Groundwater Electrical Conductivity Research Articles

Storm Surges and Sea Level Rise Cluster Hydrological Variables Across a Coastal Forest Bordering a Salt Marsh

AbstractSea level rise and storm surges drive coastal forest retreat and salt marsh expansion. Both salinization and flooding control ecological zonation and ecosystem transition in coastal areas. Hydrological variables, if coupled with ecological surveys, can explain the different stages of coastal forest retreat and marsh encroachment. In this research, long‐term data of a host of hydrological variables collected along transects from marsh to inner forest were analyzed. Linear discriminant analysis (LDA) was used to identify the primary hydrological variables responsible for the forest‐marsh gradient and their seasonal patterns. Water content (WC) in the soil (WC) and groundwater electrical conductivity (EC) were found to be the main variables responsible for the hydrological differences among the sites. Higher values of WC and EC were found in the low‐forest area near the salt marsh, with hydrological differences between forest levels reflected in ecological community structure. In particular, some sites were characterized by high EC while others by high WC values, suggesting significant spatial variations within hundreds of meters. The forested area, relatively flat in elevation, was characterized by limited hydraulic gradients and consequently lateral discharges. These characteristics made the role of groundwater level negligible in driving the hydrological clustering. Seasonal LDA data suggest that the sites are hydrologically different during winter (higher distance among clusters of variables) and similar during summer (low distance among clusters). In the study area, higher rainfall occurs during summer, decreasing groundwater EC in areas characterized by low canopy cover (dying forest). Rainfall moved low forest sites closer to the pristine high forest in the LDA analysis. During storm surge events, the distance between clusters decreased, indicating uniform salinization and flooding across the forest. Therefore, we conclude that ecological zonation in a coastal forest is reflected in seasonal hydrological differences in the absence of storm surges. Storm surges do not produce contrasting hydrological conditions and might not be responsible for ecological differences in the short‐term. On the contrary, differences in hydrological recovery are responsible for forest zonation. An additional analysis carried out using a binary Marsh‐Healthy forest LDA classifier indicates when each site switches from a forest hydrological state to a salt‐marsh hydrological state. Our results are useful for long‐term predictions of the ecological evolution of the forest–salt marsh ecotone.

GIS-statically-based modelling the groundwater quality assessment coupled with soil and terrain attributes data.

In this study, we investigated the application of Geographic Information Systems (GIS) for groundwater quality assessment through the integration of statistical models with soil and topographical data. Our primary objectives were to identify soil parameters and topographical attributes contributing to groundwater quality assessment and to evaluate the potential of geostatistics and GIS for spatial analysis of groundwater resources. Groundwater samples were collected from 43 agricultural wells, and surface soil layer samples (0-20 cm) were obtained near each well. We measured groundwater quality parameters and relevant soil properties. Our approach involved the utilization of multiple linear regression (MLR) and principal component regression (PCR), combined with topographical terrain attributes and soil data, for modeling groundwater electrical conductivity (GEC). Our findings revealed significant correlations between GEC and soil electrical conductivity (EC) (r = 0.89) as well as soil carbonate (CaCO3) (r = 0.68). Among the ten topographical attributes considered, the terrain wetness index (TWI) exerted the highest influence on GEC (r = 0.57), followed by the slope (r = -0.47). Further analysis demonstrated that the MLR model outperformed the PCR model in both the development and calibration datasets, with an achieved R2value of 0.89 and a root mean square error (RMSE)of 150 μScm-1 for MLR, compared to an R2 of 0.85 and an RMSE of 170 μScm-1 for PCR when coupled with soil and attribute data for GEC prediction. The resulting GEC map generated from the MLR model displayed spatial variations, ranging from 605 μScm-1 in the northern region to 1275 μScm-1 in the central part of the study site. In conclusion, our study demonstrated the effectiveness of combining statistical modeling with geostatistics and GIS for groundwater quality assessment, providing valuable insights for resource management and environmental planning.

Elevated arsenic concentrations in groundwater of the Upper Indus Plain of Pakistan across a range of redox conditions

Groundwater of the Ravi River floodplain is particularly elevated in arsenic (As) on both sides of the Pakistan-India border. To understand this pattern, 14 sites were drilled to 12–30 m depth across floodplains and doabs of Pakistan after testing over 20,000 wells. Drill cuttings were collected at 1.5 m intervals, 132 of which were sand overlain by 77 intervals of clay and/or silt. Radiocarbon dating of clay indicates deposition of the aquifer sands tapped by wells 20–30 kyr ago. Most (85 %) of the sand samples were gray in color, indicating partial reduction to Fe(II) oxides, whereas most (92 %) of the clay and/or silt samples were orange. Associations between groundwater electrical conductivity, dissolved Fe, sulfate, and nitrate suggest that wells can be elevated (>10 μg/L) in As in the region due to either reductive dissolution of Fe oxides, evaporative concentration, or alkali desorption. In the Ravi floodplain, 47 % of 6445 wells tested contain >10 μg/L As compared to only 9 % of 14,165 tested wells in other floodplains and doabs. The As content of aquifer sands in the Ravi floodplain of Pakistan averages 4 ± 4 mg/kg (n = 66) and is higher than the average of 2 ± 2 mg/kg (n = 51) for aquifer sands outside the Ravi. Synchrotron spectroscopy and column-based speciation indicate predominance of As(V) over As(III) in both aquifer sands and groundwater. Whereas multiple processes may be responsible for elevated levels of As in groundwater across the region, spatial heterogeneity in groundwater As concentrations in the Ravi floodplain seems linked to variations in As concentrations in aquifer sands. Regulation by the solid phase may limit variations in groundwater As over time in response to natural and human-induced changes in hydrology. This means spatial heterogeneity could be taken advantage of to lower the exposure across the region with more testing and targeted drilling.

Evaluation of geophysical parameters for groundwater productivity in the Commune of Jesse, Delta State Nigeria


 Aquifer geohydraulic properties in Jesse were studied by the application of vertical electrical sounding (VES) and pumping test. The VES data was collected using ABEM SAS 1000 Terrameter and results processed using manual matching of curves and computer iteration, and interpreted in the forms of geologic models and geoelectric layers. The aquiferous unit was found at a depth of 5.30 m to 30.30 m with a range of resistivity from 87.30 Ω-m to 1945.40 Ω-m. Dar-Zarrouk parameters estimation and results of borehole pumping test produced the aquifer hydraulic parameters. The hydraulic conductivity values range from8.171 - 8.279 m/day, transmissivity from 43.8770 - 517.7480 m2/day, and formation factor from 0.369 - 8.501. These results are indications of a prolific and high yielding aquifer capable of withstanding long duration of pumping. Results of groundwater electrical conductivity and total dissolved solids ranged from 11.65 - 198.80 μs/cm, and 29.40 - 35.40 mg/dm3 respectively, signifying very moderate mineralization of the groundwater. The average electrical conductivity of 68.04 μS /cm and total dissolved solids of 32.58 mg/L were lesser than the acceptable limits of the Nigerian Standard for Drinking Water Quality (NSDWQ) of 1000 μS/cm and 500 mg/L respectively. This study affirmed that the aquifer in Jesse is suitable for groundwater exploitation for local and regional purposes.

The impact of weather conditions on the quality of groundwater in the area of a municipal waste landfill

Abstract The quality of groundwater in the source area of pollution depends on many factors, including the weather and hydrogeological conditions within the given area. Anassessment of water quality can be carried out based on data obtained from sensors placed in boreholes. This research examined the influence of air and water temperature, groundwater table position and precipitation on the value of electrical conductivity in groundwater in a selected piezometer belonging to the monitoring network of the Quaternary aquifer in the area of a waste landfill site in Tychy-Urbanowice in southern Poland. The influence of individual factors was checked by using twenty neural network architectures of a Multilayer Perceptron Model (MLP). Each of these indicated factors were selected as input variables. Ultimately, three neural networks were selected, which were characterized by the smallest validation and test errors and showed the highest learning quality. The significance of individual variables for the effectiveness of the model was checked using a global sensitivity analysis. Three selected MLP models contained seven to nine neurons in the hidden layer and used a linear or exponential function as the hidden and output activation. The maximum test quality was 0.8369, while the smallest test error was 0.0011. The results of the sensitivity analysis highlighted the important role of water temperature and water table position on the conductivity value. The obtained goodness of fit results of the models to the input data allowed us to conclude that the MLP was applicable to such forecasts and can be extended by the analysis of further factors.

Temporal and spatial estimation of groundwater electrical conductivity using soft computing approaches: Analysis of East Azerbaijan Province, Iran

Abstract The evaluation of groundwater quality plays an important role in the management of groundwater. The main objectives of the present work are to develop a novel soft computing framework including Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet-GEP (WGEP) for the temporal and spatial estimation of groundwater electrical conductivity (EC) in the East Azerbaijan province, Iran over 2001–2020. The results demonstrate the importance of wavelet transform application; the performance percentage enhancement of the WANFIS and WGEP models compared to the ANFIS and GEP, using the RMSE criterion, ranged from 15.48 to 51.09% and from 5.06 to 86.95%, respectively. All the developed models showed the WGEP superior compared to others. The impact of land use characteristics, climatic conditions, and geological features on groundwater quality showed that there is a direct relationship between the extent of agricultural land, semi-arid climate conditions and groundwater EC amounts. The results demonstrated that the values of EC increase from east to west, indicating the direct exchange of surface and groundwater in the study area. Moreover, groundwater quality changes significantly across the width of the fault, with groundwater EC in the northern part of the fault higher than that in the southern part.

Effects of the 2021 La Palma volcanic eruption on groundwater resources (part I): Hydraulic impacts

The 2021 volcanic eruption in the Cumbre Vieja mountain range on La Palma Island (Canary Islands, Spain) raised concerns regarding the potential impact on groundwater resources. This study is the first part of a series of papers investigating those impacts, and focuses on the hydraulic impacts of the eruption, while subsequent papers will explore the geochemical consequences. Three boreholes equipped with sensors to measure hydraulic head, temperature, and electrical conductivity of groundwater were installed near the volcano. Monitoring started during the eruption and continued a year after it. Statistical analysis were performed to assess the relationship between the measured variables and real-time seismic-amplitude measurements (RSAM). In addition, the possibility of groundwater vaporization due to magma emergence was assessed with a groundwater flow numerical modelling of the island. Correlation coefficients were computed to assess the linear relationship between groundwater parameters and seismic signals, observing a statistically significant association, and suggesting near-instantaneous variations in parameters such as groundwater levels and EC. Different response patterns of groundwater levels were observed in recharging areas in highlands compared to discharge areas, showing an opposite correlation direction. Deduction of natural trends from the linear regression models of head and RSAM two months after the eruption revealed a more predictable impact on the groundwater system, as the hydrogeological system adjusts to the volcanic activity and its effects on the aquifer. The hydrogeological simulation of the "magma pumping effect" suggested that groundwater extraction was possible, but the absence of an appropriate groundwater monitoring network made it impossible to determine the amount of water extracted from the aquifer. The uncertainty analysis showed values up to 2000 m3·day−1. These findings have important implications for understanding the negative impacts of volcanic eruptions on groundwater resources, highlighting the need for regular monitoring and assessment by hydrogeologists and water management professionals.

Spatial or Random Cross-Validation? The Effect of Resampling Methods in Predicting Groundwater Salinity with Machine Learning in Mediterranean Region

Machine learning (ML) algorithms are extensively used with outstanding prediction accuracy. However, in some cases, their overfitting capabilities, along with inadvertent biases, might produce overly optimistic results. Spatial data are a special kind of data that could introduce biases to ML due to their intrinsic spatial autocorrelation. To address this issue, a special resampling method has emerged called spatial cross-validation (SCV). The purpose of this study was to evaluate the performance of SCV compared with conventional random cross-validation (CCV) used in most ML studies. Multiple ML models were created with CCV and SCV to predict groundwater electrical conductivity (EC) with data (A) from Rhodope, Greece, in the summer of 2020; (B) from the same area but at a different time (summer 2019); and (C) from a new area (the Salento peninsula, Italy). The results showed that the SCV provides ML models with superior generalization capabilities and, hence, better prediction results in new unknown data. The SCV seems to be able to capture the spatial patterns in the data while also reducing the over-optimism bias that is often associated with CCV methods. Based on the results, SCV could be applied with ML in studies that use spatial data.

The Spatio-Temporal Dynamic Patterns of Shallow Groundwater Level and Salinity: The Yellow River Delta, China

Shallow groundwater in coastal aquifers is a highly dynamic and complex system with a high risk of seawater intrusion. Analyzing the spatio-temporal dynamic patterns of groundwater can help to manage the groundwater resource and prevent it from degradation. Based on the groundwater level (GWL) and electrical conductivity (EC) monitoring data of 18 observation wells in the Yellow River Delta (YRD) from 2004 to 2010, this research analyses the groundwater dynamics using a robust seasonal trend decomposition technique (STL) and spatial interpolation method to detect the groundwater spatio-temporal dynamic patterns of groundwater level and salinity. Combined with hydro-climatic data, the Pearson correlation method and the Mann-Kendall (MK) trend analysis were used to further reveal the impacts that induce their trends and seasonal variations. Our analyses show that the risk of seawater intrusion into local shallow aquifers in this region is high, with the mean groundwater level over 42% of the region lower than the local sea level, and the mean groundwater EC over 96% of the region met the standards for seawater intrusion. In addition, the trends of groundwater level generally declined by 0.01~0.45 m/a and salinity increased by 1.153~25.608 μs/cm.a, which are consistent with the trend of precipitation decline. The seasonal dynamics of groundwater level and salinity are highly correlated with the seasonal components of rainfall and evaporation. It can be concluded that the extent of seawater intrusion will increase in the future with sea level rise. The approaches used in this study proved to be effective and can certainly serve as an example for the analysis of the spatio-temporal dynamics of groundwater in other coastal regions.

A Numerical Approach to Evaluating Groundwater Vulnerability to Seawater Intrusion on Jeju Volcanic Island, South Korea

Seawater intrusion (SWI) is a critical issue for coastal aquifers, especially in islands where groundwater is the sole source of water supply. The objective of this study was to develop a straightforward approach to evaluating groundwater vulnerability to SWI, using a statistical method with spatial analyses applied to the four basins of Jeju volcanic island. In this study, five factors were parametrized, including hydraulic conductivity, groundwater level, distance from shoreline to wells, well depth and groundwater use. These parameters were spatially interpolated and correlated with groundwater electrical conductivity as a proxy for groundwater salinization, resulting in three parameters with significant relations: groundwater use, well depth, and groundwater level. Then, a numerical model for the SWI vulnerability assessment was constructed using ratings and weights, and by evaluating the vulnerability as weak, moderate and high with a numerical index. Regional conditions, including major land-use types, industrial activities, population and the degree of urbanisation, could affect parameters differently at each region. Based on the percentage of area with a high vulnerability, regions of Jeju Island followed the order of eastern > northern > western > southern, indicating that preventive measures for SWI and its influencing parameters could be applied more effectively in certain regions.

Determination of the spatial correlation characteristics for selected groundwater pollutants using the geographically weighted regression model: A case study in Weinan, Northwest China

Groundwater pollution is a serious issue in arid and semi-arid regions. In this study, the ordinary least squares (OLS) regression and geographically weighted regression (GWR) model were used to assess the relationship between hydrochemical parameters (NO3-N, NO2-N, NH4-N, and F-) and explanatory variables related to anthropogenic and natural factors, including elevation, slope, population density, groundwater electrical conductivity, groundwater pH, and land use in the Weinan region of China. The results showed that NO3-N, NH4-N, and F- at 24, 4, and 54% of the samples exceeded the standard limits, respectively. Crop fields, grassland, and forest are the most common land use types in the study area, accounting for 62.84, 16.77, and 8.76%, respectively. The effects of explanatory variables on groundwater quality show strong spatial variation. Both positive and negative correlations were observed between groundwater nitrogen (NO2-N, NO3-N) and orchard, and between F- and crop field. The water area has significant impacts on NH4-N in Pucheng, Fuping and Linwei districts. The GWR model also suggested significant effects of water and orchard areas on groundwater NO2-N concentration in western Fuping County and eastern Dali County, which was neglected by the OLS model. The research shows advantages of the GWR model in capturing local variation.

A SUGGESTION FOR SUSTAINABLE GROUNDWATER USE ONINTENSIVE ARABLE AREA IN SEMIARID REGION

Topsoil and groundwater qualities were investigated by obtaining onsite measurements in the Alxa semiarid region in Inner Mongolia, north western China. The groundwater quality was found to be unacceptable for drinking as the concentration of groundwater nitrate (NO3–) was 66–250mg l–1, with an average value of 130 mg l–1; this value is 1.3–5.6 times higher than that specified by the regulatory drinking water standards recommended by the World Health Organization (50 mgNO3– l–1) or U.S. Environmental Protection Agency (44.26 mg NO3– l–1). The mean values of the groundwater electrical conductivity (EC) and nitrate NO3– in intensive agricultural areas were, respectively, 2.9 and 3.2 times higher than those in non-intensive agricultural areas. In addition, the groundwater EC and NO3– of the intensive and non-intensive agricultural areas exhibited considerable differences (EC: p<0.001, NO3–: p<0.05, Student t-test). This study suggests a sustainable groundwater use (as irrigation water use) without nitrogen fertilization on intensive arable area in semiarid region. The recommended amount of irrigation water use can help improve the degradation of the topsoil and enhance the groundwater qualities while decreasing the surplus N content.

Arsenic and Salinity Affected Groundwater in South-Western Bangladesh: An Assessment of Managed Aquifer Recharge and Sub-Surface Arsenic Removal as Mitigation Techniques

Naturally occurring groundwater arsenic contamination is a major problem in Narail, Bangladesh. Analyses of 32 groundwater samples showed arsenic concentrations ranged 20.33–158.90 µg/L. Relatively deeper aquifer (>45 m depth) in addition to arsenic, is contaminated with substantial amount of salinity. Electrical conductivity (EC) ranged 1.01–7.90 mS/cm indicating the enormity of salinity. The groundwater is mainly Na–Cl to Na–HCO3 type, and is SO4 limited. This research demonstrates the effectiveness of two mitigation techniques (i) managed aquifer recharge (MAR) and (ii) sub-surface arsenic removal (SAR) for in-situ salinity and arsenic treatment of contaminated groundwater. A two-year long experiment revealed that the MAR system was effective in reducing both salinity and arsenic. Groundwater EC reduced 72–81% from an initial value of 3.4 mS/cm to less than 1 mS/cm. Arsenic concentration dropped below 50 µg/L from an initial concentration of 100 µg/L. The SAR system reduced arsenic concentration below 50 µg/L from an initial concentration of 100 µg/L. The system was capable of yielding 1500 liters of arsenic safe water when injected volume of oxygen saturated water was 2000 liters indicating 70–80% recovery. Both systems can provide 1000 liters of safe drinking water at a cost of $2.00.

Inferring fault hydrology using groundwater age tracers

Faults can act as barriers to cross-fault flow, conduits to along-fault flow or both. Groundwater age inferred from hydrochemical data is one of the few tools that can estimate both barrier and conduit behaviour of the fault and associated fault damage zone. Conduit behaviour has been previously inferred from the existence of young water in deep aquifers or old water in shallow aquifers. While the ability of groundwater age to estimate the barrier behaviour of faults has been illustrated theoretically, it has not yet been observed in the field. Here, conceptual models of hydraulic head and groundwater age perpendicular to and across faults are discussed in terms of the resistance to flow across the fault (fault width divided by cross-fault transmissivity), the resistance to flow around the fault (fault length divided by aquifer transmissivity) and the recharge regime. To examine these conceptual models, field measurements of hydraulic head, 14C, chloride and electrical conductivity in groundwater are presented along transects across three contrasting fault systems. Large cross-fault head and age gradients are apparent at two of the three sites. At one of these sites, where regional flow is mostly parallel to the fault, 14C age and chloride data are used to estimate the rate of cross-fault flow. At the third site, there may not be a barrier to cross-fault flow, as there is no clear increase in either head or age gradient. The studies illustrate the importance of high data density to characterise fault zones, particularly for groundwater age which will vary both with depth and horizontal location along a transect. Although our data does not indicate conduit behaviour, such behaviour has been suggested based on groundwater chemistry at other locations along the fault strike at two of the sites.

Influence of Ion Pairs and Activity on the Index of Irrigation Water Quality Relying on Some Modern Terms in Erbil Governorate. *

Influence of Ion Pairs and Activity on the Index of Irrigation Water Quality Relying on Some Modern Terms in Erbil Governorate. *

Foliar P-Fractions Allocation of Karelinia caspia and Tamarix ramosissima Are Driven by Soil and Groundwater Properties in a Hyper-Arid Desert Ecosystem.

The allocation patterns of foliar phosphorus (P) fractions across various vegetation types generally reflect the adaptability to P-impoverished environments. However, the allocation of foliar-P fractions within the desert herb Karelinia caspia (K. caspica) and shrub Tamarix ramosissima (T. ramosissima) in soils with different environment-P availability and the impact of soil and groundwater properties on foliar-P fractions allocation remain unclear. The foliar-P fractions (metabolites-P, nucleic acid-P, structural-P, and residual-P) of K. caspica and T. ramosissima and the properties of 0–60 cm deep soil under their canopy and groundwater were determined at four different environment-P sites. Results found that as environment-P availability decreased, both plants allocated the higher proportions of foliar-P to nucleic acid-P than to metabolites-P and structural-P. With the exception of residual-P, foliar-P fractions were markedly higher for K. caspica than T. ramosissima. Soil Olsen-P, NO3–-N, soil water content, electrical conductivity (EC), groundwater EC, and total dissolved solids (TDSs) played an important role in allocating foliar P-fractions for both K. caspica and T. ramosissima. Compared with K. caspica, the foliar-P fractions of T. ramosissima were more tightly bounded to groundwater than soil properties. Overall, these findings show how desert plants flexibility take advantage of the foliar-P in low environment-P availability and illustrate the foliar-P fractions allocation of desert plants is driven by soil and groundwater properties.

Examining Chemical Interaction of Stream and Aquifer Near a River Barrage Area by Factor Analysis

A statistical approach has been applied in order to evaluate chemical interaction between groundwater and river water by using electrical conductivity (EC) in the Changnyeong-Haman river barrage (CHRB), Korea. The EC values in groundwater have been decreased an average of 50 μS/cm and 160 μS/cm. According to factor analysis using the EC data, the EC in groundwater has been disturbed by the CHRB construction as well as dredging of the Nakdong River during the construction work and with time ealpse the groundwater quality became stabilized. It is also demonstrated that hydrologic environment has been changed due to the CHRB construction, comparing the 1st period and 4th period. Hence, a long-term monitoring is required in order to reveal the change of hydrologic environment that can adversely affect plant growth.

Application of Soft Computing in Predicting Groundwater Quality Parameters

Evaluating the quality of groundwater in a specific aquifer could be a costly and time-consuming procedure. An attempt was made in this research to predict various parameters of water quality called Fe, Cl, SO4, pH and total hardness (as CaCO3) by measuring properties of total dissolved solids (TDSs) and electrical conductivity (EC). This was reached by establishing relations between groundwater quality parameters, TDS and EC, using various machine learning (ML) models, such as linear regression (LR), tree regression (TR), Gaussian process regression (GPR), support vector machine (SVM), and ensembles of regression trees (ER). Data for these variables were gathered from five unrelated groundwater quality studies. The findings showed that the TR, GPR, and ER models have satisfactory performance compared to that of LR and SVM with respect to different assessment criteria. The ER model attained higher accuracy in terms of R2 in TDS 0.92, Fe 0.89, Cl 0.86, CaCO3 0.87, SO4 0.87, and pH 0.86, while the GPR model attained an EC 0.98 compared to all developed models. Moreover, comparisons among the different developed models were performed using accuracy improvement (AI), improvement in RMSE (PRMSE), and improvement in PMAE to determine a higher accuracy model for predicting target properties. Generally, the comparison of several data-driven regression methods indicated that the boosted ensemble of the regression tree model offered better accuracy in predicting water quality parameters. Sensitivity analysis of each parameter illustrates that CaCO3 is most influential in determining TDS and EC. These results could have a significant impact on the future of groundwater quality assessments.

Distinguishing Mountain Front and Mountain Block Recharge in an Intermontane Basin of the Himalayan Region.

Intermontane basin aquifers worldwide, particularly in the Himalayan region, are recharged largely by the adjoining mountains. Recharge in these basins can occur either by water infiltrating from streams near mountain fronts (MFs) as mountain front recharge (MFR) or by sub-surface mountain block infiltration as mountain block recharge (MBR). MFR and MBR recharge are challenging to distinguish and are least quantified, considering the lack of extensive understanding of the hydrological processes in the mountains. This study used oxygen and hydrogen isotopes (δ18 O and δ2 H), electrical conductivity (EC) data, hydraulic head, and groundwater level data to differentiate MFR and MBR. Groundwater level data provide information about the groundwater-surface water interactions and groundwater flow directions, whereas isotopes and EC data are used to distinguish and quantify different recharge sources. The present methodology is tested in an intermontane basin of the Himalayan region. The results suggest that karst springs (KS) and deep groundwater (DGW) recharge are dominated by snowmelt (47% ± 10% and 46% ± 9%) as MBR from adjacent mountains, insignificantly affected by evaporation. The hydraulic head data and isotopes indicate Quaternary shallow groundwater (SGW) aquifer system recharge as MFR of local meteoric water with significant evaporation. The results indicate several flow paths in the aquifer system, a local flow for KS, intermediate flow for SGW, and regional flow for DGW. The findings will significantly impact water resource management in the area and provide vital baseline knowledge for sustainable groundwater management in other Himalayan intermontane basins.

Selecting wells for an optimal design of groundwater monitoring network based on monitoring priority map: A Kish Island case study

This paper presents a novel approach, i.e. a combination of gamma test and monitoring priority map, for optimal design of groundwater monitoring network (GMN) by considering the cumulative effects of industries, human activities, and natural factors on the groundwater quality. The proposed method was successfully applied to design an optimal network for groundwater salinity monitoring on Kish Island, Persian Gulf. The priority map of groundwater salinity monitoring was obtained based on the GALDIT index and two new factors including the average fluctuation of groundwater electrical conductivity (F) and distance from industries discharging saline effluents (P). The optimal number of monitoring wells was determined using a data analysis based on gamma test method. Then, a practical algorithm was presented to determine the optimal location of monitoring wells. Based on the results, the optimal number of monitoring wells is 110 and their location have an equitable distribution on the whole island.