precipitation models is described. A copula with appropriate dependence structure is selected from the family of Archimedean copulas. The maximum likelihood method is used to estimate the parameters of the models. The proposed class of models is used to analyse twelve years of daily rainfall data from four weather stations in London, England. The copula-embedded multi-site models captured the properties of the daily rainfall well and reproduced the correlation structure of the daily precipitation better than the other hidden Markov models.

Item Type:	Article
Uncontrolled Keywords:	Markov process, multivariate analysis, stochastic modelling, stochastic analysis, stochastic modelling in statistics, stochastic processes, Copula-embedded hidden Markov model, Covariate, dependence, multivariate precipitation modelling
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Last Modified:	04 Jul 2025 08:38
URI:	https://gala.gre.ac.uk/id/eprint/50786

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