Abstract
Spatially explicit distribution of mean annual precipitation are required in the quantitative research on several water-related issues. The difference of distribution of precipitation has complicated reasons, one of them being the spatial correlation between multivariate meteorological factors. In this study, collaborative kriging interpolation (CKI) was used to estimate the spatial distribution of mean annual precipitation in China. Precipitation data from 756 meteorological stations were used, and spatial correlations between seven meteorological factors were analyzed, including annual precipitation, average barometric pressure, average wind speed, average temperature, average water pressure, average relative humidity, and annual average sunshine hours. The estimation results were assessed by means of cross-validation with the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that adding the spatial correlation analysis between multivariate meteorological factors can help improve the prediction performance.
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Acknowledgments
The authors would like to thank the support of the National Natural Science Foundation of China (Study on Pre-qualification Theory and Method for Influences of Disastrous Meteorological Events, Grant No. 91224004) and the youth talent plan program of Beijing City College (Study on Semantic Information Retrieval of Decision Analysis of Emergency Management for Typical Disastrous Meteorological Events, Grant No. YETP0117).
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Zhang, F., Zhong, S., Yang, Z., Sun, C., Huang, Q. (2016). Spatial Estimation of Mean Annual Precipitation (1951–2012) in Mainland China Based on Collaborative Kriging Interpolation. In: Bian, F., Xie, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2015 2015. Communications in Computer and Information Science, vol 569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49155-3_69
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DOI: https://doi.org/10.1007/978-3-662-49155-3_69
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