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Groundwater is the most reliable source of fresh water. Due to several criteria such as increased population, urbanization, and industrialization, the groundwater sources are under severe threat. Climate change plays a vital role in the quality and quantity of groundwater sources. Also, the climate variability severely affects the parameters influencing the groundwater recharge. Unreliable monsoons and poor quality of the surface water resources tend to increase the decline in the groundwater levels. Hence, it is necessary to identify and delineate the groundwater potential zone (GWP) which can be used to augment the groundwater source. The study is carried out for Kancheepuram district where the groundwater serves as the main source for domestic and agricultural purposes rather than the surface water. The parameters such as topography, geology, drainage density, geomorphology, soil, land use and land cover rainfall, and the lineament density are generated as different layers in the GIS background and are subjected to weighted overlay analysis to obtain the potential zones of groundwater. The weights for the various layers were generated using the multi-criteria decision-making technique and analytical hierarchy process which allows the pairwise comparison of criteria influencing the potential zone. Further, the GWP map has been reclassified into five different classes, namely very high, high, moderate, low, and very low. The results of the study revealed that the very high potential zone comprises 12.68% (546.82 km 2 ), high 25.07% (1081.30 km 2 ), moderate 28.79% (1242.04 km 2 ), low 22.97% (990.96 km 2 ), and very low 10.50% (452.83 km 2 ), respectively. In addition, the results validated using well yield data and pre-monsoon and post-monsoon water level data were found to have a good correlation with the same. Future management plans including natural and artificial recharge practice can be effectively made in these areas as reliable results were obtained with the methodology adopted.

Uncertainty in the supply and demand and a lack of available freshwater resources require a better management plan for sustainable development in agriculturally dependent communities. Therefore, scarce freshwater resources are protected and monitored to prevent contamination. Groundwater is the largest freshwater reservoir, and groundwater zones prone to contamination need to be identified. A precise model that enables the simplification and validation of the assessment process was developed by applying a fuzzy logic technique. A hierarchical fuzzy inference model (HFIM) was developed to better handle the input. The application of the developed model was then compared with the conventional index-based DRASTIC model in the Cuddalore District. The parameters that were found to influence the degree of vulnerability, including the depth of the water table (D), net recharge to the aquifer (R), aquifer media (A), soil properties (S), topography of the area (T), impact of the vadose zone (I), and hydraulic conductivity of the aquifer (C), were considered in the model development. A geographical information system (GIS) framework was utilized to synthesize the DRASTIC model and MATLAB was employed to develop the hierarchical fuzzy inference model. The results obtained from the GIS-DRASTIC model and HFIM were classified into five and seven categories based on their index values, respectively. The models were validated using nitrate concentration (mg/l) data obtained from 40 sampling points in and around the study area. A sensitivity analysis was performed on the models by varying the input from their minimum to maximum values for a selected hydrogeological setting. The results revealed that the HFIM was better at determining groundwater vulnerability levels in the Cuddalore District. It could cope with the uncertainty and nonlinearity of the datasets; the output showed a continuous response to modifications of the input data, which contrasts the DRASTIC model.

Groundwater is a primary source of living which also requires preservative measures for furture generations. Due to the lack of effective management technologies, the wastewater generated by rapid urbanization and industrialization is being disposed untreated, leading to groundwater contamination, caused by infiltration and accumulation. This problem has become more intense in major cities of India. The present work is based on determining the water quality using fuzzy index developed for the Perambalur district, Tamilnadu, India, from where 30 groundwater samples were collected from bore well as well as dug well sources. The research focusses mainly on chemical parameters like total hardness (T.H.), total dissolved solids (TDS.), potential hydrogen (pH), calcium (Ca 2+ ), magnesium (Mg 2+ ), potassium (K), sulphates (SO 4 2− ), total nitrates (NO 3  + NO 2 ), fluoride (F), bicarbonate (HCO 3 ), carbonate (CO 3 2− ) and chloride (Cl 2− ). These parameters were assessed for fuzzy water quality index (FWQI) model, and the index was designed concerning Mamdani fuzzy inference system. Five FIS models with different linguistic variables were developed based on triangular membership function with the implementation of 189 numbers of rules. Finally, fuzzy model was classified into five categories, such as excellent, good, poor, very poor and not-suitable. Based on the results obtained from this model, 6 samples were classified into excellent, 8 samples into good, 12 to poor, 3 to very poor and 1 to not-suitable. In connection with that, the results of proposed model were compared with the output obtained from the deterministic method.

The study on land use and land cover (LULC) changes assists in analyzing the change and regulates environment sustainability. Hence, this research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009–2019 and 2019–2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA-Markov model to forecast future LULC change. It was carried out in four steps: (1) change analysis, (2) transition potential, (3) change prediction, and (4) model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are found to be under stress, developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all 3 years. This study can provide suggestions and a basis for urban development planning as it is highly susceptible to coastal flooding.

Hydrological reaction to climate change anticipates water cycle alterations. To ensure long-term water availability and accessibility, it is essential to develop sustainable water management strategies and better hydrological models that can simulate peak flow. These efforts will aid in water resource planning, management, and climate change mitigation. This study develops and compares Sacramento, Australian Water Balance Model (AWBM), TANK, and SIMHYD conceptual models to simulate daily streamflow at Rajegaon station of the Pranhita subbasin in the Godavari basin of India. The study uses daily Indian Meteorological Department (IMD) gridded rainfall and temperature datasets. For 1987–2019, 70% of the models were calibrated and 30% validated. Pearson correlation (CC), Nash Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (CD) between the observed and simulated streamflow to evaluate model efficacy. The best conceptual (Sacramento) model selected to forecast future streamflow for the SSP126, SSP245, SSP370, and SSP585 scenarios for the near (2021–2040), middle (2041–2070), and far future (2071–2100) using EC-Earth3 data was resampled and bias-corrected using distribution mapping. In the far future, the SSP585 scenario had the most significant relative rainfall change (55.02%) and absolute rise in the annual mean temperature (3.29 °C). In the middle and far future, the 95th percentile of monthly streamflow in the wettest July is anticipated to rise 40.09% to 127.06% and 73.90% to 215.13%. SSP370 and SSP585 scenarios predicted the largest streamflow increases in all three time periods. In the near, middle, and far future, the SSP585 scenario projects yearly relative streamflow changes of 72.49%, 93.80%, and 150.76%. Overall, the findings emphasize the importance of considering the potential impacts of future scenarios on water resources to develop effective and sustainable water management practices.

Soil moisture near the surface and subsurface is significant for estimating crop water demand in rainfed areas. Modeling approaches have proven its efficiency in dividing the rainfall into surface and subsurface components in a given system. Calibrating the model parameters is important for simulating the actual scenario which assesses the performance of the model and extends its application. Variable Infiltration Capacity (VIC) model is used in this study to estimate the soil moisture by parameterizing the crop and soil properties at a 5 Km spatial resolution with homogenous crop and soil. The measured volumetric soil moisture is taken from the probe installed at G. B. Pant University of Agriculture and Technology, Pantnagar, India for calibration at different end states. The site-specific information is forced into the model, and the model run is made for both monsoon and non-monsoon season. The calibrated soil moisture is compared against the simulated for each iteration. The statistical performance of the model varies between 0.81 and 0.89 for correlation coefficient (R), 0.1 to 0.70 for Nash Sutcliffe Efficiency (NSE) and 0.77 to 0.80 for Kling Gupta Efficiency (KGE). The performance of model estimates in terms of P-Bias varies from 9.02 to 9.38 in the respective layers. This study concludes that the definition of root fraction, Leaf Area Index and local meteorological conditions plays a crucial role and are found to be the sensitive components for estimating water balance components in modeling framework. The crop and soil specific calibration enhances the further understanding of hydrological processes.

Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R2, and RMSE values of 0.95, 0.979, 0.937, and 30.805 m3/s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m3/s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making.

Satellite-based Gravity Recovery and Climate Experiment (GRACE) provides a quantity of available terrestrial water storage and combining the soil moisture from Global Land Data Assimilation System (GLDAS) offering estimation of groundwater storage changes for a region. We applied satellite-driven GRACE–GLDAS data in Weinganga–Wardha and Mahanadi basin to analyze the variation of groundwater storage variation and emphasising the concernment of complex aquifer system to improve the groundwater monitoring. Groundwater-level trends were analyzed for spatial and temporal variation of various aquifer systems. In situ groundwater-level observation and GRACE and its area application comprise selecting pixel. Six pixels from combine GRACE–GLDAS outputs were selected with various aquifer systems, where each pixel contains 10–50 monitoring wells. Groundwater storage anomaly derived using monthly GRACE Release 05 version of the Global Land Data Assimilation System (GLDAS) product for each pixel from 2002 to 2016. Correlation analysis was performed between GWSA (actual) and GWSA (grace) using linear regression. Correlation results show that the simple aquifer was good agreement during premonsoon and during postmonsoon; although the performance was poorer with complex aquifers system. It was found that groundwater storage has been decreasing for many years. This study highlights the significance of integrating GRACE sensitivity in the assessment of groundwater storage change in various aquifer systems.

In hydro-meteorological studies, precipitation is an important parameter that is utilized in irrigation system design and management, as well as agricultural planning. Under the effects of climate change, precipitation is predicted to alter, which will have an impact on sustainable development. Using rainfall data for the Indravathi subbasin, Godavari basin, from 1998 to 2016, the spatial variability and temporal trend of precipitation were examined over the region, which has a humid tropical climate. Using non-parametric tests like Mann-Kendall (MK) and Sen’s slope approach, analysis for trend detection was conducted. The test data were loaded into ArcGIS software, which then performed monthly, seasonal, and annual analyses of the spatial and temporal trends of rainfall. According to an analysis of the monthly rainfall trends, February had the largest spatiotemporal declining trend in rainfall, while September had the highest spatiotemporal increasing trend. An analysis of seasonal rainfall data revealed a considerable increase in the tendency for summer rainfall over nearly 80% of the basin. Data on annual precipitation revealed an increase in annual precipitation for the basin’s central eastern sections. The findings of this research may help the decision makers and stakeholders make the most efficient use of hydrological resources by providing insight on the effects of climate change and climatic changes on precipitation patterns in the Indravathi subbasin.