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Impact of number of realizations on the suitability of simulated weather data for hydrologic and environmental applications

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Abstract

Stochastic weather generators are widely used in hydrological, environmental, and agricultural applications to simulate weather time series. However, such stochastic models produce random outputs hence the question on how representative the generated data are if obtained from only one simulation run (realization) as is common practice. In this study, the impact of different numbers of realizations (1, 25, 50, and 100) on the suitability of generated weather data was investigated. Specifically, 50 years of daily precipitation, and maximum and minimum temperatures were generated for three weather stations in the Western Lake Erie Basin (WLEB), using three widely used weather generators, CLIGEN, LARSWG and WeaGETS. Generated results were compared with 50 years of observed data. For all three generators, the analyses showed that one realization of data for 50 years of daily precipitation, and maximum and minimum temperatures may not be representative enough to capture essential statistical characteristics of the climate. Results from the three generators captured the essential statistical characteristics of the climate when the number of realizations was increased from 1 to 25, 50 or 100. Performance did not improve substantially when realizations were increased above 25. Results suggest the need for more than a single realization when generating weather data and subsequently utilizing in other models, to obtain suitable representations of climate.

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Acknowledgements

This study was made possible in part by funding from the Purdue Climate Change Research Center, Purdue University, West Lafayette, Indiana and funding provided by USDA National Institute of Food and Agriculture (Project No. IND010639R).

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Authors and Affiliations

  1. Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St, West Lafayette, IN, 47907, USA

    Tian Guo, Sushant Mehan, Margaret W. Gitau & Dennis C. Flanagan

  2. National Center for Water Quality Research, Heidelberg University, Tiffin, USA

    Tian Guo

  3. Department of Statistics, Purdue University, West Lafayette, USA

    Qi Wang & Thomas Kuczek

  4. USDA Agricultural Research Service, National Soil Erosion Research Laboratory, West Lafayette, USA

    Dennis C. Flanagan

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  1. Tian Guo
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Correspondence to Margaret W. Gitau.

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477_2017_1498_MOESM1_ESM.docx

Figure S1: Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots; Figure S2: Number of wet spells obtained from the generated precipitation; Figure S3: Number of dry spells obtained from the generated precipitation; Figure S4: Number of days with no rainfall; Figure S5: 99th percentile of daily precipitation; Figure S6: Number of days with maximum temperature greater than 32 °C precipitation; Figure S7: Number of days with minimum temperature less than 0 °C; Figure S8: Cumulative probability plots for percent error between the statistical characteristics; Table S1: The probability of percent error between the statistical characteristics of the simulated and observed data between − 5 and 5% (DOCX 10718 kb)

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Guo, T., Mehan, S., Gitau, M.W. et al. Impact of number of realizations on the suitability of simulated weather data for hydrologic and environmental applications. Stoch Environ Res Risk Assess 32, 2405–2421 (2018). https://doi.org/10.1007/s00477-017-1498-5

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