Management Of Groundwater Resources Research Articles

Combination of remote sensing, GIS, AHP techniques and geophysical data to delineate groundwater potential zones in the Shiriya River Basin, South India

Shiriya River Basin (SRB) of Kasaragod district, Kerala, is one of the minor river basins that faces acute water shortage soon after the rainy season. Increasing the aquifer productivity of the SRB is the need of the hour. The present study is performed to determine the groundwater potential zones (GWPZ) of the SRB through Weighted Overlay Analysis. Different thematic layers (Geomorphology, Geology, Lineament density, Slope, Drainage density, LULC, and Soil) were prepared. Based on Saaty's scale, the rank and weightage assigned to each thematic layer and their respective features were standardized through the AHP techniques. Subsequently, GIS-based Weighted Overlay Analysis generated the ultimate GWPZ map, dividing SRB into 3 potential zones (poor, moderate, and good). To validate the GWPZ and understand the geoelectric characteristics of the SRB, 50 Schlumberger VES were performed and analyzed. Depth to water level data is also used to validate the GWPZ. The VES data indicated three distinct geoelectric layers, the topsoil, laterite, and weathered/fractured basement, overlying the resistive basement. In the SRB second layer (laterite) and, in some regions, the third layer (fractured charnockite) functions as an aquifer. The presence of groundwater potential region is indicated by resistivity values ranging from 50 to 200 ohm-m. The combination of surface and subsurface indicators of GWPZ will be much more reliable in locating the site for sustainable groundwater exploration and management of SRB groundwater resources.

Modeling of groundwater productivity in the Alfred Nzo District, South Africa, using relative frequency ratio and Shannon entropy models

Study regionAlfred Nzo District, Eastern Cape Province, South Africa. Study focusThe study aimed to identify groundwater potential zones in the Alfred Nzo District using advanced geographic information systems and remote sensing applications by integrating relative frequency ratio (RF) with predictor rate and Shannon entropy (SE) models. Seven influential factors that are thought to control groundwater availability in the Alfred Nzo District were considered to identify groundwater potential zones. These factors are drainage density, lineament density, rainfall, slope, land use and land cover, lithology, and soil class. The factors were weighted in the RF and SE approaches. About 70 % of boreholes were used as training datasets, and 30 % were used for validation. Groundwater potential zones were delineated by integrating all these factors and their corresponding weights in ArcGIS software. The accuracy of each model was evaluated using receiver operating characteristic curve and area under curve techniques. New hydrological insights for the regionFive groundwater potential zones were found in the research area, which includes very poor, poor, moderate, good, and very good, by RF and SE models. The results of the models revealed that the SE model, with a success rate of 79.80 %, performed better than the RF, with a success rate of 75.10 %. The results of this research could be helpful in adequately managing groundwater resources in the research area.

Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques

Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning Techniques. Data from 217 wells across 12 parameters were analyzed, including TDS, EC, Cl−, Fe++, Ca++, Mg++, Na+, SO4−−, Mn++, HCO3−, K+, and pH. The Water Quality Index (WQI) was calculated, and ArcGIS mapped its spatial distribution. Machine learning algorithms, including Ridge Regression, XGBoost, Decision Tree, Random Forest, and K-Nearest Neighbors, were used for predictive analysis. Higher concentrations of Na, K, Ca, Mg, Mn, and Fe were correlated with industrial and densely populated areas. Most samples exhibited excellent or good quality, with a small percentage unsuitable for consumption. Ridge Regression showed the lowest MAPE rates (0.22 % training, 0.26 % in testing). This research highlights the importance of advanced machine learning for sustainable groundwater management in arid regions. Thus, our results could provide valuable assistance to both national and local authorities involved in water management decisions, particularly for water resource managers and decision-makers. This information can aid in the development of regulations aimed at safeguarding and sustainably managing groundwater resources, which are essential for the overall prosperity of the country.

GIS-based multi-criteria decision making for delineation of potential groundwater recharge zones for sustainable resource management in the Eastern Mediterranean: a case study

In light of population growth and climate change, groundwater is one of the most important water resources globally. Groundwater is crucial for sustaining many vital sectors in Syria, including industrial and agricultural sectors. However, groundwater exploitation has significantly escalated to meet different water needs especially in the post-war period and the earthquake disaster. Therefore, the goal was this study delineation of the groundwater potential zones (GPZs) by integrating the analytic hierarchy process (AHP) method in a geographic information systems (GIS) within the AlAlqerdaha river basin in western Syria. In this study, ten criteria were used to map the spatial distribution of GPZs, including slope, geomorphology, drainage density, land use/land cover (LU/LC), lineament density, lithology, rainfall, soil, curvature and topographic wetness index (TWI). GPZs map was validated by using the location of 74 wells and the Receiver Operating Characteristic Curve (ROC). The findings suggest that the study area is divided into five GPZs: very low, 21.39 km2 (10.87%); low, 52.45 km2 (26.65%); moderate, 65.64 km2 (33.35%); high, 40.45 km2 (20.55%) and very high, 16.90 km2 (8.58%). High and very high zones mainly corresponded to the western regions of the study area. The conducted spatial modeling indicated that the AHP-based GPZs map showed a remarkably acceptable correlation with wells locations (AUC = 87.7%, n = 74), demonstrating the precision of the AHP–GIS as a rating method. The results of this study provide objective and constructive outputs that can help decision-makers to optimally manage groundwater resources in the post-war phase in Syria.

MAPPING THE GROUNDWATER POTENTIAL IN SUB-SAHARAN AFRICA: THE CASE OF THE COMMUNE OF LOUMBILA, BURKINA FASO

The objective of this study is to identify and map potential groundwater resource areas in the rural commune of Loumbila in order to assess the potential water accessibility in the area. A spatial data analysis was applied to identify potential groundwater resource areas for drilling. To this end, geomatics tools (Geographic Information Systems and Remote Sensing) and a multi-criteria analysis using the Analysis Hierarchy Process (AHP) technique were deployed. A total of eight (decision) factors with a strong influence on the groundwater storage potential (i.e., soil types, fracture network density, land use, slope, hydrogeology, alterity thickness and drainage density, precipitation) were selected and mapped. An appropriate weight has been assigned to each factor, which were further normalized using the Analytical Hierarchy Process (AHP). Based on the analysis, the rural commune of Loumbila was qualitatively classified into five groundwater potential zones: very low, low, medium, high and very high groundwater potential. The results obtained show that 9.80% (14.75 km2), 22.02% (33.16 km2), 30.68% (46.19 km2), 18.47% (27.81 km2) and 19.03% (28.65 km2) of the rural municipality of Loumbila have very high, high, medium, low and very low groundwater potential, respectively. The groundwater potential mapping aims to identify the areas with the highest potential for the sustainable management of groundwater resources, enabling informed decisions to be taken for its management and conservation.

Scrutinizing different predictive modeling validation methodologies and data-partitioning strategies: new insights using groundwater modeling case study

Groundwater salinity is a critical factor affecting water quality and ecosystem health, with implications for various sectors including agriculture, industry, and public health. Hence, the reliability and accuracy of groundwater salinity predictive models are paramount for effective decision-making in managing groundwater resources. This pioneering study presents the validation of a predictive model aimed at forecasting groundwater salinity levels using three different validation methods and various data partitioning strategies. This study tests three different data validation methodologies with different data-partitioning strategies while developing a group method of data handling (GMDH)-based model for predicting groundwater salinity concentrations in a coastal aquifer system. The three different methods are the hold-out strategy (last and random selection), k-fold cross-validation, and the leave-one-out method. In addition, various combinations of data-partitioning strategies are also used while using these three validation methodologies. The prediction model’s validation results are assessed using various statistical indices such as root mean square error (RMSE), means squared error (MSE), and coefficient of determination (R2). The results indicate that for monitoring wells 1, 2, and 3, the hold-out (random) with 40% data partitioning strategy gave the most accurate predictive model in terms of RMSE statistical indices. Also, the results suggested that the GMDH-based models behave differently with different validation methodologies and data-partitioning strategies giving better salinity predictive capabilities. In general, the results justify that various model validation methodologies and data-partitioning strategies yield different results due to their inherent differences in how they partition the data, assess model performance, and handle sources of bias and variance. Therefore, it is important to use them in conjunction to obtain a comprehensive understanding of the groundwater salinity prediction model's behavior and performance.

Unravelling the signatures of submarine groundwater discharge and seawater intrusion along the coastal plains of Odisha, India: a multi-proxy approach.

Submarine Groundwater Discharge (SGD) and Seawater Intrusion (SWI) are two contrary hydrological processes that occur across the land-sea continuum and understanding their nature is essential for management and development of coastal groundwater resource. Present study has attempted to demarcate probable zones of SGD and SWI along highly populated Odisha coastal plains which is water stressed due to indiscriminate-exploitation of groundwater leading to salinization and fresh groundwater loss from the alluvial aquifers. A multi-proxy investigation approach including decadal groundwater leveldynamics, LANDSAT derived sea surface temperature (SST) anomalies and in-situ physicochemical analysis (pH, EC, TDS, salinity and temperature) of porewater, groundwater and seawater were used to locate the SGD and SWI sites. A total of 340 samples for four seasons (85 samples i.e., 30 porewater, 30 seawater and 25 groundwater in each season) were collected and their in-situ parameters were measured at every 1-2km gap along ~ 145km coastline of central Odisha (excluding the estuarine region). Considering high groundwater EC values (> 3000 μS/cm), three probable SWI and low porewater salinities (< 32 ppt in pre- and < 25 ppt in post-monsoons), four probable SGD zones were identified. The identified zones were validated with observed high positive hydraulic gradient (> 10m) at SGD and negative hydraulic gradient (< 0m) at SWI sites along with anomalous SST (colder in pre- and warmer in post-monsoon) near probable SGD locations. This study is first of its kind along the Odisha coast and may act as initial basis for subsequent investigations on fresh-saline interaction along the coastal plains where environmental integrity supports the livelihood of coastal communities and the ecosystem.

Spatiotemporal recharge estimation in the upper Awash sub-basin, central Ethiopia

ABSTRACT Sustainable groundwater management decisions require an understanding of the spatial distribution and seasonal fluctuations of site-specific water budget computations. This study aims to estimate the spatiotemporal distribution of recharge in the upper Awash sub-basin where the groundwater is experiencing intensive abstraction for domestic, industrial, and irrigation water uses. We estimated the spatial and long-term average monthly, seasonal, and annual groundwater recharge using a GIS-based spatially distributed water balance WetSpass-M model. Distributed grid maps of physical parameters (land-use land cover, soil, and slope) and monthly climatological records (rainfall, maximum and minimum temperature, wind speed) were used as model inputs. The WetSpass-M model estimated recharge is validated with the independently computed recharge using the automated digital filtering baseflow separation method. Attributed mainly to variability in soil texture and land use, the annual precipitation (1,032 mm) is distributed as evapotranspiration (45%), surface runoff (42%), and groundwater recharge (11%). Forest and grass areas with loamy sand have high recharge, while built-up areas with clay soil have low recharge. August to September is estimated to have the largest recharge, while November to December has the lowest. Understanding the spatial and seasonal variability of groundwater recharge is important for sustainable utilization, proper management, and planning of groundwater resources.

Delineation of groundwater potential zones in coastal alluvial region using remote sensing, geographic information system and analytical hierarchy process techniques

Delineation of groundwater potential zone in coastal region is a challenging task. One of the reasons for that is lack of understanding of the geogenic factors in the coastal alluvial areas such as geomorphology, lithology etc. These factors can influence the locations of the groundwater potential zones over the coastal alluvial terrains. The present study is an attempt to analyse the multiple factors to delineate groundwater potential zones in the coastal region of Contai Sub-division, West Bengal, India. In this study, remote sensing, geographic information system (GIS) and multi-criteria decision analysis (MCDA) techniques have been utilized to delineate the groundwater potential zones. Weights were assigned to the parameters using the analytical hierarchical process (AHP) and different classes within each parameter have been ranked on the basis of their relative importance regarding groundwater potentiality. The results of the study were validated by water yield data, water level fluctuation data and geophysical survey. The outcome of validation reveals that a strong correlation (80%) exists between water yield data and groundwater potential zones. Similarly, a good relation occurs for the water level fluctuation data and groundwater potential zones, where the overall accuracy and kappa coefficients are 84.2% and 0.76 respectively. The output of the study reveals that geomorphologically older deltaic plain, older alluvial plain and older coastal plain are good indicators of high groundwater potential regions. Furthermore, the outcome of the study also unveils that lithologically the well sorted medium grained sand is important feature to delineate high groundwater potential regions in coastal alluvial areas. The findings, thus, could establish that present methodology using GIS and AHP techniques has a better potential to identify and map the groundwater potential zones more realistically and can be applied for future planning of groundwater resource management and groundwater exploration in wider coastal alluvial terrain.

Evaluating trends in groundwater quality of coastal alluvial aquifers of Eastern India for sustainable groundwater management.

Groundwater is a precious natural element which ensures global water, food, and environmental security in the twenty-first century. Systematic monitoring, sustainable utilization, preservation and remediation are critical aspects of efficient groundwater resource management. This study deals with the analysis of spatial variability and trend in groundwater chemistry as well as identification of possible contamination sources in a coastal alluvial basin of eastern India. Pre-monsoon season data of 14 groundwater-quality variables measured in 'leaky confined' and 'confined' aquifers were analyzed for ten years (2012-2021). Mann-Kendall (M-K) test with the Sen's Slope Estimator, Spearman Rank Order Correlation (SROC) and Innovative Trend Analysis (ITA) tests were employed to assess decadal (2012-2021) trends. The analysis of the results indicated that the 'critical' water-quality parameters exceeding the acceptable limits for drinking are TDS, EC, TH, pH, Mg2+, Na+, K+, Fe2+, HCO3-, Cl- and NO3-. Weak negative correlations between rainfall and groundwater elevation for both the aquifers reveal poor rainfall recharge into the aquifers. Therefore, a reduction in groundwater abstraction and augmentation of groundwater recharge is recommended. Trend analysis results indicated that the concentrations of TH, Mg2+ and Fe2+ exhibit significant increasing trends in the 'leaky confined aquifer'. In contrast, significant rising trends in TH, Mg2+, Na+, Fe2+, HCO3- and NO3- concentrations are identified in the 'confined aquifer'. Further, the SROC test could not detect the trends in groundwater quality in most blocks and for many parameters. On the other hand, the ITA test revealed significant trends in most of the parameters of the two aquifers in almost all the blocks. Trend magnitudes of the groundwater-quality parameters based on the Sen's Slope Estimator and the ITA test vary from -63.7 to 58.65mg/L/year for TDS, -14 to 39.07mg/L/year for TH, -1.49 to 4.83mg/L/year for Mg2+, -7.14 to 22.96mg/L/year for Na+, -0.32 to 0.44mg/L/year for Fe2+, -8.33 to 20.75mg/L/year for HCO3-, -26.52 to 31.01mg/L/year for Cl- and 1.29 to 3.76mg/L/year for NO3- over the study area. The results of M-K and ITA tests were found in agreement in all the blocks for both the aquifers. Groundwater contamination in both the aquifers can be attributed to weathering, geogenic processes, mineral dissolution, seawater intrusion, poor recharge pattern and injudicious anthropogenic activities. It is strongly recommended that concerned authorities urgently formulate efficient strategies for managing groundwater quality in the 'leaky confined' and 'confined' aquifers which serve as vital sources of drinking and irrigationwater suppliesin the study area.

Mapping and projecting spatiotemporal trends in groundwater levels and flow direction in Pakistan's water-scarce aquifer system

Groundwater is the most vital and reliable source of fresh water globally. This study examined the variability in Depth to Water table (DTW) below ground level in agriculture-dominated Bari interfluve, Pakistan. Observing water levels empowers access and management of water resources ensuring long-term availability. Changes in the DTW were observed by preparing spatiotemporal maps using piezometric data for both pre-monsoon and post-monsoon seasons by five-year difference intervals from 2005 to 2020. Piezometric observations offer more precise real-time monitoring of subsurface water levels. The trend of the groundwater table was analyzed by non-parametric methods i.e., Mann-Kendall (MK) and Sen's slope estimator in all observation wells of districts at a significance level of 5 percent for sustainable groundwater resource management. The resulting trend of fluctuating water table showed a progressive decline in water level in all districts except a minor increase was also noticed. The seasonal fluctuation (between pre-monsoon and post-monsoon seasons) showed that the average DTW was found to be high in 46% of the total tehsils in the post-monsoon season while in 42% it was low and 12% depicted no change compared to pre-monsoon season. Piezometric observation points of DTW exhibiting a trend at a 5 percent significance level were used to project future water table depths that indicated a further alarming depleting trend for both the pre-monsoon and post-monsoon seasons in lower districts of Bari interfluve. The groundwater pattern calls for precautionary measures to see a reversal in trend or even maintain the groundwater levels for future needs.

Research Advances of Groundwater Nitrate Pollution and Source Apportionment in China

Groundwater nitrate (NO3-) contamination in China has become a serious environmental problem, especially in agricultural production areas, which greatly affects the safety of drinking groundwater and requires urgent attention. In this study, the main sources of groundwater nitrate in China were reviewed, including atmospheric deposition, soil nitrogen, agricultural fertilization, and fecal sewage, among which fecal sewage and agricultural fertilization were the main reasons for the excessive groundwater nitrate. The application of hydrochemical analysis, multivariate statistical analysis, stable isotope tracer method, and microbial source tracking in the source apportionment of groundwater nitrate was summarized. All types of source apportionment methods had certain limitations. It is suggested that a variety of methods should be used to identify the source of groundwater nitrate, and the contribution rate of different pollution sources should be calculated using multivariate statistical analysis and isotope quantitative analysis models. The source apportionment of nitrate pollution has undergone a process from qualitative to quantitative research. At present, the SIAR and MixSIAR models based on δ15N-NO3-and δ18O-NO3- have been used widely to analyze the source of nitrate. However, due to the overlap of isotope characteristic values of different input end-members, the difference in δ15N-NO3-and δ18O-NO3- values under different spatial and temporal changes, and the influence of isotope fractionation in the process of nitrogen migration and transformation, the results calculated by the model remain uncertain. It is necessary to further optimize the analytical method of the model to obtain the source of nitrate pollution and its contribution rate more accurately to further aid in the scientific management of groundwater resources.

Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models

Groundwater surface (GWS), which denotes the vertical extent of the water table or the volume of subterranean water within geologic formations, is pivotal for effective groundwater resource management. Accurately predicting GWS requires comprehensive and precise data to fully understand the influencing factors. The inherent temporal complexity and often incomplete datasets of GWS pose significant challenges to accurate assessments. This research aims to devise a comprehensive method that merges interpolation and prediction techniques to develop a functional model and dynamic system for GWS prediction. The study was conducted on the Azarshahr Plain aquifer in Iran, involving 34 observation wells with partially or entirely missing data. Initial analysis utilized three interpolation methods—Kriging, Support Vector Machine (SVM), and M5P—with the M5P method emerging as the most accurate, evidenced by the lowest Root Mean Square Error (RMSE) of 1.83. Two subsequent scenarios were examined: (1) using the M5P method to interpolate missing data for all 34 wells, and (2) using only data from 15 wells with complete records. GWS levels were predicted using Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models. Comparative analysis highlighted the superior performance of the CNN model in both scenarios, particularly noting its effectiveness in GWS prediction. The improvement of data quality through interpolation significantly enhanced predictive accuracy by approximately 90 percent, thereby increasing the reliability of the predictive models for future groundwater management decisions.

A Global Assessment of Groundwater Recharge Response to Infiltration Variability at Monthly to Decadal Timescales

AbstractPredictions of groundwater fluctuations in space and time are important for sustainable water resource management. Infiltration variability on monthly to decadal timescales leads to fluctuations in the water tables and thus groundwater resources. However, connections between global‐scale climate variability and infiltration patterns and groundwater are often poorly understood because the relationships between groundwater conditions and infiltration tend to be highly nonlinear. In addition, understanding is further hampered because many groundwater records are incomplete and groundwater tables are often anthropogenically influenced, which makes identifying the effects of infiltration variability difficult. Previous studies that have evaluated how infiltration variability controls groundwater are based on a limited number of point measurements. Here, we present a global assessment of how infiltration variability is expected to affect groundwater tables. We use an analytical solution derived from Richards' equation to model water level responses to idealized periodic infiltration variability with periods that range from months to decades, to approximate both the effects of short‐term and long‐term climate variability and thus infiltration patterns. Our global‐scale assessment reveals why infiltration variability would lead to periodicity in groundwater recharge in particular regions. The vadose zone strongly dampens short‐term (seasonal and shorter) variations in infiltration fluxes throughout most of Earth's land surface, while infiltration cycles exceeding 1 year would yield transient recharge, except in more arid regions. Our results may help forecasting long‐term groundwater tables and could support improving groundwater resource management.

Review: Andesitic aquifers—hydrogeological conceptual models and insights relevant to applied hydrogeology

Research on the hydrogeology of andesitic volcanic aquifers in subduction areas is reviewed. Andesitic aquifers are of high interest in volcanic arc islands and subduction zones, where they constitute a strategic water resource. This review gathers a compilation of worldwide results and case studies to propose a generic hydrogeological conceptual model (GHCM). It is based on the geological conceptual model splitting the volcanic edifice, from upstream to downstream, into central, proximal, medial and distal zones. In this geological structure, the GHCM identifies where the main aquifer types (fractured lava, pyroclastic flows, and the volcano-sedimentary basins downstream) and the typical aquitards (lahars, fine pyroclastic falls and surges, indurated pyroclastic flow, and weathered rocks) are structured and organized. To integrate the evolution of volcanoes and some specific volcanic activities, a specific GHCM for old andesitic volcanoes or andesitic shield volcanoes is detailed. The paper also describes how the GHCM results are of use to hydrogeologists in terms of scale (from the lithological units to the regional scale), to effectively site water wells, and to sustainably manage groundwater resources in such aquifers. Among these various scales, the volcanic “flank continuum” is presented as the most adapted to support groundwater resources management. Several ways to improve this GHCM are suggested, notably to better consider the geological complexity of these aquifers.

Modeling groundwater recharge potential zones in the Wadi Yalamlam, Saudi Arabia

Effective management of groundwater resources is vital in arid and semi-arid regions like the Yalamlam Basin. Identifying appropriate places for groundwater artificial recharge is key to addressing water shortages and guaranteeing the long-term viability of aquifers. By combining the Analytical Hierarchy Process (AHP) approach with GIS technology, this research offers a thorough framework for evaluating the potential for groundwater recharge. Data collection for the study includes lineament density, slope, drainage density, rainfall, Land Use/Land Cover (LU/LC), soil, elevation, and TWI, among other crucial variables. In this study, lineament density, slope, drainage density, rainfall, LULC, soil, elevation and TWI are the most sensitive characteristics with relative weights of 30, 23, 16, 11, 8, 5, 4 and 3%, respectively. According to the study’s findings, there are many zones in the Yalamlam region with varying capacities for groundwater recharging. Based on the locations that may be used for groundwater recharge, the study’s findings were categorized into five groups: very high (12.7%), high (23.3%), moderate (29.6%), poor (24.4%), and very poor (10%). The validation of the groundwater protection zones was conducted by using eight wells in the basin. As part of the validation process, the Total Dissolved Solids (TDS) in the boreholes and the groundwater protection zones (GWPZs) were compared. A strong positive correlation was found between them (0.61). The results of this research can be used for the management of groundwater resources and provide valuable information for the creation of policies that focus on specific actions to boost groundwater recharge. These findings may considerably enhance the effectiveness of efforts aimed at implementing customized methods to encourage groundwater recharge. The results suggest that there are substantial prospects for using artificial recharge in groundwater management and planning to tackle the issue of groundwater availability in the study region.

Hydrogeochemical Evaluation of Groundwater Quality and Its Applicability for Various Purposes in the Drought-Prone Konso Zone, Rift Valley, Southern Ethiopia

The present study area, the Konso Zone, is in Rift Valley, Southern Ethiopia, and highly depends on rivers and groundwater for economic and social development. Water availability in this area faces multiple challenges, including scarcity, quality concerns, and seasonal variations. In the Rift Valley region, including the present study area, groundwater quality is particularly problematic due to high fluoride levels. Moreover, the high concentrations of total dissolved solids in Konso Zone groundwater pose challenges for industrial, agricultural, and drinking purposes. GIS-based spatial analysis techniques, hydrogeochemical analyses, and water quality indices were used in the current study. The evaluation of water quality involved the utilization of both groundwater quality and irrigation water quality indices. In the study area, the cations and anions exhibited a general trend with Na+ &gt; Mg2+ &gt; Ca2+ &gt; K+ &gt; Fe2+ and HCO3− &gt; Cl−, SO42− &gt; NO3− &gt; F−. Multiple water types were identified within the research area, including Na-HCO3, Ca-Na-HCO3, NaCl, and Ca-Mg/Cl, with Na-HCO3 being the most prevalent. The formation of the NaHCO3 water type is primarily attributed to ion exchange processes, with siliciclastic aquifers being linked to cation exchange in sodium bicarbonate groundwater. Total Dissolved Solids and SO42− contaminants significantly impact the water quality, resulting in deterioration. Gibbs’s figure indicates that 50% of the water samples were found where rocks were weathered, demonstrating the importance of water-rock interactions in groundwater composition. Water samples containing more fluoride than the WHO’s limit of 1.50 mg/L were deemed unsuitable for consumption. Regarding irrigation suitability, most water samples from the Konso area were acceptable, except for the sodium percentage in 66.6% of the samples, which was unsuitable for irrigation. There are 26.67% of samples that are of questionable quality, 66.67% that are deemed inappropriate, and 26.67% that are deemed unacceptable. These findings underscore the pressing need for comprehensive strategies and interventions to address water quality issues in the Konso Zone, emphasizing the critical importance of monitoring and managing groundwater resources to ensure sustainable and safe water access for various regional uses.

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

Agent-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.

Comprehending hydrochemical fingerprint, spatial patterns, and driving forces of groundwater in a topical coastal plain of Northern China based on hydrochemical and isotopic evaluations

To comprehensively comprehend the hydrochemical features, spatial patterns, and controlling mechanisms of groundwater in the Longkou coastal plain (LCP), a prime coastal plain in northern China, a detailed study utilizing statistical analyses, Piper diagram, Gibbs model diagram, ion ratio methods, and reverse hydrogeochemical simulation was performed on 76 groundwater samples collected in 2021. The findings of this study reveal that the groundwater in the LCP has an average pH value of 7.6, indicating weak alkalinity. The groundwater types are predominantly mixed, followed by Cl–Ca. The NO3− concentration in groundwater is relatively high, with a mean value of 326 mg/L, originating mainly from agricultural production activities. In addition to NO3−, the spatial distribution analysis of the principal chemical constituents in the groundwater demonstrated a subtle north-south gradient. The chemical signatures of groundwater are primarily influenced by the process of rock weathering, resulting from the dissolution of silicates and carbonates minerals as well as gypsum. Indeed, cation exchange represents a pivotal hydrogeochemical reaction within the groundwater system of the region. The intrusion of seawater into groundwater primarily exhibits a mild nature, mostly in the form of mixing intrusion in the initial stage, affecting the western and northern regions of the LCP to a greater extent. The primary source of groundwater recharge is attributed to atmospheric precipitation; however, it is partially affected by evaporation. In addition, LCP groundwater is also being recharged by seawater intrusion, mainly in the western and northern parts of the study area. Overall, the findings of this research hold significant implications for the strategic management of groundwater resources and the implementation of measures to prevent and mitigate seawater intrusion not only in the LCP but also in analogous coastal regions across the globe.

Spatio-temporal trends in long-term seasonal groundwater level of South-western Punjab using non-parametric statistical tests.

To manage groundwater resources and develop an action plan, it is crucial to understand the long-term behavior of groundwater level (GWL) fluctuations. In this study, Geographic Information System (GIS) and non-parametric statistical tests were applied for detecting long-term (1973 to 2020) spatio-temporal variations and trends in GWL from 137 observation wells evenly distributed across the south-western part of Punjab. This region has experienced significant changes in GWL over the decades. The non-parametric statistical tests included Mann-Kendall (MK), Sens's SlopeEstimator (SSE), and Innovative Trend Analysis (ITA). The study observed significant trends in GWL fluctuations before and after monsoon. The MK and SSE tests showed a statistically increasing trend in observation wells with about 65.7% and 67.2% increase before and after monsoon, respectively. The innovative trend analysis (ITA) also revealed a statistically increasing trend in observation wells with an increase of about 63.5% and 65.7% pre and post-monsoon season, respectively. The results indicate lowering of GWL in the northern districts of southwestern Punjab, while the southern districts experience rising GWLs. This discrepancy can be attributed to diverse agricultural activities and reduced over-exploitation of groundwater in the southern district due to soil salinity and the presence of brackish groundwater. These findings provide valuable insights into the dynamics of GWL in the studied region, highlighting notable trends associated with seasonal variations.