Journal of Geo-information Science >
Analysis on the Spatio-temporal Variability of Rainy Season Precipitation in Henan Province
Received date: 2012-07-18
Revised date: 2012-12-24
Online published: 2013-06-17
With the extensive application of geographic information systems and the deeply development of geography disciplines, the spatial and temporal structure and process analysis are receiving more attention. The analysis of spatio-temporal variability of precipitation is the basis for the understanding of formation and development of regional water resources. It not only reveals the change of time-series, but finds the spatial structure and changing pattern. Thus, it provides the basis for predicting the drought and waterlogging. Based on the rainy season precipitation data of the past 51 years in Henan Province, combined with a digital elevation model (DEM), using regression analysis, spatial autocorrelation, simulation of spatial interpolation, and cross-validation, we conducted an analysis of spatial and temporal variability of precipitation in Henan Province. The result reveals that: (1) the trend is clear, and the rainy season precipitation in Henan Province overall has shown an increasing trend and in recent years it is particularly evident; (2) The differences of monthly precipitation are obvious, the maximum value is in July, and the average reaches 178.3 mm. (3) The spatial variance exists. There is a clear pattern that the precipitation in the south and east are more than that of north and west in spatial. There is a strong clustering characteristic that in the south, Luoshan and Huangchuan counties as the center formed the rainfall abundant areas, while in the north, Hui County as the center formed the rainfall scarce areas. Lin, Luanchuan and Xixia counties as spatial outliers, are significantly higher than the adjacent regional precipitation. After the spatial autocorrelation analysis, the spatial and temporal anisotropy can be acquired. Therefore, according to the spatio-temporal analysis, we get the interpolation map with Cokriging method, and it tallies with the prior conclusion.
JIANG Li-Guang, TAO Chi-Jun, WEI Xi-Chang, LIU Zhao-Fei, TUN Shan-Shan . Analysis on the Spatio-temporal Variability of Rainy Season Precipitation in Henan Province[J]. Journal of Geo-information Science, 2013 , 15(3) : 395 -400 . DOI: 10.3724/SP.J.1047.2013.00395
[1] 康淑媛,张勃,柳景峰,等.基于Mann-Kendall 法的张掖市降水量时空分布规律分析[J]. 资源科学,2009,31(3):501-508.
[2] 李丽娟,王娟,李海滨.无定河流域降雨量空间变异性研究[J].地理研究,2002,21(4):434-440.
[3] 祝青林,张留柱,于贵瑞,等.近30 年黄河流域降水量的时空演变特征[J].自然资源学报,2005,20(4):477-482.
[4] 夏军,欧阳春,HUANG G. H., 等.基于GIS 和差异信息测度的海河流域水文气象要素时空变异性分析[J].自然资源学报,2007,22(3):409-414.
[5] 叶长青,甘淑,李运刚.红河流域降水量的时空变异特征[J].云南大学学报(自然科学版),2008,30(1):54-60.
[6] Kang-tsung Chang(陈健飞,等译).地理信息系统导论(第三版)[M].北京:清华大学出版社,2009.
[7] 黄杏元,马劲松.地理信息系统概论(第三版)[M].北京:高等教育出版社,2008.
[8] 陈述彭,鲁学军,周成虎.地理信息系统导论[M].北京:科学出版社,1999.
[9] 柏延臣,李新,冯学智.空间数据分析与空间模型[J].地理研究,1999,18(2): 185-190.
[10] 王劲峰,李连发,葛咏,等.地理信息空间分析的理论体系探讨[J].地理学报,2000,55(1):92-103.
[11] Anselin L. Exploring spatial data with GeoDa: A workbook[EB/OL]. 2005,129-137. http://www.csiss.org/clearinghouse/GeoDa
[12] Anselin L, Syabri I, Kho Y. GeoDa: An introduction tospatial data analysis[J]. Geographical Analysis, 2005,38(1):5-22.
[13] Anselin L. An introduction to spatial autocorrelation analysiswith GeoDa[EB/OL]. http://sal.agecon.uiuc.edu/
[14] 王劲峰,廖一兰,刘鑫.空间数据分析教程[M].北京:科学出版社,2010,101-107.
[15] 胡庆芳,杨大文,王银堂,等.利用全局与局部相关函数分析流域降水空间变异性[J].清华大学学报(自然科学版),2012,52(6):778-784.
[16] Sawada M. Global spatial autocorrelation indices: Moran's I, Geary's C and the General Cross-Product Statistic[EB/OL].http://www.lpc.uottawa.ca/publications/moransi/moran.htm
[17] 孟斌,张景秋,王劲峰,等.空间分析方法在房地产市场研究中的应用——以北京市为例[J].地理研究,2005,24(6):956-964.
[18] 何红艳,郭志华,肖文发.降水空间插值技术的研究进展[J].生态学杂志,2005,24(10):1187-1191.
[19] Goovaerts P. Geostatistical approaches for incorporatingelevation into the spatial interpolation of rainfall[J]. Journalof Hydrology, 2000(228):113-129.
[20] 汤国安,杨昕.ArcGIS 地理信息系统空间分析实验教程[M].北京:科学出版社,2006.
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