Application of principal component analysis in grouping geomorphic parameters of a watershed for hydrological modeling

Abstract

Principal component analysis has been applied to 13 dimensionless geomorphic parameters on 8 subwatersheds of Kanhiya Nala watershed tributary of Tons River located in Part of Panna and Satna district of Madhya Pradesh, India, to group the parameters under different components based on significant correlations. Results of principal component analysis of 13 geomorphic parameters clearly reveal that some of these parameters are strongly correlated with the components but texture ratio and hypsometric integral do not show correlation with any of the component. So they have been screened out of analysis. The principal component loading matrix obtained using correlation matrix of eleven parameters reveals that first three components together account for 93.71 % of the total explained variance. Therefore, principal component loading is applied to get better correlation and clearly group the parameters in physically significant components. Based on the properties of the geomorphic parameters, three principal components were defined as drainage, slope or steepness and shape components. One parameter each from the significant components may form a set of independent parameters at a time in modeling the hydrologic responses such as runoff and sediment yield from small watersheds.

Fragmentation of the Hyporheic Zone Due to Regional Groundwater Circulation

“…However, when the independent parameters were normalized, more than two parameters were required (Table S4 in supporting information). The PCA results supported, to some extent, previous findings that stressed the importance of not only the watershed slope but also the drainage density and the shape of the watershed shape for the interaction between surface water and groundwater (Sharma et al, ). However, this study also identified subsurface hydraulic conductivity (soil type) as important both as an independent parameter and as an explanation for the degree of interaction between deep groundwater and the hyporheic zone.…”

“…Moreover, the PCA was applied on nonnormalized and normalized (with , and with ) forms of the selected independent parameters, where C i is an independent parameter and μ i and σ i are the mean value and standard deviation of each independent parameter i for all the selected reaches, respectively. Previous studies suggested that two to three watershed parameters stand for the dominant loadings to the principal components (Onesti & Miller, ; Sharma et al, ). The investigation here adds subsurface parameters, such as hydraulic conductivity and Quaternary deposit thickness, which are believed to be important for surface water‐groundwater interaction.…”

The Implementation of a Hybrid Model for Hilly Sub-Watershed Prioritization Using Morphometric Variables: Case Study in India

“…The authors used a quantitative analysis of morphometric variables based on RS and GIS techniques. PCA was applied based on linear, areal, and shape variables of 8 SW at the Kanhiya Nala watershed, a tributary of Tons River, Madhya Pradesh, India [26], to categorize the used parameters under various components using correlation analysis. The results revealed that some parameters have strong correlation with the components; however, hypsometric integral and texture ratio did not indicate any correlation with the components.…”

Development of a Composite Hydrologic Index for Semi‐Arid Region of India

“…To pacify the effect of nonuniform rainfall distribution and to aid rain water harvesting in semi-arid regions grouping of geomorphic parameters of a watershed is important for hydrologic modeling using principal component analysis (PCA) (Sharma et al 2015;Farhan et al 2017;Liu et al 2017). PCA can also be used for evaluation and management of water quality (Parinet et al 2004;Gajbhiye et al 2015aGajbhiye et al , 2015b and also in the development of water sustainability index (Ali 2009).…”