Prioritization of watershed through morphometric parameters: a PCA-based approach

Remote sensing (RS) and Geographic Information Systems (GIS) techniques have become very important these days as they aid planners and decision makers to make effective and correct decisions and designs. Principal Component Analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables. It reduces the dimensionality of the data set and identifies a new meaningful underlying variable. Morphometric analysis and prioritization of the sub-watersheds of Shakkar River Catchment, Narsinghpur district in Madhya Pradesh State, India, is carried out using RS and GIS techniques as discussed in Gajbhiye et al. (Appl Water Sci 4(1):51–61, 2013b). In this study we apply PCA technique in Shakkar River Catchment for redundancy of morphometric parameters and find the more effective parameters for prioritization of the watershed and discuss the comparison between Gajbhiye et al. (Appl Water Sci 4(1):51–61, 2013b) and the present prioritization scheme.