Over the past two decades, climate change and climatic variability have received significant attention from the scientific community. The present study investigates the impact of future climate change on irrigation water requirements in the coastal districts of Odisha, Eastern India, specifically within the Phulnakhara distributary’s command area of the main Puri canal system. Field investigations were conducted during the kharif and rabi seasons of 2019–2020 and 2020–2021. The study offers a new perspective involving a future climate data-driven model with water requirements of RCP 4.5 for this canal command area, and after integrating this with the optimal cropping area, the optimal future irrigation water needs for the kharif and rabi seasons were determined. The study focused on assessing future irrigation water demands under changing climatic conditions, with an emphasis on the conjunctive use of surface and groundwater resources. Projections indicate that peak irrigation demand will occur in the kharif season of 2042–2043 and the rabi season of 2044–2045. Furthermore, a significant decline in groundwater levels is anticipated, ranging from 1.23 to 1.42 m below ground level (BGL) during the kharif season and from 1.46 to 1.64 m BGL during the rabi season, over the next 30 years (2021–2022 to 2050–2051). The most pronounced groundwater table decline is projected for the years 2042–2043 (kharif) and 2044–2045 (rabi), highlighting the need for sustainable water resource management strategies in the region. Based on this study, integrating the optimal crop area with future irrigation water needs will result in groundwater table fluctuations under the permissible limit.

A study was conducted to locate appropriate places for artificial groundwater recharge structures for all the blocks of Durg district in Chhattisgarh. Various thematic maps including district boundaries, drainage, slope, soil texture, land use, lineaments, geology and water level depth were generated in the environment of GIS. These thematic maps were superimposed accordingly to identify the appropriate places for artificial groundwater recharge structures. Different structures and their size have been decided on the basis of topography and drainage pattern. The upper, middle and lower reaches of drainage line were considered for different size of check dams. The site for percolation tank has been identified for both cultivated and uncultivated land. The field survey of the study area was also carried out to find out the suitability of proposed artificial groundwater recharge structures. Results of this study indicated that only 36.8% of the area was found to be very good suitable sites, 13.2% of the area was found to be good suitable sites and about 10% area was found to be moderate suitable sites for recharge. Finally, 62 locations were identified for check dams, whereas, 31 locations were identified for percolation tanks. The sub-surface storage space was estimated to be 32.47 Mm³ on the basis of thickness of available vadose zone. The actual volume of 43.18 Mm³ of surface water required for saturating the vadose zone. It was found that sufficient volume of water required for artificial recharge is available. Availability of source water to recharge the subsurface reservoir has been found to be 882.75 Mm³ in the form of non-committed surplus runoff out of which 30 per cent (264.82 Mm³) is considered as surplus monsoon run-off which is available for artificial recharge.

Mahanadi is one of the major inter-state east flowing perennial rivers in peninsular India. Hamp watershed of Seonath Sub-basin of upper Mahanadi basin was considered for the study to estimate the sediment yield and nutrient loss-based identification of critical agricultural sub-watershed and its critical Hydrological Response Unit (HRU) using Soil and Water Assessment Tool (SWAT) in-terfaced with GIS i.e., ArcSWAT. The study area was divided into 14 sub-watersheds considering topographical parameters derived from DEM and drainage network. The land cover, soil layers, and DEM were used to generate 207 HRUs for analysis of annual runoff, sediment yield and nu-trient loss for 2004-2008 (calibration period) and 2010-2013 (validation period). The sediment yield, runoff estimation and nutrient loss matched consistently well with the monthly and seasonal measured values. On the basis of average annual sediment yield (18.18 t/ha), runoff (245.97 mm) and nutrient loss NO3-N (1.62 kg/ha), respectively, sub-watershed WS4 was categorized under high priority for critical are identification. The sub watershed WS4 comprises of 15 HRUs (No. 36 - 50) with four kharif crops viz rice, soybean, maize and sugarcane. Results showed that the crops soy-bean, maize and sugarcane reduced the average annual runoff by 18.1, 31.4 and 18.0 per cent, respectively whereas the sediment yield was increased drastically by 104.5, 37.5 and 5.7 per cent, respectively as compared to rice. Soybean and maize crops HRU generate significant amount of soil and nutrient loss and were found to be as the critical HRUs for the upper Mahanadi River basin

Groundwater plays an indispensable role in sustaining agricultural production, domestic needs, and industrial development, particularly in water-stressed regions like India. However, over-extraction, unregulated development, and climate variability have placed aquifer systems under significant stress, necessitating scientifically informed approaches to groundwater assessment and management. This chapter presents a comprehensive exploration of groundwater modeling and its application in aquifer recharge planning and sustainable resource governance. It introduces the physical principles underlying subsurface flow, including Darcy’s law and numerical solutions for three-dimensional groundwater systems. A wide range of modeling tools—such as MODFLOW, MIKE SHE, FEFLOW, and Visual MODFLOW—are discussed in the context of both flow and solute transport simulation. A practical case study from Chhattisgarh illustrates the calibration and application of Visual MODFLOW for a multilayered aquifer, assessing recharge potential and evaluating the effects of artificial recharge structures. The chapter also examines advancements in machine learning, remote sensing, and IoT-based monitoring systems that support data-driven water management. Emphasis is placed on integrated strategies combining recharge techniques, efficient irrigation, land use planning, and community participation. By bridging classical hydrogeology with modern modeling tools, the chapter highlights pathways to improve groundwater sustainability and resilience in the face of increasing anthropogenic and climatic pressures.