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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #303118

Research Project: ADAPTING SOIL AND WATER CONSERVATION TO MEET THE CHALLENGES OF A CHANGING CLIMATE

Location: Agroclimate and Natural Resources Research

Title: A multi-site stochastic weather generator of daily precipitation and temperature

Author
item Chen, Jie - University Of Quebec
item Brissette, Francois - University Of Quebec
item Zhang, Xunchang

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/15/2014
Publication Date: 10/15/2014
Citation: Chen, J., Brissette, F.P., Zhang, X.J. 2014. A multi-site stochastic weather generator of daily precipitation and temperature. Transactions of the ASABE. 57(5):1375-1391.

Interpretive Summary: Most stochastic weather generators work for a single site, and can only generate synthetic climate data at a single point. 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 paper presents a new 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 single-site generators with spatially correlated random numbers. 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, and the maximum and minimum temperatures for all watersheds. The spatial coherence of precipitation occurrence is also reasonably well represented. 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. This computer program will be useful to hydrologists and soil erosion modelers for simulating surface hydrology at a watershed scale.

Technical Abstract: 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 paper 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 un-modified single-site weather generator, precipitation occurrence is generated using a first-order two-state Markov chain, and temperature is generated using a conditional scheme. 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, and 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.