Chinese Journal of Agrometeorology ›› 2019, Vol. 40 ›› Issue (06): 341-349.doi: 10.3969/j.issn.1000-6362.2019.06.001

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Reproducibility Evaluation of Multi-Site Stochastic Weather Generators: a Comparison between a Typical Parametric Model and a Non-Parametric Model

ZHOU Ling-feng, MENG Yao-bin, LU Chao, WU Gan-lin, ZHANG Dong-ni, SONG Hao-zheng, WU Dan   

  1. 1. Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education/Academy of Disaster Reduction and Emergency Management/Ministry of Emergency Management & Ministry of Education/Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; 2. Hunan Liuyang Hydrology and Water Resources Bureau, Liuyang 410300
  • Online:2019-06-20 Published:2019-06-11

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Abstract:

Many impact models (e.g., hydrological and agricultural models) require simulations of weather variables reflecting the spatial and temporal dependence of observed meteorological fields. New techniques are recently available to generate weather variables simultaneously at multiple locations. This paper presents a comparison of two types of multi-site stochastic weather generators (MulGETS model and k-NN model) for simulation of precipitation and temperature at a network of 12 stations in Xiang River Basin, China. These two models were evaluated for their ability to reproduce the statistical features of the historical meteorological field. The results showed that both MulGETS and k-NN model were successful in reproducing the mean, standard deviation, and skewness of the weather variables, while the performance of k-NN was generally superior to that of MulGETS. The k-NN model was found to perform satisfactorily in preserving the spatial structure of the weather variable, especially the spatial intermittence. Only MulGETS model could generate extreme values out of the historical range. New technology is needed because both MulGETS and k-NN model have the limitation in representing temporal dependence of weather sequence, especially the autocorrelation of daily precipitation.

Key words: Multi-site, Weather generator, Model comparison, Xiang River Basin