TITLE:
Quantitative Regionalization of W. Mujib-Wala Sub-Watersheds (Southern Jordan) Using GIS and Multivariate Statistical Techniques
AUTHORS:
Yahya Farhan, Nisrin Al-Shaikh
KEYWORDS:
ASTER DEM, Morphometric Assessment, Principal Component Analysis (PCA), Cluster Analysis (CA), Discriminant Analysis (DA), GIS
JOURNAL NAME:
Open Journal of Modern Hydrology,
Vol.7 No.2,
April
28,
2017
ABSTRACT: 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.