Sustainable Management Of Groundwater Resources Research Articles

Review: Application of hydrogeological models coupled with agent-based models to address sustainable groundwater management in Latin America

AbstractAgent-based models (ABMs) have long been applied in economics and social science research. The combination or coupling of these models with those of natural systems is a more recent application, and their use in conjunction with groundwater flow and transport models can be considered an innovative way to take advantage of their potential. In the Latin American region, sustainable management of groundwater resources can still be considered understudied. Among other characteristics, the existence of large transboundary aquifers in the region adds a further complication for addressing the sustainability of groundwater resource use. In this type of system, the impacts of joint regulations for utilization by different countries, the diversity of socioeconomic drivers and the conservation interests of each group of users might be consistently represented and explored through flow models coupled with ABMs. This review of the scientific literature aims to compile up-to-date information regarding the application of hydrogeological models coupled with agent-based models, focusing on their contributions to the sustainable management of groundwater resources and analyzing their potential for the Latin American region.

Investigating seawater intrusion and salinization using the integration of hydrochemical and geoelectrical techniques

Seawater intrusion poses a significant environmental threat to water resources, agriculture, and ecosystems. We investigated the extent of seawater intrusion and salinization in the eastern coastal aquifers of Saudi Arabia. Hydrochemical measurements and electrical resistivity tomography (ERT) techniques were employed to assess groundwater quality and subsurface extension of seawater intrusion. Two resistivity profiles, 475 m long each, were analyzed to gain insights into subsurface geology and seawater intrusion. The inversion of the recorded resistivity data revealed four distinct subsurface layers. The uppermost layer consists of surficial deposits, followed by a sandy seawater intrusion layer, beneath that lies a marl layer, and at the bottom, a dolomitic-limestone layer representing the brackish-water aquifer. The ERT results are integrated with hydrochemical and hydrogeological results from 12 wells to overcome the limitations of the ERT. Our conclusions are required to develop efficient mitigation strategies and sustainable management of groundwater resources in the study area or similar coastal regions affected by seawater intrusion. Understanding and delineating the boundary between saline and fresh water is crucial for safeguarding water resources for agricultural purposes, preserving ecosystem health, and ensuring the long-term sustainability of water supplies in coastal areas.

Priorities and potential challenges of sustainable management of ultra-deep groundwater resources in Iran

The water crisis in Iran, and the large share of water allocated to the agricultural sector from groundwater resources has led to access to ultra-deep groundwater (UDGW) resources. Exploration projects have confirmed the presence of UDGW sources of fault origin in Iran. Deciding on the sustainable extraction of UDGW resources is very challenging. In order to better understand the scope and nature of the UDGW literature, our research combines a methodological framework of systematic literature review with an approach to synthesize manifest and latent content. Additionally, we describe expert predictors for and against inductive content analysis, which extracts necessary concepts, priorities, organization, and coding from the data. MFA tool has analyzed sustainability indices, compositions, correlations perceptual behavior of sustainability dimensions of groups in the variables predicting the exploitation of UDGW. In the proposed model, the synthetic index has determined weighting commensurate with the variability of data, to balance the effect of groups of variables. The most important dimensions of sustainability are Proxy/flexibility measures (69.4), Fund (63.3), Safety (63.2) and Extent and possibility of access and exploitation (63.06) of UDGW resources from engineering, social and environmental groups, respectively. However, the performance of Evaluation of quantity and quality of UDGW resources (39.8) and Fairness (42.3) groups were the lowest due to the environmental and social sustainability, respectively. In fact, a fair and sustainable expedient strategy and a restructuring of hydro-social networks that clearly acknowledge the political aspect of resource management and its intricacies are required. The ministry and the UDGW decision-makers themselves must solve these shortcomings.

Assessing groundwater quality in semi-arid conditions: a geographic information systems-integrated approach using water quality index

Meeting freshwater demands in water-scarce regions is imperative, given challenges like hydric stress, urbanization, aridity, and population growth. This study focuses on the Tebessa aquifer system, a critical water source facing escalating demands. A primary objective is to identify actionable management strategies to improve groundwater quality dynamics in semi-arid regions. The methodology involves a comprehensive assessment of groundwater health using the Water Quality Index (WQI) approach. This method integrates diverse parameters influenced by intensive agricultural and industrial activities. Additionally, spatial relationship analysis, facilitated by Geographic Information System (GIS) technology, is employed to gain insights into complex hydrogeological processes. The multi-parameter strategy implemented results in water quality maps that highlight various ions, revealing spatial disparities and assigning quality priority classes from 'Good' to 'Poor' across the groundwater. The research, conducted in the semi-arid Tebessa-Morsott Plain, encompasses field and laboratory investigations to delineate hydrochemical traits, including alkalinity, salinity, and heightened ion concentrations. The results of the study contribute to scientific understanding by uncovering interrelationships and spatial intricacies, offering valuable insights for water resource governance. The effectiveness of the methodology, incorporating WQI and GIS, is demonstrated in the comprehensive evaluation of groundwater quality. Ultimately, this research provides a foundation for informed decision-making and sustainable management of groundwater resources in semi-arid regions.

Site-specific condition evaluation for managed aquifer recharge (MAR) site selection in granitic aquifers, Ghana

ABSTRACT Water has prominence over all the usable natural resources to humankind due to its very significance to human life. Approximately, 70% of Ghana's populace relies on groundwater exploitation for freshwater consumption. Managed aquifer recharge is capable of sustaining water resources for all livelihood activities. Underperformance of MAR systems is often due to site-specific hydrogeological conditions turning out to be less favorable than anticipated. This study evaluates the site-specific conditions for MAR sites identification in granitic aquifers, integrating in situ hydrogeological factors and GIS-MCDA. One hundred and twenty-one datasets from different boreholes comprising hydro geophysics, borehole drilling and pump test reports were used. The constraint mapping results indicate 92% suitable for MAR application within the study area, implying available enormous bare lands for flooding recharge technique. The suitability analysis discloses that 48 and 51% of the studied area fall within very high and high suitable zones, respectively, showing convincing and great potential to support infiltration ponds for MAR technology application. The valuable information provided through this study can serve as a guide for MAR implementation and for sustainable groundwater resources management within the Upper East region of Ghana.

Analysis on the spatiotemporal evolutions of groundwater hydrochemistry and water quality caused by over-extraction and seawater intrusion in eastern coastal China

The over-extraction of groundwater has resulted in seawater intrusion and the southward migration of the saltwater interface, gradually deteriorating the groundwater quality in the Weibei Plain. In this research, groundwater samples were gathered from 46 monitoring wells for shallow groundwater during the years 2006, 2011, 2016, and 2021. The hydrochemical features of regional groundwater and the factors influencing the issue were subjected to statistical analysis. Additionally, the assessment of spatiotemporal variations in groundwater quality was conducted using the customized entropy-weighted water quality index (EWQI) method. The relationship between groundwater over-extraction and the southward intrusion of the saltwater interface was compared and analyzed. The results of this paper revealed that the Weibei Plain has been in a state of long-term over-extraction of groundwater from 2000 to 2021, with an average annual over-extraction of 118.49 million m3. The groundwater depression cone areas in the northern part of the study area increased from 3,247.37 to 4,581.34 km2 from 2006 to 2021, with the center of the cone experiencing a drop in groundwater level from −22 to −85 m. The saltwater interface shifted southward by 711.71 km2 from 2006 to 2021. In groundwater, the high concentrations of TH, TDS, and Cl− were primarily related to the seawater intrusion, while higher concentrations of NO3− were mainly determined by frequent agricultural production, industrial wastewater, and domestic sewage discharges. The groundwater hydrochemical types in the study area transitioned from predominantly HCO3·Ca-Mg type in 2006 to HCO3-Na type and SO4·Cl-Ca·Mg type in 2021 due to seawater intrusion. The results of PCA and HCA show the effects of seawater intrusion, human activities, and rock weathering on groundwater hydrochemistry. The evaluation results based on the EWQI revealed that the average value of the samples in 2021 was 101.36, which belonged to Class IV water quality standards, representing the poorest water quality among the 4 years. The southward migration of the saltwater interface led to the deterioration of groundwater quality in the groundwater depression cone areas, which gradually worsened from 2006 to 2021. The maximum increase in EWQI value was 174.68 during the period, shifting from Class III water quality to Class V water quality. Groundwater quality remained relatively better in the western and southern regions which were less affected or unaffected by seawater intrusion. The results of the study can provide a certain reference value for the sustainable management of groundwater resources and the management of groundwater pollution and seawater intrusion in the Weifang City area in the future.

Delineation of Groundwater Potential Zones for Sustainable Development and Planning using AHP and GIS Techniques in the Coal Mining Province of Mahan River Catchment Area

The present research aims to address the drinking water crisis in the Mahan River catchment area resulting from the disruption of groundwater availability due to extensive coal mining. The study uses GIS-based Multi-Criteria Decision Analysis (MCDA) to map the groundwater potential of the area by analysing several factors that affect groundwater availability, including rainfall, water depth, geomorphology, geology, soil, land-cover/land-use, and topographic characteristics derived from DEM. The groundwater potential map created using the MCDA technique classified the area into low, moderate, and high groundwater potential zones. The map was validated and verified using water table depth and electrical conductivity values available in the region, indicating that it can be used to identify groundwater recharging sites. The study’s results show that about 30% of the area has high groundwater potential, and more than 45% of the area has moderate groundwater potential. The information derived from the study can be used for sustainable management and proper planning of groundwater resources in the Mahan River catchment area. Overall, the study presents a useful approach to address the groundwater depletion problem resulting from coal mining activities in the Mahan River catchment area.

CALDERA: a scientific drilling concept to unravel Connections Among Life, geo-Dynamics and Eruptions in a Rifting Arc caldera, Okataina Volcanic Centre, Aotearoa New Zealand

Abstract. Silicic caldera volcanoes present major volcanic and seismic hazards but also host dynamic hydrothermal and groundwater systems and a rich but largely unexplored subsurface biosphere. Many of these volcanoes are hosted in rift settings. The intricate connections and feedbacks among magmatism, rifting, hydrothermal processes, and the biosphere in these complex systems remain poorly understood, necessitating subsurface joint observations that are only enabled by scientific drilling. The CALDERA (Connections Among Life, geo-Dynamics and Eruptions in a Rifting Arc caldera) project workshop funded by the International Continental Scientific Drilling Program (ICDP) gathered multi-disciplinary international experts in January 2023 to advance planning of a scientific drilling project within one of these dynamic, rift-hosted calderas, the Okataina Volcanic Centre (OVC), Aotearoa New Zealand. The OVC's high eruption rate, frequent unrest events and earthquake swarms, location in a densely faulted rapidly extending rift, abundant groundwater–geothermal fluid circulations, and diverse surface hot spring microbiota make it an ideal location for exploring a connected geo-hydro-biosphere via scientific drilling and developing a test bed for novel volcano monitoring approaches. Drilling configurations with at least two boreholes (∼ 200 and ∼ 1000–1500 m deep) were favoured to achieve the multi-disciplinary objectives of the CALDERA project. Decadal monitoring including biosphere activity and composition has the potential to evaluate the response of the hydro-bio system to volcano-tectonic activity. In addition to the OVC caldera-scale datasets already available, site surveys will be conducted to select the best drilling locations. The CALDERA project at the OVC would provide, for the first time, an understanding of volcanic–tectonic–hydrological–biological connections in a caldera–rift system and a baseline for global comparisons with other volcanoes, rifts, and hydrothermal systems. CALDERA would serve as an unprecedented model system to understand how and how quickly the subsurface biosphere responds to geologic activities. Discoveries will improve assessment of volcanic and seismic hazards, guide the sustainable management and/or conservation of groundwater and geothermal resources and microbial ecosystems, and provide a forum for interweaving mātauranga Māori and Western knowledge systems.

Groundwater Modelling of Jamwa Ramgarh using Visual MODFlow Flex

A steady state finite difference model, MODFLOW, is developed to quantify groundwater in Jamwa Ramgarh, Jaipur (Dt) Rajasthan, using Groundwater data from 06 observation wells. ArcGIS was used to generate various shapefiles required as input files in Visual MODFlow Flex. The findings indicate that the calculated contours of groundwater levels closely correspond to the observed contours. The sustainable utilization and management of groundwater resources pose significant challenges for many countries worldwide. In recent years, groundwater modelling has emerged as an effective approach to tackle these challenges. Various modelling software tools are available to simulate groundwater flow, with MODFLOW being widely used to investigate the interactions between surface water and groundwater and to develop models for specific study areas. MODFLOW, a renowned groundwater modelling software, enables researchers and water resource professionals to understand and analyze the complex dynamics of groundwater systems. By utilizing MODFLOW, it becomes possible to assess the interactions between surface water and groundwater, evaluate the impacts of pumping or recharge activities, and make informed decisions regarding the sustainable management of groundwater resources. The utilization of MODFLOW and similar modelling software plays a crucial role in advancing our understanding of groundwater systems and supporting effective groundwater resource management practices.

Water Governance in Tanzania – A Synthesis of Legal and Institutional Frameworks for Groundwater Management in the Upper Great Ruaha River Catchment

The importance of groundwater resources in buffering the effects of climate change on water scarcity and security is highly acknowledged worldwide. However, groundwater development and use can only be sustainable with a more robust policy and institutional arrangements related to water governance. The purpose of this study was to analyse the legal framework of water resource development and management in order to contribute in identifying policy constraints to sustainable groundwater resource development and management in Tanzania. The paper used information from literature reviews and data collected through qualitative methods using a case study of Usangu Plains in the Upper Great Ruaha River Catchment in Tanzania. The findings reveal that water governance institutional frameworks are well structured to provide guidelines on how water as a basic human resource can be utilised and properly managed, but most importantly, these institutional arrangements place disproportionate emphasis on groundwater. Attaining sustainable groundwater development and use requires new disclosures and narratives in water policy and law, integrating both surface and groundwater resources. The weaknesses identified in groundwater governance need to be taken on Board once, the existing water institutional frameworks open to perfection for sustainable utilisation of groundwater resources

Quantitative and qualitative assessment of groundwater resources for drinking water supply in the peri-urban area of Dhaka, Bangladesh

Groundwater is the primary source of freshwater for drinking, industrial, and agricultural water in many nations worldwide. This vital resource is deteriorating due to rapidly growing urbanization, pollution, overexploitation, and climate change. However, it is unclear how much this specific resource deteriorates over time. Therefore, this study aimed to investigate groundwater resources qualitatively and quantitatively in the peri-urban area of Dhaka, Bangladesh. The study used the modified Mann-Kendall trend test, hotspot analysis, groundwater recharge estimation, multivariate statistical analysis, and the water quality index (WQI) mapping. The trend analysis of groundwater levels indicates that in the most recent year, groundwater levels close to industrial areas have declined at a maximum rate of 1.18 m/year. The estimated groundwater recharge reveals that it is highest (480 mm/year) on rural agricultural land and lowest (220 mm/year) in urban areas. With a 99% confidence level, the groundwater water trend cluster analysis indicates the hotspots of groundwater level depletion near the industrial area. The results showed that Mn, Fe, and As exceeded the World Health Organization's (WHO) recommended limit for drinking water by 76.92%, 61.53%, and 23.07%, respectively. The principal component analysis and hierarchical clustering results indicate that groundwater quality is influenced by rock-water interaction and human interventions. The WQI results suggest that 47.05% of the samples were excellent, 7.69% were good, 9.96% were poor, 17.65% were very poor, and the remaining 17.65% were unfit for drinking purposes. The depletion of groundwater hotspots was identified in northeast region of the study area. Overall, an integrated framework was proposed that offers a holistic approach to sustainable groundwater resource management amid urbanization and industrialization. These findings will provide an essential guideline for groundwater resource management in the peri-urban area of Dhaka city.

Temporal and spatial variations of groundwater quantity and quality for drinking and irrigation purposes in the arid and hot weather of Southern Iran

Groundwater provides a reliable water supply of fresh water worldwide for drinking, agricultural, and industrial purposes, especially in areas where surface water is scarce or unreliable, making it an essential resource for human and economic development. Proper management and protection of groundwater resources are crucial to ensure their sustainability and continued contribution to fulfilling the growing water demand worldwide. This study focused on the quantitative and qualitative changes in groundwater in the Kahoorestan Plain, located northwest of the Persian Gulf in southern Iran. The zoning maps produced by Arc GIS revealed a declining trend in the aquifer level and a drop in the groundwater level of 5.24 m over 20 years. The maps of the flow directions also indicated a general trend from the western highlands to the south and southeast regions of the plain. The Piper diagram showed that the predominant hydrochemical facies of the plain area is NaCl. Temporal changes analysis by the Mann-Kendall test revealed that EC, TDS, TH, Na, Mg, Ca, SO4, and Cl parameters have a rising trend in the majority of wells, which attributes to groundwater over-withdrawal, drought, excessive well drilling, and insufficient aquifer recharge. The study’s analysis of the temporal and spatial changes of the water quality index, Schoeller diagram, and Wilcox diagram revealed that the aquifer’s water quality is unsuitable for drinking and irrigation use due to its high salinity and sodium content. The findings underscore the urgency of adopting effective management strategies to ensure the sustainable management of groundwater resources in the region.

Modeling spatial groundwater level patterns of Bangladesh using physio-climatic variables and machine learning algorithms

Groundwater monitoring is essential for sustainable groundwater resource management in a country like Bangladesh, where this precious resource is gradually declining due to over-extraction. However, traditional methods of acquiring groundwater level (GWL) data over a large area are time-consuming and expensive. To address this, this study proposes an alternative approach using freely available daily groundwater storage (GWS) gridded data from the Global Land Data Assimilation System (GLDAS) and other data sources such as population, rainfall, temperature, irrigation, and elevation. By employing different regression and machine learning models like multiple linear regression (MLR), regression trees, support vector machines (SVM), Gaussian process regression (GPR), and artificial neural networks (ANN), the study aimed to model GWL data for Bangladesh at a spatial resolution of 0.25° × 0.25°. In-situ weekly GWL data collected from 844 locations across the country were used for model development. The results indicated that GWS data alone was insufficient to estimate the spatial variability and trend of groundwater in Bangladesh. However, the comparison of different models showed that the ANN model performed better, with an overall correlation coefficient (R) of 0.95 and mean squared error (MSE) of 0.64 m2 when estimating GWL using GWS and other data. The study also identified population and rainfall as the most influential factors in determining GWL. The developed ANN model can be utilized to estimate GWL at locations where observation data are unavailable, enabling the monitoring of GWL for sustainable groundwater management in Bangladesh.

A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment.

Accurate prediction of the groundwater level (GWL) is crucial for sustainable groundwater resource management. Ecological water replenishment (EWR) involves artificially diverting water to replenish the ecological flow and water resources of both surface water and groundwater within the basin. However, fluctuations in GWLs during the EWR process exhibit high nonlinearity and complexity in their time series, making it challenging for single data-driven models to predict the trend of groundwater level changes under the backdrop of EWR. This study introduced a new GWL prediction strategy based on a hybrid deep learning model, STL-IWOA-GRU. It integrated the LOESS-based seasonal trend decomposition algorithm (STL), improved whale optimization algorithm (IWOA), and Gated recurrent unit (GRU). The aim was to accurately predict GWLs in the context of EWR. This study gathered GWL, precipitation, and surface runoff data from 21 monitoring wells in the Yongding River Basin (Beijing Section) over a period of 731days. The research results demonstrate that the improvement strategy implemented for the IWOA enhances the convergence speed and global search capabilities of the algorithm. In the case analysis, evaluation metrics including the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) were employed. STL-IWOA-GRU exhibited commendable performance, with MAE achieving the best result, averaging at 0.266. When compared to other models such as Variance Mode Decomposition-Gated Recurrent Unit (VMD-GRU), Ant Lion Optimizer-Support Vector Machine (ALO-SVM), STL-Particle Swarm Optimization-GRU (STL-PSO-GRU), and STL-Sine Cosine Algorithm-GRU (STL-SCA-GRU), MAE was reduced by 18%, 26%, 11%, and 29%, respectively. This indicates that the model proposed in this study exhibited high prediction accuracy and robust versatility, making it a potent strategic choice for forecasting GWL changes in the context of EWR.

Identification of groundwater nitrate sources in an urban aquifer (Alborz Province, Iran) using a multi-parameter approach.

High concentrations of NO3̄ in water resources are detrimental to both human health and aquatic ecosystems. Identification of NO3̄ sources and biogeochemical processes is a crucial step in managing and controlling NO3̄ pollution. In this study, land use, hydrochemical data, dual stable isotopic ratios and Bayesian Stable Isotope Mixing Models (BSIMM) were integrated to identify NO3̄ sources and estimate their proportional contributions to the contamination of the Karaj Urban Aquifer (Iran). Elevated NO3̄ concentrations indicated a severe NO3̄ pollution, with 39 and 52% of groundwater (GW) samples displaying the concentrations of NO3̄ in exceedance of the World Health Organization (WHO) standard of 50 mg NO3̄ L-1 in the rainy and dry seasons, respectively. Dual stable isotopes inferred that urban sewage is the main NO3̄ source in the Karaj Plain. The diagram of NO3̄/Cl‾ versus Cl‾ confirmed that municipal sewage is the major source of NO3̄. Results also showed that biogeochemical nitrogen dynamics are mainly influenced by nitrification, while denitrification is minimal. The BSIMM model suggested that NO3̄ originated predominantly from urban sewage (78.2%), followed by soil organic nitrogen (12.2%), and chemical fertilizer (9.5%) in the dry season. In the wet season, the relative contributions of urban sewage, soil nitrogen and chemical fertilizer were 87.5, 6.7, and 5.5%, respectively. The sensitivity analysis for the BSIMM modeling indicates that the isotopic signatures of sewage had the major impact on the overall GW NO3̄ source apportionment. The findings provide important insights for local authorities to support effective and sustainable GW resources management in the Karaj Urban Aquifer. It also demonstrates that employing Bayesian models combined with multi-parameters can improve the accuracy of NO3̄ source identification.

Mapping of groundwater availability in dry areas of rural and urban regions in India using IOT assisted deep learning classification model

Groundwater is a crucial resource for fulfilling the water requirements of India's rural and urban areas. The heterogeneous nature of geological, hydrological, and climatic factors results in substantial variability in the accessibility of groundwater across disparate regions. The present investigation centers on the cartography of groundwater accessibility in arid zones of rural and urban Indian areas using a Deep Learning Classification Model (DL-GWCM) supported by the Internet of Things (IoT). The introductory section underscores the importance of groundwater in India, where groundwater sources cater to around 80% of rural and 50% of urban water demands. The text highlights statistical data derived from surveys that indicate a notable decrease in groundwater levels. This underscores the pressing necessity for implementing effective monitoring and management strategies. The DL-GWCM is a proposed solution that aims to enhance the precision and effectiveness of groundwater availability mapping by incorporating IoT technology and Deep Learning Classification. The DL-GWCM comprises multiple constituent elements, such as Groundwater Prediction, Water Quality Index, and Conventional Neural Network- Bidirectional Long Short-Term Memory (CNN–Bi LSTM) classification. The process of Groundwater Prediction involves the utilization of past data and environmental factors to make precise forecasts of groundwater levels. The Water Quality Index evaluates the quality of subsurface water resources, guaranteeing their secure and enduring utilization. The Deep Learning Classification Model with IoT technology was implemented for groundwater accessibility mapping in Indian arid zones. It integrates Groundwater Prediction, Water Quality Index, and CNN–Bi LSTM classification. The model makes precise forecasts using past data and environmental factors, ensuring secure water quality. Using the CNN–Bi LSTM classification model improves the precision of groundwater availability mapping due to its resilient classification capabilities. These findings suggest that the DL-GWCM outperforms conventional approaches. The mean values of all five metrics for the proposed method are presented as follows: The performance metrics of the model are as follows: Root Mean Square Error (RMSE) of 0.77%, Mean Absolute Error (MAE) of 2.13%, Relative Absolute Error (RAE) of 8.72%, Root Relative Squared Error (RRSE) of 0.92%, and Correlation Coefficient (CC) of 0.92. The results of the proposed methodology facilitate the discernment of regions with abundant or scarce groundwater accessibility, thereby supporting the sustainable management and planning of groundwater resources.

Integrating Remote Sensing Data and MODFLOW Modeling for Sustainable Groundwater Resource Management and Monitoring in the Guinea Region, West Africa

Groundwater is vital in ensuring water security, supporting agriculture, and sustaining ecosystems in the Guinea region of West Africa. However, the region faces increasing challenges related to groundwater availability and quality due to population growth and climate change. This research presents pixel-based spatial-temporal trends assessment of groundwater mappings using an integrated approach that combines remote sensing data with MODFLOW modelling. The study focuses on the Guinea region, characterized by diverse topography and hydrogeological conditions. Remote sensing data, including satellite imagery of shallow groundwater storage (GWS), relative humidity, temperature, NDVI, and rainfall, provided valuable insights into observing hydrological cycles and climate changes. The MODFLOW modelling framework was employed to simulate groundwater flow (Hydraulic Heads) and variability of groundwater levels. It enabled the investigation of groundwater recharge and discharge processes, as well as the impact of climatic variations on groundwater dynamics. Furthermore, the trend modelling approach facilitated and revealed that the direction of the western to the northern part of the study area suffered from a depletion rate of groundwater level. The integrated analysis of remote sensing data and MODFLOW modelling highlights the complex interactions between climate change and groundwater resources. This research underscores the importance of proactive and informed groundwater management strategies to ensure the sustainable use of this invaluable resource. By combining remote sensing technology and groundwater modelling, it offers a holistic approach to support evidence-based decision-making for groundwater resource management and monitoring in the Guinea region of the West Africa.

IoT-based data and analytic hierarchy process to map groundwater recharge with stormwater.

The sustainable management of groundwater resources in developing countries is often challenging due to limited measurement and monitoring infrastructure to collect data necessary for decision support. To make a contribution towards addressing these challenges, this study investigated the use of Internet of Things (IoT) technology and low-cost sensors to collect the required groundwater-level data and develop a model to map the recharge potential with stormwater. The study focused on two stormwater ponds located in a highly urbanised area in Cape Town, South Africa. A combination of Geographic Information System and analytic hierarchy process was integrated to generate a groundwater recharge potential zone map of the study area. The IoT-based data were used to develop and calibrate a numerical groundwater model in MODFLOW. The study determined that retrofitted stormwater ponds are potential groundwater augmentation zones and can provide opportunity for stormwater recharge in urban areas. Overall, this study highlights the potential of IoT to collect hydrogeological data with low-cost sensors. Data can be collected at high temporal resolution, and the spatial scale can be increased due to availability of low-cost sensors.

Groundwater delineation for sustainable improvement and development aided by GIS, AHP, and MIF techniques

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands.

Delineating suitable zones for solar-based groundwater exploitation using multi-criteria analysis: A techno-economic assessment for meeting sustainable development goals (SDGs)

While several articles have focused on determining a suitability map for groundwater exploitation by assigning weights to different thematic layers, this technique suffers from a lack of worldwide application because it focuses on the features of a specific region (terrain features, vegetation, and climate) during the investigation. To overcome this drawback, we expanded the evaluation method of groundwater exploitation by integrating the region characteristics and topography with the three pillars of sustainability outlined by the SDGs agenda. Seven thematic layers, reflecting the economic and technical characteristics of El-Saloum depression, Egypt, were prepared to develop a solar energy-based groundwater suitability map. It was depicted that about 11.5%, 29.3%, and 44.0% of the total depression were classified into “Most’, “Moderate”, and “Marginal” suitable locations, respectively. Based on the sensitivity analysis, the southeastern zone of the depression emerged as the most suitable site for solar-groundwater utilization due to its low salinity (<5000 ppm), reasonable groundwater depths (less than 50 m), and land availability (undeveloped regions). Substituting a diesel power system (DPS) with a photovoltaic system (PVS) in groundwater pumping and/or reverse osmosis (RO) desalination would reduce 1.2 kg CO2/kWh and maintain a payback period of 6.4 years with a cost of energy (COE) of ∼0.25 US$/kWh. Applying PVS for groundwater management could fulfill about 25.0%, 37.5%, 40.0%, and 20.0% of sustainable development goals (SDGs) 2 “Zero hunger”, 6 “Clean water and sanitation”, 7 “Affordable and clean energy”, 13 “Climate action”. The achievement of these SDGs was equivalent to 22.7%, 15.4%, and 13.2% of the environmental, economic, and social pillars of sustainability, respectively. Consequently, the study's findings are crucial for stakeholders and policymakers involved in the sustainable management of groundwater resources within the SDGs umbrella.