Deciphering the nitrate sources and processes in the Ganga river using dual isotopes of nitrate and Bayesian mixing model.

The dual isotopes of dissolved NO3- (n = 43) has been used to delineate the nitrate sources and N-cycling processes in the Ganga river. The proportional contribution of nitrate from different sources has been estimated using the Bayesian mixing model. The seasonal NO3- concentration in the lower stretch of the river Ganga varied between 4.1 and 64.1 µM with higher concentration during monsoon and post-monsoon season and lower concentration during the pre-monsoon and winter season. The temporal variation in the isotopic values ranged between +0.0 and +9.6‰ for δ15NNO3- and -1.2 to +11.0‰ for δ18ONO3-. The spatial NO3- concentration during the post-monsoon season varied between 23.2 and 57.7 µM, with higher values from the middle and lower values from the lower stretch of the river Ganga. The isotopic ratio during the post-monsoon season varied between -1.0 and +11.3‰ for δ15NNO3- and -4.6 to +5.2‰ for δ18ONO3-. The temporal dataset from the lower stretch of the river Ganga showed the dominance of nitrate derived from the nitrification of soil organic matter (SOM) (average ∼53.4%). The nitrate contribution from synthetic fertilizers was observed to be higher during the post-monsoon season (34.7 ± 23.4%) compared to that in the monsoon (25.5 ± 19.5%) and pre-monsoon (22.2 ± 19.6%) season. No significant seasonal variations were observed in the nitrate input from manure/sewage (∼13.9%). Spatial samples collected during the post-monsoon season showed higher contribution of synthetic fertilizer in the lower stretch (34.6 ± 22.7%) compared to the middle stretch (21.1 ± 18.2%), which indicates greater influence of the agricultural activity in the lower stretch. The dual isotope study of dissolved NO3- established that the nitrate in the Ganga river water is mostly derived from the nitrification of incoming organic compounds and is subsequently removed via assimilatory nitrate uptake. The study also emphasises significant nitrification and assimilatory nitrate removal processes operating in the mixing zone of the Ganga river and Hooghly estuary.

Linking Land Use Land Cover change to global groundwater storage.

Groundwater storage is facing the constant threat of over-exploitation and irreversible depletion, often attributed to agricultural and industrial usage as well as human mismanagement. While several methodologies, varying from well logs to gravity recovery data, have been successfully adopted over the years to track and mitigate groundwater loss, Land Use and Land Cover (LULC) has never been quantified to evaluate groundwater storage and variability. LULC change alters the hydrological connectivity between the surface and subsurface water. Towards this, we employed a decision tree based Machine Learning model to (a) identify hydrological and terrestrial drivers affecting groundwater resources, (b) predict shallow and deep groundwater variability, (c) rank the drivers according to their impact on groundwater distribution, and (d) understand groundwater distribution as a function of LULC change. The model was developed globally, and then extended to basinal scale observations in the Indus, Ganga and Brahmaputra rivers of the Indian subcontinent. Model output has helped to (a) compute the 'infiltration index' associated with each Land Cover, (b) equate cropland expansion among the three basins with shallow and deep groundwater storage and (c) link LULC-groundwater change to crop yield. RCP 2.6 crop yield estimates for the 21st century proves detrimental to Indian food and freshwater security, given the strong coupling of groundwater-LULC among the three basins and how Land Cover change translates to groundwater storage.