Assessment Of Groundwater Resources Research Articles

Analyses of groundwater level in a data-scarce region based on assessed precipitation products and machine learning

Groundwater, a vital natural resource globally, faces challenges related to data scarcity, particularly in data-limited regions, such as multiple regions in Morocco. To address this issue, we developed an innovative approach for estimating groundwater levels by utilizing precipitation products and employed a random forest (RF) machine learning (ML) model to fill data gaps. Our study involved a comprehensive assessment of ten distinct precipitation products, comprising six Reanalysis Precipitation Products (RPPs) and four Satellite-based Precipitation Products (SPPs). We evaluated their performance across various time scales, including daily, monthly, and seasonal, across different topographical classes. The outcomes highlighted the consistency of ERA5-based datasets, boasting daily correlation to values higher than 0.6, whereas monthly and seasonal correlations exceed 0.8, except during summer. GPM-IMERG, MERRA2, and CPC-UPP also demonstrated commendable accuracy, particularly in plain and mountainous areas. Nonetheless, CFSR, CHIRPS, PERSIANN CDR, and TRMM datasets exhibited limitations, particularly in high mountain areas. To address data gaps, we initially explored correlations between RPPs, SPPs, and groundwater data. However, these correlations failed to meet the accuracy standards required for precise predictions. Notably, the strongest correlations were observed in monitoring stations located in mountainous regions, indicating significant aquifer recharge activities in these areas. In the subsequent phase, the Multiple Imputation by Chained Equations (MICE) machine learning-based imputation method served as a valuable tool for estimating groundwater levels in regions where ground observations were insufficient. Our trend analysis yielded significant insights, with approximately 95% of groundwater points displaying negative trends, with a maximum rate of -0.91 meters. In contrast, 69% of precipitation stations exhibited negative trends, with a maximum rate of -0.06 millimeters. Our approach offers a promising potential to address the challenges associated with the scarcity of groundwater and precipitation data, making it a valuable tool for the assessment, monitoring, and management of groundwater resources.

Assessment of groundwater resources from geophysical and remote sensing data in a basement complex environment using fuzzy-topsis algorithm

This study addresses the pressing global water challenge by focusing on a typical basement complex area experiencing acute water shortage. Indiscriminate well siting without reliable hydrogeological maps has resulted in failed attempts to address water shortages. To overcome these challenges, this research aims to enhance the accuracy and reliability of groundwater potential assessments by incorporating crucial groundwater-related factors.The Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method was adopted due to its ability to improve multi-criteria decision-making (MCDM) techniques for effective groundwater resource management. Unlike TOPSIS, FTOPSIS better reflects decision-makers' intentions, especially concerning geological boundaries and natural phenomena. To achieve the objectives of the study, geophysical and remote sensing datasets were utilized. The study employed the electrical resistivity method, utilising the Vertical Electrical Sounding (VES) technique with the Schlumberger array while the remote sensing data used were the Digital Elevation Model (DEM) image and Landsat ETM image which were processed to generate key groundwater conditioning factors. These factors were integrated using the FTOPSIS algorithm. This algorithm facilitated the calculation of the groundwater potential indices by assigning weights based on their level of significance to influencing groundwater potential in order to generate the groundwater potential map (GPM). The GPM was classified into five zones of varying groundwater potential, with very low and low potential occupying 74 % of the total area. Validation with well data yielded an impressive 79 % accuracy, showcasing the model's enhanced precision. Beyond improved accuracy, the study's implications extend to practical applications in groundwater resource management. By providing a clearer understanding of groundwater potentiality, the research can inform more robust decision-making frameworks, promoting sustainable water use not only in the study area but also in similar geological settings of the world.

Integrating in-situ data and spatial decision support systems (SDSS) to identify groundwater potential sites in the Esan plateau, Nigeria

Establishing suitable groundwater resource areas is a challenging endeavour across the globe. However, novel spatial technologies have emerged as valuable tools for the effective strategy, management, and assessment of groundwater resources, especially in data-scarce emerging economies. The current study used spatial decision support systems (SDSS) for evaluating and defining groundwater potential sites (GPSs) in Nigeria's Edo central region to promote sustainable governance of groundwater. By merging multiple groundwater contributing theme layers, a leading-edge information-based multiparametric analytical hierarchy process (AHP) was applied to define the groundwater prospective areas. By systematically assigning weights to every subject-specific layer and feature, the subject matter layers of geology, geomorphology, drainage density, slope, soil properties, landuse/landcover, rainfall distribution, hydraulic conductivity, transmissivity, curvature, proximity to surface water bodies, and elevation were generated and used for groundwater potential map generation. Each thematic layer's weights were allocated and adjusted depending on their qualities and relevance to groundwater recharge. The multicollinearity (MC) analysis was used to evaluate the model's predictive capacity. Finally, groundwater potential sites were created by integrating the theme-specific maps with the weighted total overlay computation tool. The study area contained three separate groundwater potential sites: low, moderate, and high. According to the regional geographic distribution, the largest portion of the area (65%) fell within the moderately significant groundwater potential geographical area. The high and low GPSs, which both have a low curvature and a valley plain characteristic, account for 25% and 10%, respectively, of the entire area. The outcomes were contrasted with the yield of groundwater from boreholes gathered in the study region. The validation analysis found an acceptable 88.89% similarity. This highlights the potential for the groundwater map's significant prediction. Therefore, the applied approach is a viable choice for the advancement of groundwater in the central Edo region and with comparable geology all over the globe.

Groundwater Resource Assessment by Applying Long-Term Trend Analysis of Spring Discharge, Water Level, and Hydroclimatic Parameters

Groundwater Resource Assessment by Applying Long-Term Trend Analysis of Spring Discharge, Water Level, and Hydroclimatic Parameters

Unraveling aquifer dynamics: Time series evaluation for informed groundwater management

Optimizing groundwater monitoring networks is essential for the sustainable management of this critical resource, which is vulnerable to both anthropogenic and natural changes. Focusing on the Hashtgerd aquifer in Iran, this research aims to enhance the efficiency of groundwater level monitoring systems by integrating statistical methods, artificial intelligence (AI), and hydrogeological studies. The study's core objective is to pinpoint and comprehend the factors that cause certain observation wells to become isolated from the main aquifer body, leading to parts of the aquifer being cut off from the central hydrogeological system. Employing clustering techniques on data from 29 observation wells, based on parameters like groundwater level (GWL), groundwater level change (GWLC), groundwater depth (GWD), air temperature (AT), distance (D), ground level (GL), and temperature (T) through Hierarchical Cluster Analysis (HCA), the research categorizes the groundwater information into four distinct groups. Significantly, Cluster 4 encompasses wells with similar characteristics. The analysis reveals that two wells, categorized in Clusters 1 and 2, exhibit unique behaviors, likely due to their connection to local karstic aquifers, as corroborated by geological evidence. Cluster 3's separation is determined through geophysical investigations, identifying confined conditions near specific wells, notably Wells 4 and 8. AI techniques further distinguish differences in meteorological influences and water discharges among the clusters, noting minimal weather impact on Clusters 1 and 2, considerable shallow groundwater influence on Cluster 3, and a pronounced effect of groundwater discharge on Cluster 4. This nuanced identification of aquifer segments disconnected from the main system underscores the need to refine monitoring networks for accurate assessment of groundwater resources. By leveraging a detailed analysis of aquifer complexities through state-of-the-art methodologies, the study guides towards informed, sustainable groundwater management strategies.

Optimizing urban water sustainability: Integrating deep learning, genetic algorithm, and CMIP6 GCM for groundwater potential zone prediction within a social-ecological-technological framework

This study presents a novel approach to groundwater resource assessment and drought vulnerability, achieved through integrating cutting-edge deep-learning algorithms into a comprehensive framework. In regions susceptible to drought, ensuring groundwater availability is of paramount importance. Addressing this critical need, our research employs an ensemble of advanced algorithms, including long short-term memory, convolutional neural network, deep neural network, and recurrent neural network. These algorithms are further enhanced through optimization using a genetic algorithm to map groundwater potential zones (GWPZ). Leveraging model validation based on the area under the receiver operating characteristic curve (AUCROC), the long short-term memory-genetic algorithm model emerges as the superior algorithm, boasting the highest values of AUCROC: 0.995 for training and 0.996 for testing). Utilizing this optimized model, this study developed the GWPZ map, subsequently overlaying it with established drought maps developed by the Bangladesh Agricultural Research Council across distinct periods—Pre-Kharif, Kharif, and Rabi. The derived baseline GWPZ spatial distribution revealed five categories—very low (34.99%), low (27.67%), moderate (13.26%), high (11.71%), and very high (12.37%)—and their intersection with drought-prone regions, indicative of probabilities of drought occurrence ranging from very severe to low (15.14% to 24.69%). Moreover, employing the best-predicted model, this study projected future GWPZ for 2050 and 2100 using the coupled model intercomparison project 6 general circulation model data under the ambit of three distinct shared socioeconomic pathways (SSPs): SSP1-2.6, SSP2-4.5, and SSP5-8.5. Our findings suggested a contraction in the groundwater potential area by the end of the twenty-first century (2100). This pioneering integration sheds light on the intricate relationship between groundwater availability and drought susceptibility, furnishing invaluable insights for formulating targeted water resource management strategies. By amalgamating advanced computational techniques with geospatial analyses, this research contributes to a more comprehensive grasp of water resource dynamics within the context of escalating climate challenges. Consequently, it offers a foundation for informed decision-making and implementing sustainable water management practices in regions grappling with the dual challenges of groundwater scarcity and recurrent droughts.

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.

Evaluating groundwater resources trends through multiple conceptual models and GRACE satellite data.

In this research, three numerical groundwater flow models, developed and calibrated from three equally plausible conceptual models over the Nasia Basin, have been used to assess groundwater resources variations over a transient period. The use of multiple numerical models reduces the effect of uncertainties in conceptual model formulation. All the three calibrated numerical models indicate an increasing trend of groundwater recharge and storage over the period of the groundwater level monitoring. This suggests that the prevailing erratic climatic conditions in the area are conducive for increasing groundwater recharge and storage in the terrain. The high-intensity, short duration rainfall patterns, attending climate change in the basin, enhance high levels of infiltration and percolation, leading to steadily increasing groundwater recharge. Groundwater recharge estimates from each of the models over the transient period appear to reflect the pattern of seasonal variations in rainfall in the region. Data from the models indicates a significant role of baseflow in sustaining perennial streamflow in the area. This presents a significant development in terms of groundwater-based adaptation projects, especially in agriculture. The trend of groundwater recharge in the Nasia Basin is in sync with regional groundwater storage variations estimated from the Gravity Recovery and Climate Experiment (GRACE) satellite data collected and processed over the Volta Basin. At the Volta Basin level, groundwater storage variations indicate a strong positive trend of increasing groundwater recharge from 2002 (beginning of the GRACE mission) to 2022 (end point of the data used for this research). Analysis of the GRACE data suggests that there is a cumulative increase in groundwater storage by 30cm, representing approximately 120 km3 of groundwater over the period in the basin. This translates into approximately 15mm/year of groundwater storage increase. Thus, at both the regional and local levels, groundwater appears to be responding positively to the impacts of erratic rainfall patterns observed in the area recently. The high-intensity, short duration rainfall patterns appear to favor significant groundwater recharge, resulting in a strong positive groundwater storage signal. The high positive groundwater storage signal suggests increasing groundwater resources potential in the area, indicating promising opportunities for groundwater-based climate change adaptation interventions.

Assessing Groundwater Resources Vulnerability with the New DRASTIC-LP: A Case Study in Chiang Mai Province, Thailand

Groundwater vulnerability has gained widespread attention, particularly in Chiang Mai Province, one of Thailand’s fastest-growing cities, which is experiencing rapid development in both economic and social sectors. The expansion of urban communities and the industrial, tourism, and agriculture sectors has resulted in the overutilization of available resources, notably water resources. This overuse, coupled with the adoption of modern technology to boost productivity and meet market demands, has led to an increased reliance on groundwater to supplement surface water sources, providing benefits across all sectors. However, the economic and social growth plays a pivotal role in shaping the diversity of land use, encompassing residential, commercial, industrial, and agricultural activities. These activities, in turn, directly contribute to environmental pollution, particularly in terms of the risk of groundwater contamination in Chiang Mai Province. This study aims to predict the future vulnerabilities of groundwater resources under an ensemble of climate change scenarios and changes in land-use patterns. Chiang Mai Province in northern Thailand is one of the fastest-growing cities and therefore is experiencing rapid urbanization, as well as land-use pattern changes, which was important for the case study. The new DRASTIC model, namely the DRASTIC-LP model, combined with GIS-based techniques and overlay techniques, was used to generate the map of groundwater resource vulnerabilities. A point pollution source (P)-related land-use pattern (L) that represents contamination impacts was considered an additional new DRASTIC parameter. The study’s findings reveal the high reliability and maximum effectiveness of the new DRASTIC-LP model in assessing groundwater vulnerability and contamination-risk areas under a climate change scenario (by MIROC-ESM-CHEM model under RCP.8.5 scenario) and land-use pattern changes (by CA_Markov Chian Model) for both the current year (2020) and the next 50-year period (2021–2070). Furthermore, the new DRASTIC-LP model is employed to trace the movement of pollutants from high- to very high-risk areas based on the groundwater vulnerability and contamination-risk maps. The results highlight that waste disposal dumping sites pose a more critical distribution and movement of pollutants when compared to industrial sites. Additionally, unconsolidated aquifers and cracked consolidated rock aquifers show a potentially higher occurrence of pollutant distribution and movement when compared to consolidated aquifers. Consequently, the study’s outcomes are applied to formulate guidelines for the management and control of groundwater resource contamination. These guidelines serve as valuable tools for decision makers, aiding in pollution prevention and the effective management of contamination risks in groundwater resources.

PRELIMINARY ASSESSMENT OF GROUNDWATER POTENTIAL ZONES IN AYEDE-EKITI, EKITI STATE, SOUTHWESTERN NIGERIA USING ELECTRICAL METHODS

An electrical resistivity survey was conducted at different locations within Ayede Ekiti, Ekiti State, Southwestern Nigeria. This is aimed at evaluating groundwater potential zones and aquifer properties within the study area. Data were acquired at four (4) different locations using the vertical electrical sounding method by employing the Schlumberger array configuration. This study utilizes resistivity data processed with IPI2win software to analyze the subsurface geology of a basement complex region. The result of the study showed that qualitatively, three major curve types (A, HA, and H) were observed. Varying curve types, predominantly A curves, were observed across the research area, with notable groundwater potential indicated by H and HA curve types at select points. Through curve matching techniques, three to four distinct geo-electric layers were identified, while the quantitative interpretation resulted in deducing layers with different resistivity and thickness values for each VES(s) of the study area. It was deduced that the VES 2 and 3 zones are connected to a high output of groundwater. However, VES 1 and 4 are characterized by low groundwater potential. This research contributes valuable insights for geological mapping and groundwater resource assessment in complex basement terrains of the study area.

Assessment of Groundwater Resources in the Hard Basement Rocks of Yemen, Al Bayda City Case Study

The present study was carried out with the aim of assessing the availability of groundwater in a typical hard basement complex area in Yemen, the catchment area of Al-Bayda city catchment in the mountainous region of the country. The main objective of this study is to evaluate and quantify the groundwater resources available in this area. The available regional data on climate, topography, and geology as well as ASTER and Landsat imaginary data were collected, reviewed, and analyzed together with local hydrogeological studies and well inventories and then used to identify the general geohydrology of the catchment, characterize the local aquifer system, assess the groundwater situation and quantify the available groundwater in the study area. The results of this study show that the aquifer system accounts for only about 5 % of the entire catchment area and has a total thickness of 10 - 30 m. This aquifer has a small lateral extent and is interrupted by faults, dykes, and plutonic intrusions. Limited amounts of groundwater occur in isolated bodies along the main wadis at the base of the aquifer system. The groundwater level in these bodies has dropped by 19 m since 1983, on average by 0.5 m per year. Groundwater recharge for the current state is estimated at 1.95 million m3/year, which is only 3% of the total annual precipitation. The total amount of groundwater withdrawn is estimated at 2.2 million m3/year, which is 80% below the current (2023) total annual demand of around 11 million m3. The study underlines the need to implement an urgent action plan to improve groundwater recharge through water extraction techniques. The research results can be used for the improvement and development of water resources in this area in particular and in the Yemen region in general.

Assessment of Under Ground Water Resources through RS and GIS Techniques

Water resource management is of utmost importance due to climate change and increased water demand. With 98% of the world’s freshwater being ground water, monitoring its distribution is crucial. These modern-day tools aid in assessing changing land use patterns, mapping water resources, and analyzing hydrogeological data. This paper explores the role of RS and GIS applications in exploring and assessing groundwater resources, estimating natural recharge distribution, analyzing hydrogeological data, and monitoring processes. The study area is situated in the Jodhpur district of Rajasthan, India, characterized by a dry region with sand dunes, alluvial areas, and scattered hillocks. To analyze groundwater potential zones, various spatial datasets such as rainfall, land use, land cover, soil type, and slope were prepared using topographic maps and satellite imagery through integrated RS and GIS techniques. The Soil and Water Assessment Tool (SWAT) integrated with ArcGIS software was used to estimate run off and groundwater recharge through water balance ratios. The research findings demonstrate that GIS and RS layers were analyzed, including factors such as watershed, land use, slope, soil type, temperature, solar radiation, and rainfall data. The water balance ratio was found to be a driving force impacting soil erosion, plant growth, and sediment movement. Overall, this study highlights the effectiveness of RS and GIS techniques for assessing groundwater recharge. The results obtained through these tools provide valuable insights for decision-makers in managing water resources and understanding the potential impacts of human activities on groundwater quality.

Quality index metrics with Bi-GRU-based water quality prediction

ABSTRACT The precise assessment of groundwater resources is significant in the face of growing water demands, environmental pollution, and degradation. However, traditional methods of water quality prediction are inadequate in dealing with large volumes of data or missing data. Machine learning-based prediction methods are being explored to address this issue. Currently, single-indicator techniques are commonly used, but they may not accurately forecast multimodal water quality or capture inter-indicator connections. In this study, a Bi-GRU is proposed to predicting water quality based on quality index data. To evaluate the performance of our proposed method, we utilize Kaggle Datasets, which provide a diverse range of water quality data. We compare our results with existing methods such as MLP, LSTM, and GRU, using metrics including accuracy, precision, recall, and f-score. Overall, our study demonstrates that the Bi-GRU model is highly effective in predicting water quality based on quality index data. The results of our experiments indicate that our proposed method surpasses traditional approaches like MLP, LSTM, and GRU in terms of accuracy, precision, recall, and f-score. These findings have significant implications for improving water quality monitoring and pollution prevention efforts, enabling better management of groundwater resources in the face of growing water demands and environmental challenges.

Geophysical Approach for Groundwater Resource Assessment: A Case Study of Oda Community Akure, Southwestern Nigeria

The geophysical investigation for groundwater was carried out at Oda town, Akure south local government area of Ondo State. Fourteen (14) points were sounded using a Schlumberger array with AB/2 of 80 m and the resulting geoelectric parameters were used in the estimation of the aquifer layer parameters of the subsurface. The first layer resistivity value ranges from 29 to 164 Ωm and thickness ranges from 0.6 to 2.5 m. The second layer has a resistivity value ranging from 21-1361 Ωm with a thickness ranging from 1.5 m to 14.6 m. The third layer resistivity value is from 68 to 297 Ωm with thickness ranging from 4 m to 12.4 m. The fourth layer which is the deepest layer has a resistivity value ranging from 180 to 4364 Ωm with depth ranging from 4 m to 19.5 m. The parameters interpreted from the geoelectric data were used to generate the aquifer thickness and resistivity maps, with bedrock relief which were combined to produce the groundwater potential map of the area. These maps were used to characterise the study area into low to high groundwater potential zones. The southwestern and eastern parts were identified as productive groundwater potential zones. The result was validated by taking water column depth from eight existing hand-dug wells. A significant correlation was obtained between the groundwater potential model and the well water column. The surveyed area is generally suitable for hand-dug well aside from the north-eastern part where groundwater potential is low and water volume is observed.

Multiple Conceptual Model Approach for Assessing Groundwater Resources Sustainability Under Multiple Stresses

Multiple Conceptual Model Approach for Assessing Groundwater Resources Sustainability Under Multiple Stresses

Water mixing processes in a complex multi-layer hydrosystem in southwestern Madagascar: a combined isotopic and piezometry approach

Water transfers through a multilayered aquifer system are difficult to characterize. This study explores whether the conceptual model of water mixing at depth can be extrapolated over a hydrosystem extended across several tens of kilometers and including multiple aquifer layers. The processes are investigated using a combination of isotope tracers and piezometric monitoring over 10 years. The goal of this approach is to better understand how water transfer occurs throughout a complex and poorly documented hydrosystem of the Mahafaly Plateau in southwestern Madagascar. The results show a clear smoothing of isotopic variability with depth, associated with a smoothing of the recharge peaks. Isotopic values are strongly variable in the near surface (from -6.8 to -2.5‰ 18O) and stabilize at a critical depth (near 20 m) at around -4.7‰ 18O. These results indicate high vertical flows through the aquifer system, where there is neither obvious dominant recharge via preferential pathways nor lateral mixing. Such a strong smoothing effect on groundwater isotopic variability with depth has been rarely observed so clearly over a large spatial scale. These results provide information on a remote groundwater flow system at a scale pertinent to groundwater resource assessment. The results also indicate that the Neogene aquifers of the Mahafaly Plateau are poorly connected with other water resources (rivers, old sedimentary formations) except for the percolation of water towards the deep Eocene karst. This means that groundwater resources in the Ankazomanga Basin are limited and that it is essential to understand and quantify recharge for sustainable groundwater management.

Assessment of groundwater aquifer using geophysical and remote sensing data on the area of Central Sinai, Egypt

The study aims to assess groundwater resources in Sinai's central area using remote sensing, geoelectric, and well-logging data, utilising techniques for modelling hydrogeological frameworks and evaluating desert regions' groundwater potential. Its utilized satellite image sources, soil maps, and geological maps to map the effects of various factors on groundwater potentiality recharge, dividing it into five zones. Eighteen deep VES stations were used to examine the upper part of the groundwater aquifer in Central Sinai, Egypt, comparing it with available borehole information (Well-1, and JICA-1) to establish subsurface geology and hydro-geology positioning. Borehole data, VES interpretation results, hydro-geophysical maps, and four geoelectrical cross-sections were used to visualize the rearward expansion of eight lithological units, groundwater-bearing sections, and aquifer-filled thicknesses. From interpretation data output reveal three zones with significant recharge and storage potential, including two groundwater aquifers. The shallow aquifer has a saturation thickness of the fractured limestone of 35–250 m, while the deep aquifer Nubian sandstone is detected at depths ranging from 660–1030 m. NW–SE and NE–SW faults likely recharge conduits connecting shallow and deep aquifers, providing sites with acceptable groundwater potential for living, agriculture, and development in Sinai.

Recharge and vulnerability assessment of groundwater resources in North west India: Insights from isotope-geospatial modelling approach

Recent studies indicate dwindling groundwater quantity and quality of the largest regional aquifer system in North West India, raising concern over freshwater availability to about 182 million population residing in this region. Widespread agricultural activities have resulted severe groundwater pollution in this area, demanding a systematic vulnerability assessment for proactive measures. Conventional vulnerability assessment models encounter drawbacks due to subjectivity, complexity, data-prerequisites, and spatial–temporal constraints. This study incorporates isotopic information into a weighted-overlay framework to overcome the above-mentioned limitations and proposes a novel vulnerability assessment model. The isotope methodology provides crucial insights on groundwater recharge mechanisms (18O and 2H) and dynamics (3H) - often ignored in vulnerability assessment. Isotopic characterisation of precipitation helped in establishing Local Meteoric Water Line (LMWL) as well as inferring contrasting recharge mechanisms operating in different aquifers. Shallow aquifer (depth < 60 m) showed significant evaporative signature with evaporation loss accounting up to 18.04% based on Rayleigh distillation equations. Inter-aquifer connections were apparent from Kernel Density Estimate (KDE) and isotope correlations. A weighted overlay isotope-geospatial model was developed combining 18O, 3H, aquifer permeability, and water level data. The central and northern parts of study area fall under least (0.29%) and extremely (1.79%) vulnerable zones respectively, while majority of the study area fall under moderate (42.71%) and highly vulnerable zones (55.20%). Model validation was performed using groundwater NO3– concentration, which showed an overall accuracy up to 82%. Monte Carlo Simulation (MCS) was performed for sensitivity analysis and permeability was found to be the most sensitive input parameter, followed by 3H, 18O, and water level. Comparing the vulnerability map with Land Use Land Cover (LULC) and population density maps helped in precisely identifying the high-risk sites, warranting a prompt attention. The model developed in this study integrates isotopic information with vulnerability assessment and resulted in model output with good accuracy, scientific basis, and widespread relevance, which highlights its crucial role in formulating proactive water resource management plans, especially in less explored data-scarce locations.

Hydrogeochemical evolution and water quality assessment of surface and groundwater resources: case study, Shirin Darreh dam basin (NE Iran)

Hydrogeochemical evolution and water quality assessment of surface and groundwater resources: case study, Shirin Darreh dam basin (NE Iran)

Geospatial assessment and hydrogeochemical characterization of groundwater resources of Manipur Valley, India.

Spatio-temporal hydrogeochemical assessments of groundwater resources were carried out for the valley region of Manipur in India to investigate its quality status and suitability criteria for irrigational and potable uses. The assessment was carried out for 140 spatially distributed groundwater samples collected during 2018-2021 for pre- and post-monsoon seasons. To assess and comprehend the hydrogeochemical behavior of underlying aquifers and controlling factors for groundwater quality in the region, groundwater quality indices and statistical tools were used. Assessment of in situ parameters such as pH, TDS, salinity, and EC under field conditions reveal that the values exceed concentration in many locations. Most of the samples fall in the category of hard to very hard water. Moreover, observation of both positive and negative oxidation-reduction potential (ORP) and low dissolved oxygen (DO) concentration in groundwater samples indicates aquifers are of recent unstable geologic formations. Similarly, elevated concentrations of F-, Cl-, HCO3-, and Fe3+ greater than the prescribed standards of the World Health Organization in many samples indicate the unsuitability of the groundwater sources for potable uses. The geochemical interactions were found to be dominated and controlled by the rock-weathering geochemical process that contributes to HCO3--Ca2+-type water, followed by HCO3--Na+, Cl--Ca2+, and mixed-type water in both seasons. Spatio-temporal geospatial vulnerable groundwater zone mapping using interpolation techniques carried out in the ArcGIS platform identifies the aquifers based on the water quality and pollution indices. The study's significant findings can provide baseline information that can supplement the government's planning and management initiatives to deal with the current water security challenges in the region as groundwater uses are increasing due to various hydroclimatic phenomena in the state.