Evaluation of multiple stochastic rainfall generators in diverse climatic regions
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Long term synthetic precipitation data are useful for water resources planning and management. Commonly stochastic weather generator (SWG) models are useful to produce synthetic time series of unlimited length of weather data based on the statistical characteristics of observed weather at a given location. However, it is difficult to find a single model which works best for all weather (climate) patterns. The objective of this study is to evaluate five different SWG models namely CLIGEN, ClimGen, LARS-WG, RainSim and WeatherMan to generate precipitation at three diverse climatic regions: a Mediterranean climate of western USA, temperate climate of eastern Australia and tropical monsoon region in northern Vietnam. The performance of SWG models to generate precipitation characteristics (i.e., precipitation occurrence; wet and dry spell; and precipitation intensity on wet days) varies between three selected climatic regimes. It was observed that the second order Markov chain (ClimGen and WeatherMan) performed well for all three selected regions in generating precipitation occurrence statistics. All models are able to simulate the ratio of wet/dry spell lengths with respect to observed precipitation. The RainSim performed well in reproducing wet/dry spell lengths in comparison to other models for wetter regions in Australia and Vietnam. ClimGen and WeatherMan are the two best models in simulating precipitation in the western USA, followed by CLIGEN and LARS. Similarly, ClimGen and WMAN are the two best models for synthetic precipitation generation for eastern Australian and northern Vietnam stations, but CLIGEN performs poorly over these regions. All SWG model performed differently with respect to climatic regimes, therefore careful validation is required depending on the weather pattern as well as its application in different water resources sectors. Although our findings are preliminary in nature, however, in order to generalize the performance of SWG’s in a given climate type, it is recommended that more number of stations needs to be evaluated in future studies.
KeywordsStochastic weather generator CLIGEN ClimGen LARS-WG RainSim WeatherMan
We appreciate the suggestions provided by associate editor and reviewers that helped us to improve quality of our manuscript. Authors would also like to thank Risk Engineering and Systems Analytics Center, Clemson University and American International Group for providing financial support.
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Conflict of interest
All authors declare that they have no conflict of interest.
- Campbell GS (1990) CLIMGEN, a program that generates weather data (precipitation, maximum and minimum temperatures). Biological systems engineering department, Washington State University, Pullman, Washington, USAGoogle Scholar
- Neyman J, Scott EL (1958) Statistical approach to problems of cosmology. J R Stat Soc B 20(1):1–43Google Scholar
- Nicks AD, Gander GA (1994) CLIGEN: a weather generator for climate inputs to water resources and other models. In: Watson DG, Zazueta FS, Harrison TV (eds) Proceedings of fifth international conference on computer in agriculture. ASAE, St. Joseph, MI, pp 903–909Google Scholar
- Nicks AD, Lane LJ, Gander GA (1995) Weather generator. In: Flanagan DC, Nearing MA (eds) USDA-water erosion prediction project: hillslope profile and watershed model documentation. NSERL report no. 10. USDA-ARS Nat. Soil Erosion Research Lab, West Lafayette, in (Chapter 2)Google Scholar
- Racsko P, Szeidl L, Semenov M (1991b) A serial approach to local stochastic weather models. Ecol Model 57(27):41Google Scholar
- Richardson CW, Wright DA (1984) WGEN: a model for generating daily weather variables. US Department of Agriculture, Agricultural Research Service, ARS-8, 83Google Scholar
- Semenov MA, Shewry PR (2011) Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Sci Rep 1(66):1–5Google Scholar
- Stern RD, Coe R (1984) A model fitting analysis of daily rainfall data. J R Stat Soc A147:1–34Google Scholar
- Stöckle CO, Campbell GS, Nelson R (1999) ClimGen manual. Biological systems engineering department, Washington State University, Pullman, WAGoogle Scholar
- Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34(2995):3008Google Scholar