Groundwater quality characterization using an integrated water quality index and multivariate statistical techniques.

This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as 'good' and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA's factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.

Optimization of virtual water flows in agriculture by changing cropping patterns using an integrated approach.

Utilizing available water resources efficiently is crucial to address both our present and future requirements and plays a vital role in safeguarding food security. This current investigation deals with assessment and optimizing water footprint (WF) and virtual water flow (VWF) for primary crops in Banas River Basin (BRB) using AquaCrop model with local datasets and district-level estimates. VWF in the basins were estimated by multiplying the WF of crops with the amount exported/imported, which is determined based on the difference between production and consumption in the basin. The possibility of changing the cropping patterns was evaluated for the potential reduction of the blue WF. Annual WF from primary crops in the basin amounts to 19,255 MCM/yr (70 % green, 21 % blue and 10 % grey WF, respectively). Banas basin is a net exporter of agriculture commodities with nearly 7391 MCM/yr of water flowing out of the basin due to agricultural exports of which approximately 265 MCM/yr is virtual blue water outflow. Crops having low economic water productivity of blue water are being grown in vast areas resulting in a high blue WF. The optimizing the cropping pattern can result in a 5-42 % lower blue water footprint with 11-39 % higher economic output under different scenarios with and without considering the consumption needs. Changing the cropping pattern and making trade plan to optimize the crop import/exports can be viable option for tackling the blue water scarcity issues in the basin. WF can be managed sustainably by improving water resource allocation for better economic, social, and environmental productivity and going for less aggressive agricultural production.