Principal component analysis has been applied to thirteen dimensionless geomorphic parameters for sixteen watersheds of the Chambal catchment of Rajasthan, India, in order to group the parameters under different components based on significant correlations. Results of the principal component analysis clearly revealed that first two principal components are strongly correlated with some of the geomorphic parameters. However, the third principal component is not found to be strongly correlated with any of the parameters but is moderately correlated with stream length ratio and bifurcation ratio. Furthermore, on the basis of the results, it is evident that some parameters are highly correlated with components but the parameters of hypsometric integral and drainage factor could not be grouped with any of the component because of its poor correlation with them. The principal component loadings matrix obtained using correlation matrix of ten parameters reveals that first three components together account for 87.01% of the total explained variance. Therefore, principal component lading matrix is applied in order to get

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The streamflow prediction is an essentially important aspect of any watershed modelling. The black box models (soft computing techniques) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years (1992-2011) rainfall and other hydrological data were considered, of which 13 years (1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004) was for training and rest 7 years (2005)(2006)(2007)(2008)(2009)(2010)(2011) for validation of the models. The mode performance was evaluated by R, RMSE, EV, CE, and MAD statistical parameters. It was found that, ANN model performance improved with increasing input vectors. The results with fuzzy logic models predict the streamflow with single input as rainfall better in comparison to multiple input vectors. While comparing both ANN and FL algorithms for prediction of streamflow, ANN model performance is quite superior.

The present study aimed to assess the groundwater quality in the hard rock aquifer system of the Nand Samand catchment for irrigation use in Rajasthan, India, by employing distinct water quality indices (SAR, percent sodium (%Na), electrical conductivity, residual sodium carbonate, soluble sodium per cent (SSP), Kelly's ratio, and permeability index) and also by using graphical illustration techniques (United States Salinity Laboratory (USSL) diagram, Piper, Gibbs, Wilcox, and Chadha diagram). Groundwater samples were collected in two seasons, i.e., pre- and post-monsoon seasons (for the years 2019 and 2020). Ninety-five samples were collected and analysed to assess overall groundwater quality for irrigation use. The USSL diagram indicated that the maximum groundwater samples were classified under categories C3S1 and C4S1 during the pre-monsoon season, indicating groundwater suitable for irrigation. The major facies observed in groundwater are mixed Ca–Mg–Cl, CaHCO3, and Ca–Mg–Cl type. Gibbs's diagram depicts that the maximum groundwater samples belonged to the evaporation–crystallization zone, which raises salinity by raising sodium and chloride concerning the increase of total dissolved solids.