Sustainable Water Resources Research Articles

Analyzing urban water metabolism of Adama city using water mass balance method for advancing water sensitive interventions

Urban water metabolism focuses on measuring water inflows and outflows within a defined urban system. As an emerging concept, it provides valuable understandings into water flow dynamics, supporting evidence-based decision-making. One approach to quantify these flows is the urban water mass balance method, which accounts for both human-induced and natural water resources. By equating these flows, it identifies whether water movement within the system is linear or circular. The primary goal of water mass balance analysis is to assess how closely a city aligns with water-sensitive management approaches. However, urban metabolism studies are rare in developing countries, where cities often lack the experience to estimate water inflows and outflows for informed water-sensitive interventions. This study addresses this gap by analyzing Adama city in Ethiopia using the water mass balance method to measure its water metabolism. The result revealed that the city faces a negative water balance with outflows exceeding inflows by 46.89 million cubic meters annually. The results indicated that Adama’s water flow follows a linear “take-make-use-dispose” model. The imbalance in Adama’s water cycle is driven by urbanization, impervious surfaces, and climate change, which increase runoff and evaporation. The study found that 61.3% of the city’s water comes from a centralized system, with 90% sourced from distant rivers through a telecoupling system. In the city, inadequate water harvesting, high population density and intensive water use are worsening water scarcity. Urban water metabolism indicators reveal significant losses and indicating the need for water conservation efforts. Despite the reliance on centralized systems, the study identifies strong potential for decentralized solutions and alternative water harvesting. To tackle these challenges, the research recommends adopting water-sensitive strategies such as low-impact development, sustainable urban drainage systems, and water-sensitive urban design and planning. These approaches can reduce the negative effects of urbanization, mitigate urban water scarcity risks and improve water management through water sensitive management approach. The study also emphasizes the need for collaborative learning, community involvement, and innovative technologies, supported by legal frameworks to ensure effective water wise interventions. Shifting toward circular water management and decentralized water systems will boost Adama’s resilience and promote sustainable water resource management, making the city more internally self-sufficient.

Geoelectrical resistivity mapping for sustainable groundwater management in Umuahia South: Insights from vertical electrical sounding

The study of geoelectrical resistivity provides critical insights into subsurface characteristics and aquifer dynamics, particularly in regions with varying geological formations. This research investigates the geoelectrical properties of the aquifer system in Umuahia South, highlighting its significance for sustainable groundwater management. The primary aim of this study is to characterize the geoelectrical layers, assess aquifer thickness and resistivity, and evaluate the implications for groundwater resource management. Vertical Electrical Sounding (VES) was conducted across ten locations to measure the resistivity and thickness of subsurface layers. The data collected were analyzed to identify distinct geoelectrical layers, their respective resistivity values, and thicknesses. The study reveals significant variations in aquifer properties across the region, with VES 1 showcasing the largest thickness of 69.2 m and VES 9 the smallest at 5.2 m. Aquifer thickness decreases from the Benin Formation toward the Ameki and Ogwashi-Asaba Formations. Hydraulic conductivity ranges from 0.207 to 0.954 m/day, indicating varying groundwater flow potential, while transmissivity values vary from 4.965 to 30.441 m²/day, with 60% of aquifers classified as intermediate. Groundwater Potential Index (GWPI) highlights approximately 70% of the area as high potential, underscoring the need for targeted management strategies to optimize water resource extraction and sustainability. The variability in resistivity and thickness across the study area suggests significant subsurface heterogeneity. High resistivity values indicate favorable conditions for groundwater flow, particularly in areas like VES 6. Conversely, lower resistivity in locations such as VES 9 indicates potential challenges for groundwater storage. This study provides vital information on the aquifer system in Umuahia South, emphasizing the need for targeted groundwater management strategies based on the geoelectrical characteristics identified. The research contributes to a deeper understanding of the aquifer dynamics in the region and offers a framework for sustainable groundwater resource management, highlighting the importance of geoelectrical assessments.

Climate Change Adaptation and Resource Resilience in Semi-Arid Regions: Strategies, Challenges, and Case Studies

Semi-arid regions face unique climate change challenges that threaten local ecosystems, agriculture, and water resources. This review explores climate adaptation strategies, resource resilience practices, and policy interventions tailored to semi-arid ecosystems, with a focus on Rajasthan, India, as a representative region. Key strategies include sustainable water resource management, soil conservation, and climate-smart agriculture. Through case studies and a comparative global analysis, this review underscores the importance of blending traditional and modern practices to enhance resilience and advocates for policy and technological innovation to support sustainable adaptation. Insights gained from this review may serve as a foundation for adaptation strategies applicable to other semi-arid regions globally.

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Climate change and human activities alter the global freshwater cycle, causing nonstationary processes as its distribution shifting over time, yet a comprehensive understanding of these changes remains elusive. Here, we develop a remote sensing-informed terrestrial reanalysis and assess the nonstationarity of and interconnections among global water cycle components from 2003 to 2020. We highlight 20 hotspot regions where terrestrial water storage exhibits strong nonstationarity, impacting 35% of the global population and 45% of the area covered by irrigated agriculture. Emerging long-term trends dominate the most often (48.2%), followed by seasonal shifts (32.8%) and changes in extremes (19%). Notably, in mid-latitudes, this encompasses 34% of Asia and 27% of North America. The patterns of nonstationarity and their dominant types differ across other water cycle components, including precipitation, evapotranspiration, runoff, and gross primary production. These differences also manifest uniquely across hotspot regions, illustrating the intricate ways in which each component responds to climate change and human water management. Our findings emphasize the importance of considering nonstationarity when assessing water cycle information toward the development of strategies for sustainable water resource usage, enhancing resilience to extreme events, and effectively addressing other challenges associated with climate change.

Capacity of an aquatic macrophyte, Pistia stratiotes L., for removing heavy metals from water in the Oued Fez River and their accumulation in its tissues.

Water resources, particularly rivers, are increasingly exposed to pollutants, especially heavy metals of chemical origin, which are difficult to monitor and can pose risks to both ecosystems and human health. This study assesses heavy metal contamination in the Oued Fez River, focusing on the bioaccumulation by the invasive plant Pistia stratiotes. The methodology involves measuring and comparing metal concentrations in water and plant tissues. Results revealed that while aluminium (Al) slightly exceeded recommended levels at 0.2978 mg L-1, other metals like zinc (Zn), iron (Fe), copper (Cu), lead (Pb) and cadmium (Cd) remained within acceptable limits. The study demonstrates P. stratiotes' effectiveness in heavy metal phytoremediation, with its roots showing high bioaccumulation up to 19,726 mg kg-1 for Fe and 15,128 mg kg-1 for Al, indicating its potential for water decontamination. Eco-toxicological assessments, including bioconcentration and translocation factors, confirm the plant's capacity to mobilize toxic metals. Statistical analysis also points to possible industrial, urban, or agricultural contamination sources based on correlations between Al, Fe and Zn. The study underscores P. stratiotes' role in phytoremediation while emphasizing the need for monitoring and controlling contamination sources and managing the spread of this invasive species to ensure sustainable water resources.

Potential Sites for Rainwater Harvesting Focusing on the Sustainable Development Goals Using Remote Sensing and Geographical Information System

An innovative way to combat water scarcity brought on by population increase and climate change is rainwater harvesting (RWH), particularly in arid and semiarid areas. Currently, Pakistan is facing major water issues due to underprivileged water resource management, climate change, land use changes, and the sustainability of local water resources. This research aims to find out the suitable sites and options for RWH structures in the Quetta district of Pakistan by integrating the depression depth technique, Boolean analysis, and weighted linear combination (WLC) with hydrological modeling (HM), multicriteria analysis (MCA), a geographic information system (GIS), and remote sensing (RS). To find suitable sites for RWH, a collection of twelve (12) thematic layers were used, including the slope (SL), land use land cover (LULC), subarea (SA), runoff depth (RD), drainage density (DD), lineament density (LD), infiltration number (IFN), distance from built-up area (DB), distance from roads (DR), distance from lakes (DL), maximum flow distance (MFD), and topographic wetness index (TWI). The Boolean analysis and WLC approach were integrated in the GIS environment. The consistency ratio (CR) was calculated to make sure the assigned weights to thematic layers were consistent. Overall, results show that 6.36% (167.418 km2), 14.34% (377.284 km2), 16.36% (430.444 km2), 18.92% (497.663 km2), and 18.64% (490.224 km2) of the area are in the categories of very high, high, moderate, low, and very low suitability, respectively, for RWH. RWH potential is restricted to 25.35% (666.86 km2) of the area. This research also identifies the five (5) best locations for checking dams and the ten (10) best locations for percolation tanks on the streams. The conducted suitability analysis will assist stakeholders in selecting the optimal locations for RWH structures, facilitating the storage of water, and addressing the severe water scarcity prevalent in the area. This study proposes a novel approach to handle the problems of water shortage in conjunction with environmental and socioeconomic pressures in order to achieve the sustainable development goals (SDGs).

Inorganic–Organic Hybrid Nonwoven Fabrics with Colorimetric Detection Capability for Borate Ions in Aqueous Solutions

Abstract A nonwoven fabric sheet with an immobilized colorimetric reagent was prepared to detect borate anions in aqueous media. The fabric comprised immobilized chromotropate and 1-butanesulfonate anions and chromotropate, as the colorimetric reagent, per anion-exchange capacity of the layered double hydroxide. The resulting sheet quantitatively detected borate anions through photoluminescence measurements. Thus, it can be used for onsite quantitative analysis of dissolved boron in water, with promising potential for water quality monitoring and consequently, sustainable and safe water resources globally.

Integrating remote sensing modeling for drought assessment and vegetation monitoring in El Tarf, Algeria: a 40-year analysis

The escalating frequency and intensity of drought events, exacerbated by anthropogenic climate change, present critical challenges to hydrological resources, ecosystem resilience, and agricultural productivity. In North Africa, and specifically Algeria, comprehending the spatiotemporal patterns of drought is essential for sustainable water resource management and ecological conservation. This investigation focuses on the El Tarf region, located in northeastern Algeria, recognized for its ecological diversity and agricultural relevance. Employing a synergistic methodology, this study integrates the Standardized Precipitation Evapotranspiration Index (SPEI) for long-term drought severity evaluation with the Normalized Difference Vegetation Index (NDVI) to monitor vegetation phenology and health over time. Utilizing the computational power of Google Earth Engine, we process extensive temporal datasets of SPEI spanning from 1980 to 2023, complemented by high-resolution satellite imagery for NDVI computation. This comprehensive approach facilitates an in-depth analysis of drought dynamics, their severity, and impacts on vegetative cover in El Tarf over the past four decades. Our findings indicate significant long-term drought trends, with notable dry spells occurring between 1994 and 2003. Severe drought events were particularly pronounced in December 2022 (SPI = -2.50) and May 2002 (SPI = -2.2), correlating with substantial precipitation deficits. Although areas experiencing extreme drought have diminished, there has been an increase in moderate drought occurrences, suggesting an uptick in drought frequency, which poses risks to hydrological resources and ecosystem health. These observed patterns align with regional climate change projections, highlighting an urgent necessity for adaptive management strategies in agriculture and water resources.

Enhancing industrial wastewater treatment: Magnetite-embedded matrix membrane for efficient tetrahydrofuran separation by pervaporation and vapor permeation techniques

The treatment of industrial wastewater is crucial for recovering water of high purity, especially considering its significance in mitigating environmental pollution and ensuring sustainable water resources. This study focuses on the purification of industrial wastewater, emphasizing the separation of tetrahydrofuran (THF) from aqueous solutions using membrane-based techniques. Composite membranes incorporating magnetic nanomaterial (magnetite) within a sodium alginate matrix were synthesized and evaluated for their effectiveness in purifying water from THF mixtures. Both Pervaporation (PV) and Vapor Permeation (VP) processes were employed, with thorough analysis conducted on the impact of magnetite content on permeate purity. The study identified optimal magnetite content for industrial wastewater treatment and developed a new membrane composition based on these findings. The VP process’s performance was evaluated by determining the permeation flux and separation factor. The highest separation factor of 460 was obtained for an 80% wt. THF-water solution in the VP process. Using these findings, the optimal magnetite content for industrial wastewater treatment was identified, and a new membrane composition was developed. The activation energies for the permeation of THF through the NaAlg-Fe20 membrane were found to be 10.50 kcal/mol and 7 kcal/mol for the VP and PV processes, respectively.

Increased precipitation has not enhanced the carbon sequestration of afforestation in Northwest China

Concerns have been raised about the sustainability of large-scale afforestation in semi-arid regions due to potential water constraints. This study investigated whether increased humidity in the semi-arid regions of northwest China could sustain the continued expansion of afforestation efforts. Using multi-source remote sensing data, we found that between 2012 to 2020, annual cumulative precipitation increased by 2.5 millimeters per year, while annual average carbon sequestration in afforested areas declined by 0.002 kilograms of carbon per square meter per year, indicating asynchronous trends. This disparity was primarily attributed to the trade-off between vegetation photosynthesis and transpiration in response to external water conditions, which led to a decline in the water use efficiency of afforested vegetation. The effect of water use efficiency on carbon sequestration was driven by gross primary productivity, rather than evapotranspiration. These findings underscore the importance of targeted afforestation in semi-arid regions, considering local water resource sustainability.

Enhancing Sustainability in Watershed Management: Spatiotemporal Assessment of Baseflow Alpha Factor in SWAT

The increasing frequency of extreme rainfall events poses significant challenges to sustainable water resource management, leading to severe natural disasters. To mitigate these challenges, understanding the hydrological characteristics of watersheds, especially baseflow, is critical for enhancing watershed resilience and supporting sustainable water quality and resource management. However, conventional watershed models often neglect the accurate simulation of baseflow recession. This study proposes a method for calculating and applying the alpha factor for each hydrologic response unit (HRU) in the Soil and Water Assessment Tool (SWAT), considering both temporal and spatial variability in baseflow. The study watershed has undergone significant development, increasing the need for effective water management strategies that promote long-term sustainability. The alpha factor was computed using BFlow2021, and its effectiveness was evaluated by comparing recession and baseflow estimates under different methods. The results indicate that incorporating monthly HRU-specific alpha factors significantly improves model predictions of recession characteristics, highlighting the need for a more spatially and temporally detailed approach in hydrological modeling. The proposed methodology can help clarify the connection between recession and baseflow and can be applied to ungauged stations, offering a valuable tool for sustainable watershed and water quality management.

Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves

The precise forecasting of groundwater levels significantly influences plant growth and the sustainable management of ecosystems. Nonetheless, the non-stationary characteristics of groundwater level data often hinder the current deep learning algorithms from precisely capturing variations in groundwater levels. We used Variational Mode Decomposition (VMD) and an enhanced Transformer model to address this issue. Our objective was to develop a deep learning model called VMD-iTransformer, which aims to forecast variations in the groundwater level. This research used nine groundwater level monitoring stations located in Hangjinqi Ecological Reserve in Kubuqi Desert, China, as case studies to forecast the groundwater level over four months. To enhance the predictive performance of VMD-iTransformer, we introduced a novel approach to model the fluctuations in groundwater levels in the Kubuqi Desert region. This technique aims to achieve precise predictions of the non-stationary groundwater level conditions. Compared with the classic Transformer model, our deep learning model more effectively captured the non-stationarity of groundwater level variations and enhanced the prediction accuracy by 70% in the test set. The novelty of this deep learning model lies in its initial decomposition of multimodal signals using an adaptive approach, followed by the reconfiguration of the conventional Transformer model’s structure (via self-attention and inversion of a feed-forward neural network (FNN)) to effectively address the challenge of multivariate time prediction. Through the evaluation of the prediction results, we determined that the method had a mean absolute error (MAE) of 0.0251, a root mean square error (RMSE) of 0.0262, a mean absolute percentage error (MAPE) of 1.2811%, and a coefficient of determination (R2) of 0.9287. This study validated VMD and the iTransformer deep learning model, offering a novel modeling approach for precisely predicting fluctuations in groundwater levels in a non-stationary context, thereby aiding sustainable water resource management in ecological reserves. The VMD-iTransformer model enhances projections of the water level, facilitating the reasonable distribution of water resources and the long-term preservation of ecosystems, providing technical assistance for ecosystems’ vitality and sustainable regional development.

Identification of dominant drivers of streamflow spatiotemporal variations in typical mountainous areas in the Hexi Corridor, China

Study regionTypical mountain areas in the Hexi Corridor, China. Study focusWater security and ecosystem sustainability of arid inland river basins are highly dependent on upstream streamflow. However, due to the complex geographical environment and limited observation data in the study region, the attribution of spatiotemporal variations in streamflow influenced by climate change and/or human activities remains unclear. Here, we used partial least squares regression (PLSR) and the Budyko framework to unravel the dominant drivers of spatiotemporal variation in streamflow over the past 30 yr. New hydrological insight for the regionPrecipitation, topographic wetness index, slope, forest land, gross primary productivity, hydrological connectivity, soil organic carbon content, silt content, relative relief, NDVI and gravel content dominated spatial variation in streamflow. Temporal variation of streamflow was sensitive to precipitation and land surface. Specifically, increased precipitation and land surface alteration dominated the increase in streamflow in 50 % of the watersheds and the decrease in streamflow in 33 % of them, respectively. Further, land surface alteration was dominated by expansion of agricultural and built-up areas, weakened hydrological connectivity, increased landscape aggregation and forest cover. Controlling agricultural and built-up areas and the scale of afforestation, and focusing on the dynamics of hydrological connectivity and landscape patterns in the upstream reaches are imperative to maintain the security and sustainability of water resources in the arid inland river basins.

Groundwater-surface water exchanges in an alluvial plain in southern France subjected to pumping: A coupled multitracer and modeling approach

Study regionThe study was conducted in an alluvial plain between the Rhône and the Ouvèze Rivers (in the southeast of France) extensively exploited for drinking water. The research area is characterized by significant groundwater-surface interactions influenced by groundwater pumping activities. Study focusThe aim of this study is to enhance the understanding of interactions between rivers and alluvial aquifers by combined multi-tracer and numerical modeling approaches. Over an 18-month period, groundwater temperature, piezometric levels, and river surface water levels were continuously monitored. Field campaigns focused on conductivity, stable isotopes of water, and radon-222 activity concentration in both groundwater and surface water. Radon-222 was used to quantify water exchanges between the river and the aquifer. A MODFLOW model, calibrated using piezometric data and PEST, was employed to simulate groundwater flow and reactive transport of radon-222 using MT3DMS. New hydrological insights for the regionThe study reveals that river water recharges the aquifer, with radon-222 data delineating this recharge zone. The methodology extended the interpretation of periodic groundwater temperature signals to isotopic signals, allowing the identification of dispersivity and Darcy's velocity. The Ouvèze River was found to contribute approximately 55 % of the pumping water supply, alongside the Rhône. These findings provide valuable insights for sustainable water resource management, demonstrating the relevance of using natural tracers in scenarios where artificial tracers are impractical.

Beyond aquifer boundaries: unveiling the multi-dimensional value of groundwater management in Jeju, Korea

ABSTRACT This paper presents a comprehensive analysis of groundwater valuation in Jeju Island, Korea, which relies heavily on groundwater resources. The research addresses the limitations of conventional valuation methodologies that often undervalue groundwater management and ignore spatial heterogeneity. We employ three non-market valuation aggregation methods: local benefit, national benefit, and a combined heterogeneous benefit approach. The heterogeneous benefit approach divides the population into groups based on their distinct groundwater valuations and then consolidates their willingness to pay. Using a contingent valuation method, we analyze survey data from 542 respondents in Jeju and 1,000 respondents nationwide to capture the comprehensive value of improving groundwater quality, considering both local and non-local dependencies. Results show that national benefits exceed local benefits by a factor of 21.4, and this disparity increases to 21.6 when spatial heterogeneity is included. These findings highlight a significant underestimation in previous studies focusing solely on local beneficiaries. Our approach enhances understanding of the varied values attributed to groundwater by different populations, providing crucial insights for sustainable water resource management

Geogenic and anthropogenic factors influencing groundwater quality for irrigation purposes from a volcano-sedimentary aquifer in the Puebla State, Mexico.

This study investigates the hydrogeochemical characteristics and water quality of the Puebla Valley aquifer, a volcano-sedimentary system predominantly influenced by water-rock interactions and ion exchange processes. The research assesses the suitability of groundwater for agricultural irrigation by applying various water quality indices, including salinity and sodicity indices and Wilcox diagrams and USSL. Seventy-one water samples were analyzed to determine key physicochemical parameters and dominant ion concentrations, revealing that the primary hydrogeochemical facies are HCO -Mg and HCO -Ca. The results indicate a heightened risk of salinization, particularly in agricultural and urban areas, underscoring the need for ongoing monitoring. Natural processes such as water-rock interaction and ion exchange are the main factors influencing groundwater quality. However, anthropogenic activities, particularly the use of fertilizers and irrigation return flows from the Atoyac River, exacerbate groundwater quality degradation. This study emphasizes the necessity of sustainable water resource management to ensure the long-term viability of the aquifer.

Operational Google-Earth-Engine Workflow to Monitor Irrigated Areas in a Semi-Arid Climate

Abstract. Sustainable water resources management relies on advanced hydrological models calibrated from long-term satellite observations of water cycles. Monitoring water resources from Earth-observing satellites is enabled by cloud computing environments like Google Earth Engine (GEE) that support easy processing and analysis of archived data. While there are different satellite-based indicators of water resources availability, this paper focuses on mapping irrigated areas for each summer harvest season. The paper presents an automated workflow to extract yearly irrigated areas from imagery made public by Landsat and Sentinel-2. This method uses a normalised difference vegetation index (NDVI) to identify fields that exhibit higher vegetation greenness than their surroundings and are potentially being irrigated. As a first step, Google Earth Engine was used to create a seasonal maximum NDVI composite on which a grid-based thresholding algorithm was applied to identify irrigated areas in different parts of a catchment. The irrigated areas were then refined using morphological image processing and a quality-control step was conducted with ancillary datasets such as the most recent landuse classification, evapotranspiration data and crop phenology information. Finally, real-world use cases are presented to demonstrate the ability of satellite remote sensing to provide critical information on irrigation practices over the last 4 decades at the catchment scale.

Dynamics of Energy Fluxes in a Mediterranean Vineyard: Influence of Soil Moisture

Accurate evaluation of grapevine water use is essential for optimizing water management and maximizing grapevine yield and berry quality in Mediterranean climates. Understanding the water and heat flux dynamics in a vineyard during grapevine berry maturation is of utmost importance. This study focuses on evaluating sensible and latent energy fluxes at the canopy, the soil beneath the canopy, and the interrow areas. The primary objective is to develop a model framework for accurately estimating these energy fluxes, contributing to a better understanding of their behavior during berry ripening. The model’s accuracy was assessed by comparing the estimated fluxes with those measured by an eddy-covariance system installed at a reference height of three meters in the experimental vineyard. This validation step was essential to confirm the model’s ability to capture the intricate energy flux dynamics of the vineyard, especially during grape maturation. The results revealed a high level of agreement between the observed and estimated fluxes, confirming the model’s reliability. This comprehensive evaluation of energy fluxes provides valuable insights for optimizing irrigation strategies. By doing so, this study contributes to improving grape quality, ensuring sustainable water resource use, and ultimately enhancing vineyard productivity in arid and water-scarce regions.

A global analysis of nearshore and submarine springs: spatial distribution, controlling factors, and probability of presence

Coastal springs act as bi-directionally preferential flow paths between coastal aquifers and oceans. While these springs can supply coastal ecosystems with nutrients, they also present vulnerabilities such as contamination and seawater intrusion. Despite their significance, substantial knowledge gaps exist regarding coastal springs due to their complex hydrogeological nature. This study provides a comprehensive global assessment of coastal springs, focusing on their distribution, controlling factors, and likelihood of occurrence. A global dataset of known coastal springs was compiled and analyzed, revealing 66 % of identified springs in the Mediterranean region, mainly linked to karst systems. In contrast, fewer springs were noted in Africa, South America, and South Asia. Key factors influencing spring occurrence were examined using geostatistical methods and integrated into a multi-criteria decision-making framework to develop a Coastal Spring Probability Index (CSPI) along coastline. High-potential areas for coastal springs were identified in regions characterized by significant carbonate and volcanic rock formations, wetter climates, and active tectonic margins, such as southern Europe, the Caribbean, tropical islands, and eastern Asia. Conversely, regions with dry climates and high water demand, such as North Africa and South America, exhibited a lower likelihood of spring presence. These findings will serve as a baseline for local-scale studies and aimed at improving coastal spring inventories and establishing monitoring networks. It will contribute to vulnerability assessment studies, thereby enhancing the management of coastal groundwater resources. The study emphasizes the importance of multidisciplinary approaches to understand coastal spring dynamics and advocates for a strategic planning in groundwater management and conservation. Ongoing efforts to inventory and monitor coastal springs, coupled with targeted conservation measures, are essential for ensuring the long-term sustainability of coastal water resources and ecosystems.

An approach to demarcate prospective areas for groundwater recharge a case study of Upper Thurinjalar Tamil Nadu, India

India, as the world's largest consumer of groundwater, faces an alarming water crisis, particularly in southern regions like Upper Thurinjalar, Tamil Nadu. The relentless extraction of groundwater has severely depleted reserves, making it increasingly difficult to meet the water demands of households, agriculture, and industry. This unsustainable usage endangers the region's water security, emphasizing the urgent need for sustainable management practices. Immediate action is essential to safeguard these vital resources and secure a sustainable water future for the region. The present study addresses the critical need to identify optimal groundwater recharge areas to mitigate the adverse effects of groundwater depletion. Utilizing Remote Sensing (RS) and Geographic Information Systems (GIS), this research focuses on delineating suitable recharge zones in Upper Thurinjalar, Tamil Nadu, to promote sustainable water resource management. Through geospatial analysis, thematic maps were generated, incorporating variables such as geomorphology, geology, subsurface lithology, lineaments, land use, drainage density, soil types, and slope. Utilizing satellite data within the GIS framework, groundwater recharge zones were classified into three categories: highly suitable (61.06 km2), moderately suitable (214.18 km2), and least suitable (48.07 km2). Additionally, prioritized interventions for 18 rural reservoirs within the same catchment area included sediment removal, depth enhancement, and infrastructure upgrades. The application of artificial groundwater recharge in these targeted areas is anticipated to significantly alleviate irrigation water deficits, thereby advancing sustainable development and rehabilitating degraded land. The use of a weighted overlay technique within Geographic Information Systems (GIS) has proven to be a highly effective method for optimizing water resource management, facilitating the sustainable development of groundwater resources. The results emphasize the importance of promptly undertaking focused interventions, such as prioritizing tank improvements and deploying artificial recharge procedures, in order to guarantee the long-term availability of water. Implementing these proactive measures is essential for reducing water scarcity and promoting the development of unused land in the designated recharge areas.