基于GIS和多元统计技术的约旦南部Mujib-Wala流域定量区划

Y. Farhan, N. Al-Shaikh
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引用次数: 15

摘要

在干旱和半干旱流域,自然资源(即土地、水和生态资源)的可持续管理以及流域管理是应用形态计量学研究中的关键问题。地貌参数及其相互关系对于表征这些集水区的形态、地形、地质和结构、水文潜力和地貌演变至关重要。利用ASTER DEM和GIS分析了与W.Mujib-Wala流域相关的76个已划分子流域的空间特征和形态发展。多元统计技术,如主成分分析(PCA)、聚类分析(CA)和判别分析(DA),也被用于评估排水网络的不同方面及其形态计量特性。主成分分析(PCA)将22个形态计量参数简化为5个成分,解释了90.4%的总方差。评估了这些成分与形态计量变量和各个亚流域的关系,然后探讨了形态计量描述符之间的相互关联程度。76个子流域根据其与组成部分的个体关系以及形态特征的相似性进行了分类。采用层次聚类分析(CA)实现了亚水体的区域化。通过判别分析对所得聚类组的有效性进行统计学检验。本调查提供的信息突出了地貌分析和多元统计在模拟水文响应方面的优势:即地表径流和沉积物产量、水文评估、水资源规划和流域管理。此外,研究结果可用于水土保持规划和山洪潜在性评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantitative Regionalization of W. Mujib-Wala Sub-Watersheds (Southern Jordan) Using GIS and Multivariate Statistical Techniques
In arid and semi-arid watersheds, sustainable management of natural resources (i.e. land, water and ecological resources), and watershed management are crucial issues in applied morphometric studies. Geomorphometric parameters and their interrelationships are of paramount importance in characterizing the morphology, topography, geology and structure, hydrological potential, and geomorphic evolution of such catchments. An analysis of spatial characteristics and morphological development of the demarcated 76 sub-watersheds related to W. Mujib-Wala catchment, was carried out using ASTER DEM and GIS. Multivariate statistical techniques such as Principal Component Analysis (PCA), Cluster Analysis (CA), and Discriminant Analysis (DA), were also employed to assess different aspects of drainage networks, and their morphometric properties. Principal Component Analysis (PCA) reduces the 22 morphometric parameters to five components, which explain 90.4% of total variance. The relationship of these components to the morphometric variables and to the individual sub-watersheds was evaluated, and then the degree of inter-correlation among the morphometric descriptors was explored. The 76 sub-watersheds were classified according to their individual relation to the components, and similarities in their morphometric characteristics. Regionalization of sub-watertsheds was achieved using hierarchical Cluster Analysis (CA). The validity of the resultant cluster groups was tested statistically by means of Discriminant Analysis. The present investigation provides information which highlights the benefit of geomorphometric analysis and multivariate statistics in modeling hydrological responses: i.e., surface runoff and sediment yield, hydrological assessment, water resources planning, and watershed management. Furthermore, the results can be useful for soil and water conservation planning, and assessment of flash floods potential.
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