Estimation of Seasonal Freshwater Inflows in Coastal Southern India using Stable Isotope Analysis and Machine Learning Techniques

The Cochin backwater 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. In addition, seven different advanced machine learning techniques were tested to improve analysis and prediction of seasonal variations in isotopic composition and salinity. 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 enriched isotope values during the pre-monsoon season, showing significant evaporation effects. Carbon (C) isotopic analysis in dissolved inorganic matter (δ¹³C-DIC) at 17 stations during the pre-monsoon season revealed spatially distinct carbon dynamics influenced by various sources. Zone 1, dominated by seawater, exhibited heavier δ¹³C-DIC values. Zone 2 showed significant contributions of lighter terrestrial δ¹³C, while Zone 3 reflected inputs from surrounding paddy fields with a C3 isotopic signature (-25‰), modified by estuarine productivity. The combination of 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, paving the way for better management and conservation strategies.