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A Multi-Site Stochastic Weather Generator for Daily Precipitation and Temperature

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. 57(5): 1375-1391. (doi: 10.13031/trans.57.10685) @2014
Authors:   Jie Chen, François P. Brissette, Xunchang J. Zhang
Keywords:   Interstation correlation, Multi-site weather generator, Precipitation, Temperature.

Stochastic weather generators are used to generate time series of climate variables that have statistical properties similar to those of observed data. Most stochastic weather generators work for a single site and can only generate climate data at a single point or independent time series at several points. However, for hydrological impact studies, spatially coherent climate information is usually required at several locations over a watershed. This climate information can be generated using a multi-site weather generator. This article presents a new Matlab-based stochastic weather generator (MulGETS) for generating multi-site precipitation and temperature. MulGETS is an extension of a single-site weather generator that makes it possible to drive individual single-site models with temporally independent but spatially correlated random numbers. Similar to an unmodified single-site weather generator, precipitation occurrence is generated using a first-order two-state Markov chain, and temperature is generated using a first-order linear autoregressive model. However, instead of generating daily precipitation amounts based on a single gamma distribution, MulGETS uses a multi-gamma distribution to address the spatial correlation of precipitation amounts. The performance of MulGETS is evaluated with respect to its ability to produce the spatial correlation and statistical characteristics of daily precipitation and temperature for five watersheds selected from different climate conditions. The five watersheds also differ in watershed size and number of stations. The results show that MulGETS accurately preserves the spatial correlation of precipitation occurrence and amounts as well as the maximum and minimum temperatures for all watersheds. The joint probabilities of precipitation occurrence are also reasonably well reproduced. Additionally, MulGETS is capable of reproducing the mean and standard deviation of daily precipitation amounts for individual sites, as well as the watershed-averaged precipitation. Overall, MulGETS is an effective model for generating multi-site precipitation and temperature. It can easily be used as a downscaling tool for climate change impact studies by modifying its parameters based on climate model outputs. The entire set of Matlab routines utilized is available on the Mathworks file exchange site.

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