A programming tool to generate multi-site daily rainfall using a two-stage semi parametric model. (January 2015)

• Record Type: Journal Article
• Authors: Mehrotra, Rajeshwar; Li, Jingwan; Westra, Seth; Sharma, Ashish
• Is Part Of: Environmental modelling & software. Volume 63(2015:Jan.)
• Page Start: 230
• Page End: 239
• Publication Date: 2015-01
• Subjects: Markov model -- Multi-site rainfall -- Weather generator -- Spatial correlations -- Kernel density estimation -- Nonparametric -- Low-frequency variability -- Longer-term aggregated variables -- MRS; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.10.016
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Many problems in hydrology and agricultural science require extensive records of rainfall from multiple locations. Temporal and/or spatial coverage of rainfall data is often limited, so that stochastic models may be required to generate long synthetic rainfall records. This study describes a multi-site rainfall simulator (MRS) to stochastically generate daily rainfall at multiple locations. The MRS is available as an open-source software package in the R statistical computing environment. The software includes statistical analysis and graphics functions, and can display statistics and graphs at multiple time scales, including from individual sites and areal averages. The MRS thus provides a detailed set of modelling functions to simulate and analyse daily rainfall. The capabilities of the package are demonstrated using 30 gauges located in Sydney, Australia, and the results show that the model preserves observed year-to-year variability, interannual persistence and various daily distributional and space–time dependence attributes. Highlights: A multi-site rainfall simulator to stochastically generate daily rainfall at multiple locations. Available as an open-source software package. Includes statistical analysis and graphics functions at multiple time scales. Preserves observed year-to-year variability and interannual persistence. Preserves various daily distributional and space–time dependence attributes.