Open AccessSimulation of Spatial Dependence in Daily Precipitation Using a Mixture of Generalized Chain-Dependent Processes at Multisites
Author(s) -
Xunhua Zheng,
C. Thompson
Publication year - 2011
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/2010jhm1269.1
Subject(s) - precipitation , environmental science , spatial dependence , overdispersion , spatial correlation , variance (accounting) , autoregressive model , variable (mathematics) , stochastic modelling , computer science , statistics , atmospheric sciences , meteorology , climatology , mathematics , geology , mathematical analysis , physics , accounting , count data , business , poisson distribution
Recently, a single-site stochastic precipitation model called “the mixture of generalized chain-dependent processes conditioned on a climate variable” was developed. The model can effectively eliminate overdispersion—that is, underestimation in variance of seasonal precipitation total. In this paper, the single-site model is further developed into a multisite stochastic precipitation model by driving a collection of individual single-site models, but with spatial dependence following a method proposed by D. S. Wilks. Specifically, a computationally effective algorithm for estimating the spatial dependence of precipitation occurrence is developed to replace the construction of the empirical curves in the Wilks method. An effective and straightforward approach for correcting the bias of the spatial correlation of precipitation intensity is also proposed. This model is tested on a small network of sites from a significant hydroelectric power generation region of South Island, New Zealand.