Modeling of multisite precipitation occurrences using latent Gaussian-based multivariate binary response time series

JOURNAL OF HYDROLOGY

卷:590

Article

Chen, Hsien-Wei¹ 

[1] McGill Univ, Dept Civil Engn & Appl Mech, 817 Sherbrooke St West, Sherbrooke, PQ H3A 0C3, Canada

关键词: Multivariate time series;    Binary response random variable;    Multisite daily precipitation occurrence;    Spatio-temporal modelling;    Lagged interstation dependence;    Latent Gaussian model;   

DOI  :  10.1016/j.jhydrol.2020.125069

来源: Elsevier

【 摘 要 】

A new stochastic model for daily precipitation occurrence processes observed at multiple locations is developed. The modeling concept is to use the indicator function and the elliptical shape of multivariate Gaussian distribution to represent the joint probabilities of daily precipitation occurrences. By using this concept, the number of parameters needed for precipitation occurrence modeling can be largely reduced when compared to the commonly used two-state Markov chain approach. With this parameter reduction, the modeling of spatio-temporal dependence of daily precipitation occurrence processes observed at different locations is no longer difficult. Results of an illustrative application using the precipitation record available from a network of ten raingauges in the southern Quebec region, also demonstrate the accuracy and the feasibility of the proposed model.

【 授权许可】

Free