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新疆阿克苏河流域降水空间变异特征分析
Research on the Spatial Variability of Rainfall in Akesu River Basin
查看参考文献9篇
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文摘
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根据阿克苏河流域降水空间观测数据，其降水稀疏且分布不均匀的特点．选取不同模型对降水空间变化规律进行研究。其结果精确性差异很大。通常应用地统计理论研究降水空间变异性．一般只涉及单个变量．传统的多元回归分析虽然涉及多个变量的影响。但缺乏区域化的空间结构特征。揭示具有协同区域化特征的降水空间变异现象及建立其空间分布模型。既要考虑多元信息的空间位置关系．即同一变量在不同地理位置上的相关性．又要考虑多元信息由于空间重复性引起的协同关系．即同一地理位置上不同变量的相关性。本文用阿克苏河流域范围内的降水观测数据建立析取一协克立格模型，考虑高程变量对降水量空间分布的影响．定量地揭示降水区域化变量的空间变异规律，并将其结果用于降水量的空间最优插值。 |
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其他语种文摘
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The spatial variability of rainfall is a major problem in its description and prediction. Akesu river basin lies in the semi-arid and arid region of northwestern China. The local climate and topographic factors affect the magnitude and distribution of rainfall. Rainfall has very low total magnitude in the southeast region, but in the northwest region comparatively more precipitation events occur. Fifteen representative stations were selected for years to reflect the regional rainfall patterns throughout the region. The study area lies between 40°00′-42°00′N and 76°00′-82°00′E. The network of rainfall gauging stations in the southwest is sparse and the available data are insufficient to characterize the highly variable spatial distribution of rainfall in this mountain area. Therefore, in areas where data are not available, it is necessary to develop methods to estimate rainfall using data from the surrounding measuring stations. The major goal of this study is to characterize the spatial variability of annual rainfall by suitable variogram models, which are then used in the kriging process for assigning values to ungauged locations for compiling mean rainfall isoline map. The multiple-regression model for predicting rainfall results in many prediction errors because this kind of model considers precipitation to be independent without spatial distribution pattern and mutual interdependence. Geostatistics, which is based on the theory of regionalized variables, is increasingly preferred in hydrology and meteorology because it allows one to capitalize on the spatial correlation between neighboring observations to predict attribute values at unsampled locations. More and more cases have shown that the geostatistical prediction technique provides better estimates of rainfall than conventional methods. Kriging is a geostatistical estimation technique for regionalized variables that exhibit an autocorrelation structure. Kriging algorithm, based on unbiased and minimum-variance estimates, involves a linear system of equations to calculate the weights. Such a structure can be described by a semivariogram of the observed data. This paper presents one univariate and two multivariate geostatistical algorithms for the spatial prediction of rainfall： ordinary kriging, ordinary co-kriging and disjunctive eo-kriging incorporating elevation for precipitation prediction. The resulting semivariograms are anisotropic and fitted by spherical models. Cross validation is used to compare the prediction performances of the three geostatistical interpolation algorithms. The three geostatistical algorithms outperform the multiple regression, in particular the disjunctive co-kriging which stresses the importance of accounting for spatially dependent rainfall observations in addition to the colocated elevation. Cross validation and validation withhold one or more data samples and then make a prediction to the same data location. If the prediction errors are unbiased, the standardized mean prediction errors should be near zero, small root-mean-square prediction errors, average standard error near root-mean-square prediction errors, and standardized root-mean-square prediction errors near one. The prediction error results of the cross validation demonstrated that disjunctive co-kriging is one of the best approaches to describe the spatial variability of rainfall and the result is used to plot isoline maps of rainfall in Akesu river basin. |
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来源
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地球信息科学
,2006,8(1):131-138 【扩展库】
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关键词
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阿克苏河流域
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地统计模型
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空间变异
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半变异函数
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地址
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中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室, 北京, 100101
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1560-8999 |
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学科
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地球物理学;水利工程 |
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基金
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国家自然科学杰出青年基金
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世界银行项目
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文献收藏号
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CSCD:2307872
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