Suitability of the Lower Ganga basin groundwater for irrigation, using hydrogeochemical parameters and land-use...

The northern Ganga basin is one of the most densely populated basins in the world. Most agricultural and industrial contaminants drained in the river length are likely to be accumulated in the lower part of the Ganga basin. In this study, we have used ten parameters obtained from 495 sampling locations, besides using long-term climate data (GLDAS_NOAH025_M) to understand the irrigation suitability using the TOPSIS model. Multi-criteria decision making (MCDM) model using TOPSIS has been used to make the best choices from the available finite number of alternatives based on their ranking. The entropy weights for the irrigation suitability parameters such as electrical conductivity (Ec), sodium adsorption ratio (SAR), magnesium hardness (MH), sodium percent (Na%), total hardness (TH), Kelly's ratio (KR), permeability index (PI), chloride concentration (Cl^-), groundwater level fluctuation (GWLF), and the Lang factor (Df) are found to be 0.08, 0.14, 0.02, 0.02, 0.04, 0.08, 0.01, 0.32, 0.29, and 0.01, respectively. We find that SAR, Cl^-, and GWLF control the water quality for irrigation in the Lower Ganga basin since these parameters have relatively higher entropy weights (more than 0.10). The results obtained from the computed performance index or the closeness coefficient show that the area percent having very good and good groundwater quality for irrigation in the Lower Ganga basin is 77.03% and 22.97% respectively. The land-use change dynamics for the between 2000 and 2015 estimated using the transition matrix shows a positive percentage change for settlement (133.50%), wetland (35.04%), and bare area (0.98%); however, several other classes such as the agriculture (- 0.85%), forest (- 0.49%), grassland (- 14.38%), sparse vegetation (- 11.39%), and water (- 4.12%) show a decreasing trend. The highest amount of percentage change was observed in settlement areas which were contributed by other land-use classes such as agriculture (694.43 km²), water (41.61 km²), forest (16.77 km²), and grassland (1.86 km²). The results may be useful to the concerned organization for the proper planning and management of water resource for sustainable development.