Seasonal Freshwater Inflows in Cochin Backwater Estuary Inferred from Stable Isotopes and Machine Learning

The Cochin Backwater region in Southern India is one of the most dynamic estuaries, strongly influenced by seasonal river runoff and seawater intrusion. This study explores the relationship between monsoonal rains, salinity, and stable isotopic composition (δ18O and δ13C) to estimate the contribution of freshwater fluxes at different seasonal intervals for the Cochin Backwater (CBW) estuary. Seasonal variations in oxygen isotopes and salinity revealed distinct trends indicative of freshwater–seawater mixing dynamics. The comparison of Local and Global Meteoric Water Lines highlighted the occurrence of enriched isotope values during the Premonsoon season, showing significant evaporation effects. Carbon (C) isotopic analysis in dissolved inorganic matter (δ13CDIC) at 17 stations during the Premonsoon season revealed spatially distinct carbon dynamics zones, influenced by various sources. These characteristic zones were categorized as Zone 1, dominated by seawater, exhibiting heavier δ13CDIC values; Zone 2, showing significant contributions of lighter terrestrial δ13C; and Zone 3, reflecting inputs from regional and local paddy fields with a distinct C3 isotopic signature (−25‰), modified by estuarine productivity. In addition, different advanced machine learning techniques were tested to improve analysis and prediction of seasonal variations in isotopic composition and salinity. Although the data were sufficiently robust for demonstrating the feasibility and advantages of ML in isotopic hydrology, further expansion of the dataset would be essential for improving the accuracy of models, especially for δ13C. The combination of these advanced machine learning models not only improved the predictive accuracy of seasonal freshwater fluxes but also provided a robust framework for understanding the estuarine ecosystem and could pave the way for better management and conservation strategies of the CBW estuarine system.