Integrated Hydrological and Groundwater Modelling for Sustainable Water Resource Management in the Kabini River Basin: A Comprehensive Review

Abstract

This review paper presents an integrated hydrological–groundwater modelling framework to evaluate surface-subsurface interactions in the Kabini River Basin. The synthesis highlights its role in sustainable water resource management under climate and anthropogenic pressures. An integrated surface water-groundwater modelling framework combining hydrological and groundwater models is reviewed to assess hydrological processes, groundwater dynamics, and sustainable water resource management strategies in the Kabini River Basin.” Integrated management of water resources in river basins needs a comprehensive understanding of surface water and groundwater interactions, especially in areas where water demand is increasing, land use patterns are changing, and the climate is fluctuating. The Kabini River Basin, a large sub-basin of the Cauvery River Basin in southern India, is of prime importance for irrigation, drinking water supply, hydropower, and sustainable ecosystems. But increasing human-induced pressures and spatial-temporal variability in water availability have accentuated the need for scientifically sound decision-support systems. This review provides a comprehensive synthesis of integrated hydrological and groundwater modelling techniques for water resource management in river basin scales, particularly in the Kabini River Basin. The review critically assesses the popular surface water models (such as SWAT, HEC-HMS, MIKE SHE, and VIC), groundwater models (such as MODFLOW and its derivatives), and fully or loosely coupled integrated models that couple surface and subsurface processes. The discussion focuses on the model structure, data requirements, calibration and validation procedures, and the ability of each modelling strategy to capture essential hydrological processes such as recharge, base flow contribution, river-aquifer exchange, and groundwater abstraction. Recent advances that incorporate remote sensing data, geographic information systems (GIS), climate model outputs, and machine learning algorithms are also reviewed to emphasise their importance in enhancing model accuracy and mitigating data uncertainties in data-scarce basins.