Sustainable Management Of Groundwater Resources Research Articles

Status and codification of karst aquifer systems in Greece

Karst groundwater is an important natural resource for the water supply. The karst aquifer systems of Greece are developed within carbonate sedimentary (limestone, dolomite) and metamorphic rocks (marbles) and contribute significantly to water supply for domestic and irrigation use. They are discharged through springs: submarine, coastal brackish and inland freshwater springs. This review presents the general characteristics of karst aquifers focusing on hydraulic properties. Evaluation of the results shows that the hydraulic parameters of the karstic aquifer systems range within a large scale of values depending on karstification, tectonics and stratigraphy. High values of transmissivity and specific capacity are recorded in the upper stratigraphically levels of the karstic aquifer systems. In addition, a total of 229 different karst systems were classified according to five criteria: 1) Lithology, 2) Position, 3) Quality status, 4) Exploitation and quantitative status and 5) Discharge of springs. The majority (80%) of karst systems is developed in sedimentary rocks (limestones) and is of good water quality and quantitative status. Poor water quality status is recorded in coastal karst aquifers (mainly on islands) due to seawater intrusion phenomena. Finally, this work summarizes the characteristics of the karst aquifers in Greece in order to ensure the sustainable management of groundwater resources.

Evaluation of Hydrogeoethics approach for sustainable management of groundwater resources in the upper Kabul sub-basin, Afghanistan

Evaluation of Hydrogeoethics approach for sustainable management of groundwater resources in the upper Kabul sub-basin, Afghanistan

Evaluation of quantitative and qualitative sustainability of aquifers by groundwater footprint methodology: case study: West Azerbaijan Province, Iran.

In arid and semi-arid areas with limited surface water resources, the daily life and the growth of various economic sectors depend on the provision of services by the groundwater ecosystem. In many countries, groundwater is the main source of freshwater. In addition, groundwater plays an important role within the water cycle, and as such, it feeds and sustains groundwater-dependent ecosystems (such as rivers and wetlands). Therefore, sustainable management of groundwater resources is necessary. Groundwater footprint indicator is a useful tool for assessing the sustainable use of groundwater resources and its associated ecosystem services. In this paper, the sustainable use of groundwater in 28 main alluvial aquifer systems of West Azarbaijan province in the west of Lake Urmia-Northwest of Iran has been investigated by the groundwater footprint indicator. In order to assess the aquifers' water quality sustainability for agricultural use, the integrated groundwater footprint indicator developed by Kourgialas et al. (2018)-based on GIS spatial analysis-has been used. This assessment can help water sector managers in adopting policies and measures to the sustainable management of the alluvial aquifer system. Due to the availability of sufficient data for all aquifers in 2011, the calculations were performed only for this year. The results showed that no aquifer has a sustainable use of groundwater resources. Also, 16 aquifers do not have qualitative sustainability due to salinity pollution. The results of implementing three applicable policy options to improve the quantitative and qualitative sustainability of aquifers showed that it is possible to change quantitative sustainability all aquifers, but improving qualitative sustainability is possible only in three aquifers.

Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models

Developing accurate groundwater vulnerability maps is important for the sustainable management of groundwater resources. In this research, resampling methods [e.g., Bootstrap Aggregating (BA) and Disjoint Aggregating (DA)] are combined with machine learning (ML) models, namely eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Random Forest (RF), to improve the GALDIT groundwater vulnerability mapping framework that considers Groundwater occurrence (G) (i.e., aquifer type), Aquifer hydraulic conductivity (A), depth to groundwater Level (L), Distance from the seashore (D), Impact of existing seawater intrusion status (I), and aquifer Thickness (T). The proposed approach overcomes the subjectivity of the weights and ratings given to the six variables in the GALDIT framework (via the ML methods) and helps address the small dataset issue (via resampling methods) common to groundwater vulnerability predictive mapping. Considering the Shabestar Plain aquifer, situated in the northeast of Lake Urmia (Iran), the predicted vulnerability indices from GALDIT were adjusted using total dissolved solid (TDS, an indicator of drinking water quality) concentrations, and were modeled by the ML models. Pearson’s correlation coefficient (r) and distance correlation (DC) between the predicted vulnerability indices and TDS were used to validate the models. Using a validation set, the GALDIT framework (r = 0.447 and DC = 0.511) was compared against the best performing standalone (XGBoost-GALDIT, r = 0.613, DC = 0.647) and coupled resampling (BA-XGBoost-GALDIT, r = 0.659, DC = 0.699 and DA-RF-GALDIT, r = 0.616, DC = 0.662) ML models, revealing that the proposed framework significantly increases r and DC metrics. In general, the BA resampling method led to better performing ML models than DA. However, in all cases, it was found that integrating resampling methods and ML models are promising tools to improve the accuracy of GALDIT vulnerability models.

Efficacy of machine learning techniques in predicting groundwater fluctuations in agro-ecological zones of India

In the 21st century, groundwater depletion is posing a serious threat to humanity throughout the world, particularly in developing nations. India being the largest consumer of groundwater in the world, dwindling groundwater storage has emerged as a serious concern in recent years. Consequently, the judicious and efficient management of vital groundwater resources is one of the grand challenges in India. Groundwater modeling is a promising tool to develop sustainable management strategies for the efficient utilization of this treasured resource. This study demonstrates a pragmatic framework for predicting seasonal groundwater levels at a large scale using real-world data. Three relatively powerful Machine Learning (ML) techniques viz., ANFIS (Adaptive Neuro-Fuzzy Inference System), Deep Neural Network (DNN) and Support Vector Machine (SVM) were employed for predicting seasonal groundwater levels at the country scale using in situ groundwater-level and pertinent meteorological data of 1996–2016. ANFIS, DNN and SVM models were developed for 18 Agro-Ecological Zones (AEZs) of India and their efficacy was evaluated using suitable statistical and graphical indicators. The findings of this study revealed that the DNN model is the most proficient in predicting seasonal groundwater levels in most AEZs, followed by the ANFIS model. However, the prediction ability of the three models is ‘moderate’ to ‘very poor’ in 3 AEZs [‘Western Plain and Kutch Peninsula’ in Western India, and ‘Deccan Plateau (Arid)’ and ‘Eastern Ghats and Deccan Plateau’ in Southern India]. It is recommended that groundwater-monitoring network and data acquisition systems be strengthened in India in order to ensure efficient use of modeling techniques for the sustainable management of groundwater resources.

Hydrochemical Zoning and Chemical Evolution of the Deep Upper Jurassic Thermal Groundwater Reservoir Using Water Chemical and Environmental Isotope Data

A comprehensive hydrogeological understanding of the deep Upper Jurassic carbonate aquifer, which represents an important geothermal reservoir in the South German Molasse Basin (SGMB), is crucial for improved and sustainable groundwater resource management. Water chemical data and environmental isotope analyses of δD, δ18O and 87Sr/86Sr were obtained from groundwater of 24 deep Upper Jurassic geothermal wells and coupled with a few analyses of noble gases (3He/4He, 40Ar/36Ar) and noble gas infiltration temperatures. Hierarchical cluster analysis revealed three major water types and allowed a hydrochemical zoning of the SGMB, while exploratory factor analyses identified the hydrogeological processes affecting the water chemical composition of the thermal water. Water types 1 and 2 are of Na-[Ca]-HCO3-Cl type, lowly mineralised and have been recharged under meteoric cold climate conditions. Both water types show 87Sr/86Sr signatures, stable water isotopes values and calculated apparent mean residence times, which suggest minor water-rock interaction within a hydraulically active flow system of the Northeastern and Southeastern Central Molasse Basin. This thermal groundwater have been most likely subglacially recharged in the south of the SGMB in close proximity to the Bavarian Alps with a delineated northwards flow direction. Highly mineralised groundwater of water type 3 (Na-Cl-HCO3 and Na-Cl) occurs in the Eastern Central Molasse Basin. In contrast to water types 1 and 2, this water type shows substantial water-rock interaction with terrestrial sediments and increasing 40Ar/36Ar ratios, which may also imply a hydraulic exchange with fossil formation waters of overlying Tertiary sediments.

Groundwater sustainability challenges revealed by quantification of contaminated groundwater volume and aquifer depletion in hard rock aquifer systems

The lack of comprehensive studies involving the quantification of groundwater depletion and aquifer deterioration has challenged the sustainable management of groundwater resources in semi-arid regions. In this study, we formulated a new pragmatic relationship consisting of a set of equations to quantify contaminated groundwater volume and groundwater virtually lost through contamination. We also used extensive monitoring of the hydraulic head and its spatio-temporal variations as well as groundwater modeling to quantify total aquifer volume, aquifer storage, and aquifer depletion. The watershed is divided into three distinct zones based on groundwater level fluctuations and recharge (Intermediate Borewell Zone-IBW, Random Borewell Zone- RBW, and Slow Borewell Zone-SBW zones). This study provides an advanced understanding of the relationship between aquifer depletion, geogenic contamination and the resultant virtual groundwater loss. Our long-term study reveals a negative change in storage/groundwater loss in poor monsoon years and a reduction in total groundwater volume which can result in aquifer depletion over time and low groundwater reserves. The decadal estimations of groundwater gain/loss for a period of ten years (2001–2010) in the IBW zone of the aquifer show a groundwater gain of 3.03 mcm (million cubic metes) but at the same time, this zone has virtually lost 1.06 mcm to contamination. Similarly, 1.3 mcm gain is estimated in the RBW zone and 0.53 mcm loss of groundwater is estimated in the SBW. The virtual groundwater losses through contamination in RBW and SBW zones are 4.5 mcm and 16 mcm. In such a situation an aquifer depletion stage beyond an economic level will be reached in most of the watershed in a few years. The contamination in these years is also high which may result in virtual loss through contamination. Under such conditions, the groundwater resource may be conditionally renewable as a part of the groundwater volume remains contaminated and unusable. These results highlight the need to understand the interconnection between groundwater contamination and continued over-exploitation which gradually moves towards aquifer depletion and conditionally renewable groundwater resources.

Application of artificial intelligence in groundwater ecosystem protection: a case study of Semnan/Sorkheh plain, Iran

Groundwater ecosystems have unparalleled environmental value. Accurate modeling of groundwater level (GWL) fluctuations is a vital requirement for the protection of the groundwater ecosystems. The GWL modeling is a challenge due to complexities of the underground geological structure. Among the various modeling methods, artificial intelligence (AI)-based approaches serve as desirable alternatives due to their distinctive and potent properties. One of the most practical AI-based approaches is an artificial neural network (ANN) model. The purpose of the current study was to apply time delay neural networks (TDNN) with different network structures and input delays to model the GWL fluctuations. The variables used in the construction and validation of the models were average weekly GWL from January 2002 to January 2013 in two monitoring sites in Semnan/Sorkheh plain, Iran. The study area is an arid region, where overutilization of groundwater threatens the water security in this area. The computational results of the current research demonstrated that the TDNN model is a practical tool in modeling time-series GWL compared to the other state-of-the-art AI-based approaches. Future studies are recommended to explore application of proposed model for more sustainable and effective Groundwater Resources Management (GWRM).

Hydraulic tomography analysis of municipal-well operation data with geology-based groundwater models

The sustainable management of groundwater resources is essential to municipalities worldwide due to increasing water demand. Planning for the optimized use of groundwater resources requires reliable estimation of hydraulic parameters such as hydraulic conductivity (K) and specific storage (Ss). However, estimation of hydraulic parameters can be difficult with dedicated pumping tests while municipal wells are in operation. In this study, the K and Ss of a highly heterogeneous, multi-aquifer/aquitard system are estimated through the inverse modeling of water-level data from observation wells collected during municipal well operations. In particular, four different geological models are calibrated by coupling HydroGeoSphere (HGS) with the parameter estimation code PEST. The joint analysis of water-level records resulting from fluctuating pumping and injection operations amounts to a hydraulic tomography (HT) analysis. The four geological models are well calibrated and yield reliable estimates that are consistent with previously studies. Overall, this research reveals that: (1) the HT analysis of municipal well records is feasible and yields reliable K and Ss estimates for individual geological units where drawdown records are available; (2) these estimates are obtained at the scale of intended use, unlike small-scale estimates typically obtained through other characterization methods; (3) the HT analysis can be conducted using existing data, which leads to substantial cost savings; and (4) data collected during municipal well operations can be used in the development of new groundwater models or in the calibration of existing groundwater models, thus they are valuable and should be archived.

GIS-based assessment of groundwater quality index (DWQI and AWQI) in Tiruchendur Coastal City, Southern Tamil Nadu, India

The present study was conducted to evaluate the hydrochemical parameters to improve a water quality index for Tiruchendur, an agricultural area in the Tuticorin district of Tamil Nadu, India. The population growth and intensive agricultural activities in and around the groundwater system in the study area have a direct effect on the quality of this groundwater. A total of 39 samples were collected and analyzed for physicochemical parameters and assessed the suitability of groundwater for drinking and irrigation purposes based on the Water Quality Index (WQI) mapping by the Geographic Information System (GIS). The measured data were used to compute irrigation factors such as sodium percentage (Na%), sodium adsorption ratio (SAR), residual sodium carbonate (RSC) and permeability index (PI) to determine the suitability of water for agricultural activities. The hydro-chemical facies reflect that 57% of Ca2+-Mg2+-Cl−-SO42− type of facies with an alkaline (Ca2+ and Mg2+) nature exceeds the alkalis (Na+ and K+) and chloride (strong acid) exceed HCO3− (weak acid) with the sequence of cation Na+ > Mg2+ > Ca2+ > K+ and anion Cl− > HCO3− > SO42− > NO3− respectively. The Drinking Water Quality Index (DWQI) results show that no area is in excellent to good water class for drinking while 41% of samples have poor water quality, 20% of samples have very poor categories and 39% are of inappropriate for drinking utilities. The Agricultural Water Quality Index (AWQI) indicates that the vast majority (41%) areas can be used for agriculture with Moderate Restriction and 5% of estuary areas are classified as having low restriction over agricultural use. This comprehensive research provides a geological database through such spatial surveys as the DWQI and AWQI, which will help to plan and improve the sustainable management of groundwater resource along with the Tiruchendur coastal city.

Groundwater quality index as a hydrochemical tool for monitoring saltwater intrusion into coastal freshwater aquifer of Eastern Dahomey Basin, Southwestern Nigeria

Saltwater intrusion into coastal freshwater aquifers is a threat to groundwater quality globally. This study aims to determine the extent of saltwater intrusion into the coastal freshwater aquifer of the Eastern Dahomey Basin (EDB), Nigeria. Groundwater chemistry was sampled and analysed for ionic ratios and interpreted using a hydrochemical facie evolution diagram (HFE-D), the saltwater mixing index (SMI) and the Groundwater quality index for saltwater intrusion (GQIswi). High EC and TDS and the concentration of dissolved ions showed increased salinity as a result of seawater intrusion in wells located around communities in Seme, Lekki, Eleko, Okun-Ajah, Ode-Mahin and Igbokoda. Correlation of ions in the wet season also suggests higher salinities which originate partly from industrial and municipal effluents especially from wells which are close to river channels, while dry season groundwater shows the dominant influence of seawater intrusion. HFE-D revealed that mixed groundwater of Na + Ca–HCO3, Na–Cl and Ca–Cl dominate the area due to gravity-driven flow leading to groundwater freshening inland from the coastline towards the northern part of the basin. The groundwater quality index from SMI and GQIswi shows areas within 3 km from the coastline that are more sensitive to saltwater intrusion based on abstraction rate and depth of the wells. The present study provides information of value to planners and policy-makers for the sustainable management and protection of coastal groundwater resources in the Eastern Dahomey Basin.

Hydrogeochemical and Hydrodynamic Assessment of Tirnavos Basin, Central Greece

A combined hydrogeochemical and hydrodynamic characterization for the assessment of key aspects related to groundwater resources management was performed in a highly productive agricultural basin of the Thessaly region in central Greece. A complementary suite of tools and methods—including graphical processing, hydrogeochemical modeling, multivariate statistics and environmental isotopes—have been applied to a comprehensive dataset of physicochemical analyses and water level measurements. Results revealed that the initial hydrogeochemistry of groundwater was progressively impacted by secondary phenomena (e.g., ion exchange and redox reactions) which were clearly delineated into distinct zones according to data processing. The progressive evolution of groundwater was further verified by the variation of the saturation indices of critical minerals. In addition, the combined use of water level measurements delineated the major pathways of groundwater flow. Interestingly, the additional joint assessment of environmental isotopes revealed a new pathway from E–NE (which had never before been validated), thus highlighting the importance of the joint tools/methods application in complex scientific tasks. The application of multivariate statistics identified the dominant processes that control hydrogeochemistry and fit well with identified hydrodynamic mechanisms. These included (as dominant factor) the salinization impact due to the combined use of irrigation water return and evaporitic mineral leaching, as well as the impact of the geogenic calcareous substrate (mainly karstic calcareous formations and dolostones). Secondary factors, acting as processes (e.g., redox and ion exchange), were identified and found to be in line with initial assessment, thus validating the overall characterization. Finally, the outcomes may prove to be valuable in the progression toward sustainable groundwater resources management. The results have provided spatial and temporal information for significant parameters, sources, and processes—which, as a methodological approach, could be adopted in similar cases of other catchments.

Monitoring of land surface subsidence using persistent scatterer interferometry techniques and ground truth data in arid and semi-arid regions, the case of Remah, UAE

The United Arab Emirates (UAE) is located in an arid desert climate with very limited water resources and scarce rainfall. Along with the fast development of the country, the water demand for agriculture, industrial, and domestic purposes increased and led to diminishing groundwater resources. In this study, we explore the land surface deformations due to groundwater overexploitation in the agricultural area of Remah by analyzing Sentinel-1 data between 2015 and 2019 with the novel Parallelized-Persistent Scatterer Interferometry (P-PSI) technique. The detected land surface deformations have been correlated to the recorded groundwater levels at nearby water wells. This study detected land surface deformations in a form of an extensive subsidence bowl (with 28.5 km in diameter) with a maximum subsidence rate of 40 mm/year and a standard deviation within the bowl of less than 2 mm/year. The detected subsidence was associated with a 12 m drop in the water table level within the study area. The Persistent Scatterers with the highest deformations rate were spatially correlated with the depression cone of the groundwater level. These findings provide useful insights in understanding the groundwater regime of the area and have an important role in assessing regional hazards and driving mitigation measures towards managing uncontrolled groundwater overexploitation for sustainable management of groundwater resources.

Seasonal variation in groundwater quality and beneficial use for drinking, irrigation, and industrial purposes from Deccan Basaltic Region, Western India.

Sustainable management of groundwater resources requires detailed basin-wide water assessments. Semi-urbanized areas surrounding metropolitan cities in the western part of India were assessed for their suitability for domestic, irrigation, and industrial purposes. These study areas reflect rapid urban growth with residential complexes, combined with agricultural, horticultural, and industrial uses. Therefore, 68 representative groundwater samples were collected during the pre-monsoon (PRM) and post-monsoon (POM) seasons of 2015 and analyzed for major ions. According to the World Health Organization (WHO) drinking standards, parameters like EC, TDS, TH, HCO3, Ca, and Mg were found to exceed the desirable maximum limits, and the B and F content exceeded the permissible limits. The drinking suitability was studied using the modified water quality index (MWQI). The irrigation suitability was assessed using indices such as sodium adsorption ratio (SAR), percent sodium (%Na), and permeability index (PI). The industrial suitability was evaluated based on Langelier saturation index (LSI), saturation index (SI), Ryznar stability index (RSI), etc. MWQI results corroborate that 52.94 and 70% samples fall in no pollution category, and 47% and 30% samples were identified to be in the moderate category of pollution in the PRM season and POM season, respectively. The spatial variation maps of LSI, SI, RSI, Puckorius scaling index (PSI) and Larson-Skold index (LaI) show that the majority of the samples in the PRM season have low to insignificant scaling and corrosive potentials as compared to POM samples. The study results provide reliable information for water reserve managers to prepare the sustainable and more accurate basin management plans.

Groundwater vulnerability mapping using the modified DRASTIC model: the metaheuristic algorithm approach.

Vulnerability assessment and mapping is a significant tool for sustainable management of the precious natural groundwater resources. DRASTIC is an extensively used index model to map groundwater vulnerable zones. However, the original DRASTIC model rates and weights used in most of the research depict the poor correlation between nitrate concentration and groundwater vulnerability index. Wilcoxon test and five population-based metaheuristic (MH) algorithms, namely, firefly algorithm (FA), invasive weed optimization (IWO), teaching learning-based optimization (TLBO), shuffled frog leaping algorithm (SFLA), and particle swarm optimization (PSO), were used to optimize the rates and weights of the DRASTIC model to improve its accuracy. The performance of all the employed metaheuristic algorithms converges to a global optimal solution at different iterations, and to choose the best algorithm for DRASTIC weights optimization, a ranking methodology was proposed. The algorithms were ranked by calculating the relative closeness of alternatives with computational speed and the number of iterations as attributes in the TOPSIS method.This study identifies FA as the outperforming algorithm among the employed for this specified weight optimization problem based on ranking. The result of the optimization model proposed depicts significant improvement in the correlation coefficient between the groundwater vulnerability index and nitrate concentration from 0.0545 for the original DRASTIC model to 0.7247 for the Wilcoxon-MH- DRASTIC. Hence, this ranking approach can be adopted when global optimal solution is found by all employed algorithms in DRASTIC weight optimization.

Identification of Groundwater Potential Zones Using Remote Sensing and GIS Techniques: A Case Study of the Shatt Al-Arab Basin

Rapid population growth has raised the groundwater resources demand for socio-economic development in the Shatt Al-Arab basin. The sustainable management of groundwater resources requires precise quantitative evaluation, which can be achieved by applying scientific principles and modern techniques. An integrated concept has been used in the current study to identify the groundwater potential zones (GWPZs) in the Shatt Al-Arab basin using remote sensing (RS), geographic information system (GIS), and analytic hierarchy process (AHP). For this purpose, nine groundwater occurrence and movement controlling parameters (i.e., lithology, rainfall, geomorphology, slope, drainage density, soil, land use/land cover, distance to river, and lineament density) were prepared and transformed into raster data using ArcGIS software. These nine parameters (thematic layers) were allocated weights proportional to their importance. Furthermore, the hierarchical ranking was conducted using a pairwise comparison matrix of the AHP in order to estimate the final normalized weights of these layers. We used the overlay weighted sum technique to integrate the layers for the creation of the GWPZs map of the study area. The map has been categorized into five zones (viz., very good, good, moderate, poor, and very poor) representing 4, 51, 35, 9, and 1% of the study area, respectively. Finally, for assessing the effectiveness of the model, the GWPZs map was validated using depth to groundwater data for 99 wells distributed over the basin. The validation results confirm that the applied approach provides significantly solid results that can help in perspective planning and sustainable utilization of the groundwater resources in this water-stressed region.

Development and evaluation of the soil water balance model in an inland arid delta oasis: Implications for sustainable groundwater resource management

Sampling and testing were conducted on the groundwater level depth, soil water content, and vegetation coverage in the delta oasis of the WeiGan-KuChe River Basin, and a method for analyzing soil water balances for groundwater-dependent vegetation (GDV) was developed using the soil water balance model with the Richards equation. Three typical observation points (sites A, B, and C) were selected to understand the relationship between plant transpiration, soil water content changes, and groundwater level depth via HYDRUS-1D model simulations. The model calibration results showed that the simulated water contents at each site and in each soil layer fit well with the measured data and the comparison results of each sites are R2A = 0.81, R2B = 0.89, R2C = 0.78, in respectively. The soil water balance model of the three different sites demonstrated that vegetation transpiration was related to the groundwater level depth, vegetation cover, and plant age (P. euphratica). According to field observations within the WeiGan-KuChe River delta oasis, the groundwater level depth influenced the soil moisture and soil salinity, which are the most important environmental factors determining the distribution and changes in GDV. When the groundwater level depth was below 1 m or above 6 m, the plant species occurrence frequency decreased by 10%, and the degree of land desertification was severe. These simulations and field investigation results showed that the groundwater level threshold for salinity control was approximately 1 m, the ecological warning threshold varied by approximately 5 m, and the preferred groundwater level for GDV growth varied between 2 and 4 m. The findings of this study have important implications for groundwater resource management and ecological preservation in arid and semiarid areas.

Potential and net recharge assessment in paddy dominated Hirakud irrigation command of eastern India using water balance and geospatial approaches

Spatially distributed potential and net recharge rates were assessed in the paddy dominated Hirakud command area (Eastern India) at 100 m grid resolution using surface water balance and Water Table Fluctuation (WTF) methods, respectively, for the period 2001–05. Net recharge estimated using the WTF method corresponding to observation well locations was further interpolated using kriging technique available in the ArcGIS software. Net recharge to potential recharge ratios (%) were also assessed spatially. Water balance components (i) runoff was estimated using the Natural Resources Conservation Service-Curve Number (NRCS-CN) method (ii) reference evapotranspiration by (Hargreaves and Samani, Applied Engineering Agriculture ASABE 1:96–99, 1985)), crop evapotranspiration by (Allen et al., Crop evapotranspiration: Guidelines for computing crop water requirements, FAO Irrigation and Drainage, Food and Agriculture Organization, Rome, Italy, 1998) and evaporation from uncultivated lands by Ritchie (1972) approaches, and (iii) canal seepage using simple canal flow model. Annual groundwater draft during Kharif and Rabi was found to be 144.41 and 112.49 ha-m, respectively. Nearly, 90% of the study area contributed runoff in the range of 200–400 mm during the years 2002–03, 2003–04, and 2004–05. The estimated seepage losses vary between 5 and 15% of irrigation depth for all distributaries. Potential groundwater recharge during wet, normal, and dry years ranges between 650 and 1033 mm, and equivalent to 67%, 78%, and 60% of annual rainfall, respectively. Net recharge ranges between 8 and 11% of the annual rainfall. Mean ratio between net recharge to potential recharge is nearly 30%, indicating that nearly 70% of potential recharge is accounted as outflow from the study area. Parmanpur distributary canal located at the centre of the study area that exhibited higher potential recharge can be scheduled at the end to avoid water logging problem. Further, extraction of groundwater during non-monsoon period for irrigation purpose not only helps in controlling waterlogging but also helps in maintaining stable groundwater level. Overall, spatio-temporal distribution of recharge in the command area indicated that the irrigation demands during non-monsoon season can be met through sustainable management of underexploited groundwater resources. Such an integrated management of surface and groundwater can help in improving water use efficiencies as well as agricultural productivity.

Groundwater dating with dissolved organic radiocarbon: A promising approach in carbonate aquifers

A complete hydrogeological understanding of the deep Upper Jurassic carbonate aquifer in the South German Molasse Basin is essential for the future development of this important drinking water resource and geothermally used system.Water chemistry data, δ13CDIC, 14C of the dissolved inorganic carbon (14CDIC) and stable water isotope (δ18O and δD) measurements have been used to evaluate a promising groundwater dating approach with 14C of dissolved organic carbon (14CDOC). The pre-concentration of dissolved organic matter (DOM) was performed by the easy applicable solid phase extraction (SPE) with a styrene-divinylbenzene copolymer sorbent (PPL).Based on the sampling campaign of seven groundwater wells conducted between 2017 and 2019, it was shown that the groundwater is mainly of Ca–HCO3 type with some evidence of ion exchange between Ca2+ and Na+ at two of the investigated wells. The δD values ranged from −89.4‰ to −70.9‰ while δ18O values varied between −12.5‰ and −9.8‰. The obtained stable water isotope signatures indicated that the groundwater is of meteoric origin and was recharged during warm climate (Holocene), intermediate climate and cold climate (Pleistocene) infiltration conditions.The measured 14CDOC activities varied from 5.7 pmC to 51.1 pmC and the calculated piston-flow water ages (ORAs) ranged from 4200 years to 25,248 years using an initial 14C0DOC of 85 pmC. The calculated ORAs showed a very good correlation to the infiltration temperature-sensitive δ18O values which were affirmed with noble gas infiltration temperatures for two wells after Weise et al. (1991) and were also in good accordance with the atmospheric temperature record of the northern hemisphere from Dokken et al. (2015).The results reflect a consistent hydrogeological picture of the carbonate aquifer, which also supports the applicability of the SPE-PPL method for 14CDOC dating in groundwater with a low DOC content (<1 mg/l). In contrast, 14CDIC activities of 1.4 pmC to 21.3 pmC led to geochemically corrected piston-flow ages between 8057 years and >30,000 years and generally to an overestimation of the apparent water ages.This study gives insights into the promising approach of 14CDOC groundwater dating in carbonate aquifers with low DOC contents and allows future sustainable groundwater resource management of the investigated aquifer system.

A fuzzy geospatial approach for delineation of groundwater potential zones in Raipur district, India

Delineation of groundwater potential zones was carried out using fuzzy geospatial approach for the sustainable groundwater resource management in Raipur district, Chhattisgarh, India. In the present study, eleven groundwater controlling factors were selected and their respective fuzzy membership (FM) layers were overlaid with fuzzy GAMMA operator to develop the groundwater potential zone (GPZ) map. A total of 62.62% and 37.38% of the total area fell under low to moderate and high GPZ category, respectively. Sensitivity analysis highlighted that lineament density (SD = 0.0057), proximity to surface water body (SD = 0.0046), and aquifer (SD = 0.0036) were the significant contributors towards the groundwater potentiality of the region. Ground truth verification of the proposed model was done with available well yield data of the study area and a linear relation between computed GPZ indices and well yield values was found with a significant positive correlation value of 0.77 (r) that justified the model reliability and authenticity. The proposed model is a robust tool and can be utilized with versatile scale dataset in any parts of the world.