Planning Of Groundwater Resources Research Articles

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

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

GIS-based groundwater potential zonation and assessment of groundwater quality and suitability for drinking and irrigation purposes in the Shanmughanadhi river basin, south India

To develop a specific watershed scenario, the quantity and quality of groundwater resources must be assessed and monitored locally. In the present study, an attempt has been made to identify possible groundwater potential zones, determine the quality of groundwater resources in this region, and evaluate their suitability for drinking and irrigation purposes. This goal has been achieved with the combined use of the weighted index overlay analysis (WIOA), groundwater quality index (GWQI), Remote Sensing (RS), and Geographic Information System (GIS) techniques. The proposed study area, Shanmuganadhi, is marked with superior rainfall, oscillating temperature, and runoff with litho-units encompassing charnockite (CHK) and hornblende biotite gneiss (HBG). Over-abstraction of groundwater and intensive agricultural practices have resulted in declining and degrading water quantity and quality in this area. The resulting integrated thematic map has been classified into five groundwater potential zones, namely poor, low, moderate, high, and very high, covering 3.2, 45.49, 15.3, 27.9, and 8.1% area, respectively. Sixty groundwater samples were collected during the pre-monsoon (PRM) and analyzed for major Physico-chemical parameters. From the water quality index assessment, over 28.0% of the samples fall within the “excellent,” 8.0% “good” and 4.0% “poor” in CHK, followed by 5.71% “excellent,” 48.57% “good,” 37.14% “poor,” 5.71% “very poor” and 2.87% “unsuitable” for drinking purposes in HBG terrain of this region. Irrigation indices also demonstrated that most of the groundwater samples in the CHK and HBG exposed zones exhibited good classes for irrigation purposes, as evidenced by the SAR and %Na+. The outcome, which shows the research area's groundwater potential zones, is beneficial for improved groundwater resource planning and management. It is concluded that for larger-scale groundwater-quality investigation and assessment, the groundwater quality index-making approach (GWQI) is more practical and dependable. Water planners and decision-makers may find it helpful in effectively controlling and monitoring groundwater quality at the basin or watershed scale. Reducing urban land use and natural runoff from farms can help prevent water contamination in some regions. To ensure the safe use of groundwater, long-term monitoring of these groundwater was suggested.

Delineation of Groundwater Potential Zones Using Remote Sensing and Gis Techniques of Phewa Watershed (Nepal)

Groundwater is the primary source of water in the study region, both for domestic and agricultural needs. The main objective of this research is to identify the groundwater potential zones via remote sensing (RS) and Geographic information system (GIS), which is essential in evaluating, preserving, and monitoring various groundwater-related development programs. Man-made pressures include over-exploitation of groundwater and climate changes have led to a strain on groundwater resources. As the use of consumable water increases for human consumption, agriculture, industrial growth, municipal, and pharmaceutical needs to evaluate the groundwater potential and aquifer productivity also increase. Groundwater inspections have been historically done by field survey method, which is inefficient or not practical in terms of time and resources. Arc GIS software is utilized to manipulate datasets. The LULC map of the study area will be developed using Landsat 8 satellite data. A soil map and rainfall map will be created using the data sources from USGS, a Parent soil map will creates using the data from NARC and slope map, lineament density and drainage density from ASTER from earth data. Seven thematic maps will be applied, each with appropriate weight and rank assigned based on its characteristics and connection with groundwater. All the thematic layers combine into a GIS domain, and weight values are put to the attribute table for each polygon. The groundwater potential zone map will be classified into different zones, i.e., very poor, poor, moderate, good, and very good. These results will help hydrogeologists, decision-makers, planners, and local authorities formulate better groundwater resource planning in the Phewa watershed area.

Assessment of groundwater potential zone mapping for semi-arid environment areas using AHP and MIF techniques

AbstractGroundwater resources are essential for drinking water, irrigation, and the economy mainly in semiarid environments where rainfall is limited. Currently, unpredictable rainfall due to climate change and pollution on the Earth’s surface directly affects groundwater resources. In this area, most people depend on groundwater resources for irrigation and drinking purposes, and every summer, most of the area depends on groundwater in a semiarid environment. Hence, we selected two popular methods, the analytical hierarchy process (AHP) and multiple influence factor (MIF) methods, which can be applied to map groundwater potential zones. Nine thematic layers, such as land use and land cover (LULC), geomorphology, soil, drainage density, slope, lineament density, elevation, groundwater level, and geology maps, were selected for this study using remote sensing and geographic information system (GIS) techniques. These layers are integrated in ArcGIS 10.5 software with the help of the AHP and MIF methods. The map of the groundwater potential zones in the study area revealed four classes, i.e., poor, moderate, good, and very good, based on the AHP and MF methods. The groundwater potential zone area is 241.50 (ha) Poor, 285.64 (ha) moderate, 408.31 (ha) good, and 92.75 (ha) very good using the AHP method. Similarly, the MIF method revealed that the groundwater potential classes were divided into four classes: 351.29 (ha) poor, 511.18 (ha), moderate, 123.95 (ha) good, and 41.78 (ha) very good. The results were compared to determine which methods are best for planning water and land resource development in specific areas that have basaltic rock and drought conditions. Both groundwater potential zone maps were validated with water yield data. The receiver operating characteristic (ROC) curve and area under the curve (AUC) model results are found to be 0.80 (good) and 0.93 (excellent) using the MIF and AHP methods, respectively; hence, the AHP method is best for delineation of groundwater potential zone maps and groundwater resource planning. The present study’s framework and the results will be valuable for improving the efficiency of irrigation, conserving rainwater and maintaining the ecosystem in India.

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

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

Providing Enhanced Insights into Groundwater Exchange Patterns through Downscaled GRACE Data

The measurement of groundwater exchange between neighboring regions is a critical topic in water resource management and can usually be achieved through a combination of field investigations and the use of groundwater flow models. In this study, we employed the water balance and Darcy’s law methods, utilizing downscaled Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) data to assess groundwater exchange patterns in the Beijing-Tianjin-Hebei (BTH) region of China. Additionally, we determined the contributions of human activities and climate factors to the observed variations via residual analysis. The results revealed a consistent decrease in groundwater storage in the study area since 2008, especially in the spring and summer months. The groundwater exchange rates calculated by 1° and 0.05° groundwater storage anomalies (GWSAs) were basically consistent, and the downscaled GWSAs could better reflect the small-scale groundwater exchange characteristics. The groundwater exchange rate showed a decreasing trend from the Piedmont plain to the coastal areas. A notable trend of declining groundwater exchange between the Taihang Mountains and Piedmont plains was observed, and the downward trend gradually intensified from north to south between 2003 and 2007. After 2008, there was an increasing trend, and coastal areas exhibited the smallest amount of groundwater exchange. Human activities emerged as the predominant factor accounting for more than 90.9% of the overall reduction in groundwater storage, while climate change imposed a minimal influence on groundwater storage variations. The insights obtained in this study hold significant implications for groundwater resource planning and management in the region.

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

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

Developing a primal-dual algorithm for optimal planning of groundwater resources

Abstract The exploitation of water resources to provide water for agriculture follows methods that increase the efficiency of adopted policies. One of the effective ways to improve the efficiency of these systems is to evaluate the role of flow estimation in improving performance indicators such as reliability and vulnerability. In the correlation of water, energy and food, the purpose of decision-making is to achieve a balance between water extraction and energy consumption, which will lead to a reduction in the risk of supplying the water needed by the plant during periods of drought stress. In this article, a decision-making method using discrete wavelet transform and primal-dual algorithm is introduced to estimate the amount of monthly groundwater extraction. The proposed model has been evaluated by the Nash–Sutcliffe method and the mean squared error and optimized to increase the reliability of agricultural water supply. The results indicate the strong role of the accuracy of the proposed method in the efficiency of the aforementioned policies, as it has shown an 8% increase in reliability.

A Novel Simulation-Optimization Model Built by FloPy: Pollutant Traceability in a Chemical Park in China

Heavy metal pollution of groundwater will not only destroy the ecological environment but also negatively affect the functioning of the human liver. Tracing the source of groundwater pollution is an important way to protect groundwater resources. FloPy is promoting the use of big data in the groundwater field, especially in groundwater resource planning and management and contaminant traceability. This paper takes Mn as an example and codes a simulation-optimization model for solving the groundwater pollutant traceability problem using FloPy. The Bayesian optimization and strengthen elitist genetic algorithm (SEGA) algorithms are then used to optimize the hydraulic conductivity and pollutant sources in the study area. The results show that the model runs in 411 s, which is an acceptable amount of time spent, the slope of the fitted curve between the model-calculated water level and the actual observed water level is 0.914, and the contaminant traceability results can successfully locate the contaminant sources in real engineering problems. The numerical groundwater flow model and solute transport model can be quickly built, modified, and run by writing code, and can be easily and efficiently coupled with various optimization algorithms with FloPy.

Assessing groundwater potential zones and artificial recharge sites in the monsoon-fed Murredu river basin, India: An integrated approach using GIS, AHP, and Fuzzy-AHP

The sustainable management of water relies heavily on groundwater resources, and an important aspect of their effective utilization is artificial recharge. In the specific case of the Murredu watershed in India, a comprehensive approach is proposed in this research, integrating Geographic Information Systems (GIS), Remote Sensing (RS), and Multi-Criteria Decision Making (MCDM) techniques. The aim is to delineate groundwater potential zones (GWPZs) and identify suitable locations for artificial recharge. Several thematic layers, such as slope, drainage density, geomorphology, land use land cover (LULC), curvature, soil, topographic wetness index (TWI), rainfall, distance from the river, and elevation, were considered in this study. To ascertain the relative importance of these thematic maps in influencing groundwater availability and recharge, the Analytic Hierarchy Process (AHP) and Fuzzy-AHP techniques were employed to assign weights. Utilizing a weighted overlay technique within a GIS environment, the GWPZs map was generated. Within the study area, the GWPZs were categorised as poor, moderate, and high based on the AHP and Fuzzy-AHP models. According to the AHP model, 14.93% of the GWPZs in the region were classified as poor, 73.53% as moderate, and 11.54% as high. On the other hand, the Fuzzy-AHP model classified 14.02% as poor, 76.55% as moderate, and 9.44% as high. The accuracy of these classifications was verified using the receiver operating characteristic curve (ROC) and area under the curve (AUC), resulting in accuracy rates of 71.4% for AHP and 77.6% for Fuzzy-AHP. Additionally, the study employed RS and GIS techniques to develop a map by overlaying the drainage density and lineament density maps. This approach helped identify 126 potential sites for artificial recharge based on the best-performing Fuzzy-AHP model. The insights gained from this study facilitate the sustainable management and planning of groundwater resources, ensuring their availability and sustainability for future generations.

Inferences of Groundwater Response to Projected Hydroclimatic Changes in North Florida

Discerning anthropogenic stressors on groundwater is critical for climate change adaptation to reduce risks and increase resiliency. Long-term groundwater level trends are forecasted and examined at three sites in North Florida using a large ensemble of Global Climate Model (GCM) projections under low and medium emission scenarios. The forecasts indicate groundwater levels are likely to decline from 2020 to 2099. However, the declines are expected to accelerate after 2040s, reaching critical levels by the end of this century. Pumping impact constitutes 10% to 45% of future declines but is amplified by enhanced drought. Examination of distinct influence of rainfall, evapotranspiration (ET), and groundwater pumping on future trends shows highly divergent groundwater response to projected hydroclimatic changes. The future long-term rainfall trend may lead to rising groundwater levels, which may be overshadowed by heightened ET loss driven by climate change and increased groundwater pumping, causing steep declines. This study also reveals poor performance of predictions driven by GCM projections in replicating the timing of high and low levels at the sites influenced by Florida’s peninsular climate due to the limitation of downscaling and bias-correction to capture oscillations in climate cycles driving hydrologic memory. However, groundwater levels are predicted well by a few GCMs at one site influenced primarily by continental climate. Additionally, a multidecadal harmonic analysis exposes presence of centennial periodicity in groundwater levels, which opens a new perspective in the understanding of climate change impacts on groundwater resources. Further investigation is needed to better understand the effect of centennial cycles on future groundwater levels and how these cycles can be incorporated into the downscaling methods. Hence, GCM-based forecasts are recommended to be cautiously utilized for groundwater resource planning when they significantly depart from historical long-term cyclic patterns.

GIS-based multi-criteria decision-making techniques and analytical hierarchical process for delineation of groundwater potential.

The profound knowledge and management of groundwater resources is a prerequisite in order to ensure the sustainability of these resources. In this research, multi-criteria decision-making (MCDM) and analytical hierarchical process (AHP) method based on GIS were used to determine the groundwater potential recharge model of the Mornag plain (coastal area in North Tunisia). Influential eight factors were used in the groundwater recharge modeling: lithology, land use/land cover, hydrogeomorphology, elevation, rainfall, drainage density, lineament density, and soil. The influence of each factor was examined by the weighted linear combination method. The results reveal a very high to high groundwater recharge potential in the order of 20% of the total area. The validation of results by the histogram method showed that 41% of the total area corresponds to the moderate to very high recharge potential classes. The groundwater recharge potential model (GIS-MCDM-AHP) is useful in better management and planning of groundwater resources and implementation of wells and hydraulic structures in arid and semi-arid areas.

Evaluation and Prediction of Groundwater Quality in the Source Region of the Yellow River

With the disturbance of human factors, the groundwater resources in the source region of the Yellow River have gradually depleted and the water quality has become worse, which has seriously affected the development of high-altitude areas. The groundwater quality of the source region of the Yellow River from 2016 to 2020 was evaluated using single-component and comprehensive evaluation methods, following by a prediction of the groundwater quality from 2021 to 2100 based on the RCPS (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios coupled with the SWAT hydrological model under the CMIP5 global climate model. The results indicated that the groundwater temperature had an increasing trend, pH showed an obvious decreasing trend, and total hardness (Th), sulfate, and ammonia nitrogen (NH4+-N) contents exhibited no obvious increasing or decreasing trend in the source region of the Yellow River during 2016–2020. The increase rate of total nitrogen (TN) and total phosphorus (TP) in the future climate scenario followed the order of RCP 8.5 > RCP 4.5 > RCP 2.6, and the groundwater contents of TN and TP in the source region of the Yellow River gradually increased. This result is of great significance, as it can help clarify the current situation of groundwater in high-altitude and cold regions, showing the influence of groundwater on global climate change. It provides a reference for the development and utilization planning of groundwater resources in the source region of the Yellow River in the future.

Geo-statistical analysis of ground water fluctuations for sustainable management of groundwater resources within the chaliyar basin, southwest India

Ground water is the primary source for potable water and it forms the fundamental connecting link amongst the Climates, Society & Natural Ecosystem. Phenomenon like climate change has a marked influence on ground water recharge giving rise to spatial variation in ground water levels. Today, fluctuations in ground water level have become a major problem and their study is of prime importance from water security & sustainable development point of view. To meet such challenges, geospatial technologies have proven to be an effective enabler i.e. spatial interpolation mechanism using geo-statistical analysis has become very popular. The Kriging method is an example of such techniques. Hence, this paper attempts to study & analyze the spatio-temporal variations of ground water levels pertaining to 29 observation wells located in Chaliyar basin, Southwest India for which Geo-statistical analysis was performed using ordinary Kriging method in Arc GIS environment. Finally, the spatial maps showing the ground water level fluctuations were generated for the Chaliyar basin which could be used for sustainable management & planning of Ground water resources within the basin.

Characterization of the groundwater quality in Udham Singh Nagar of Kumaun Himalaya, Uttarakhand

The Udham Singh Nagar district is affluent in fertile land and water resources and even favoured for industrial advancement, resulting in a rapid increase of industrialization and urban growth, which are the inevitable causes of groundwater contamination. In recent years the increased rate of urbanization and Integrated Industrial Estate (IIE) in Pantnagar, Kashipur and Sitarganj area (of the district) have increased the demand for groundwater. Proper groundwater resource planning and monitoring on a regional scale are required to cope with the pace of rapid urbanization and industrialization along with dire irrigation needs. To suffice this strategy, the spatial distribution of water quality shall determine the sustainable approach to be implemented in an area. This research aims to assess the hydrochemical quality of the groundwater in Udham Singh Nagar district (areal extent 3055 Km2), Uttarakhand. The groundwater samples collected from the aquifer have shown anomalous values of Total Hardness (TH), Total Dissolved Solids (TDS), Magnesium (Mg2+), Iron (Fe), and Lead (Pb2+) on undergoing major elemental analysis, which confirms degradation in the groundwater quality. Moreover, the Piper Trilinear Diagram (PTD) has identified groundwater's hydrochemical facies and characterization. Based on the interpretation of TDS, most groundwater samples < 500 mg/L of dissolved solids, making it desirable for drinking. Therefore, this phenomenon attributes to suitable drinking water present in the aquifer of Udham Singh Nagar district, Uttarakhand.

Groundwater potential mapping based on long time series remote sensing data in Penghu Islands, China

AbstractIslands are special bodies of land surrounded by seawater. Many islands are facing water shortages as the demand for groundwater increases, thus, highlighting the importance and urgency of the assessment, planning, and management of island groundwater resources. To facilitate the same, the groundwater potential of the Penghu Islands in China was assessed for the first time using remote sensing (RS), geographic information system (GIS), and the analytic hierarchy process (AHP). Long‐term series remote sensing studies were also conducted to monitor the dynamic change in groundwater potential and to identify groundwater potential areas. In this study, the geological environment factors were normalized, and their weight was allocated based on the AHP of RS and GIS. The groundwater potential assessment (GPA) index, established using the weighted comprehensive algorithm, was used to analyse groundwater potential and demarcate groundwater potential zones. Finally, the trend in the spatio‐temporal variation of groundwater potential was analysed using long‐term series remote sensing data. The results from 2015 to 2021 showed that the Penghu Islands can be divided into five groundwater potential zones of different grades, such as 6.3%–8.1% of grade I, 22.2%–24.2% of grade II, 23%–24.4% of grade III, 19.6%–21% of grade IV, and 25.1%–25.5% of grade V. The results of the GPA were verified by comparing them with the abundance of water‐content rock formation in hydrogeologic maps, which yielded correlation coefficients of 78.3%–88.9%. Overall, the groundwater potential zones of different grades in the study area tended to be stable all year‐round, including the zones of grades I and II. The results of this study highlighted the reliability of predicting groundwater potential based on long‐term series remote sensing data and its usability to local personnel in appropriate groundwater resource planning and management.

Sub-watershed prioritization of Koyna river basin in India using multi criteria analytical hierarchical process, remote sensing and GIS techniques

Watershed characterization and prioritization have become essential for proper natural resources management and sustainable development. This paper introduces an empirical hierarchical method based on the multi-criteria analytical hierarchical process (AHP). This method has been applied for sub-watershed or watershed prioritization and land and water development of sensitive areas in the Koyna River Basin. The study area was delineated and classified into nineteen sub-watersheds, which referred as SW1 to SW19. Remote sensing (RS) and Geographic Information System (GIS) methods are highly developed that help planners and policymakers create reliable and timely plans and decisions. The geomorphologic parameters are used in the watershed prioritization analysis covering the linear/areal aspects viz. the bifurcation ratio, stream density, stream frequency, drainage intensity, texture ratio, overland flow with profile factors. Drainage intensity is identified as the most important of morphometric characteristics to identify the prioritization of the watershed. We have found that SW1 to SW5, SW9, SW10, SW16, and SW19 very high priority sub-watersheds, SW6, SW7, SW8, SW11, SW13, SW15, SW17, and SW18 moderate priority sub-watersheds and SW12 and SW14 low priority sub-watersheds in the basin area using RS and GIS methods. These sub-watershed wise planning and management would be beneficial important for the maintaining the ecosystem, and recharging groundwater. The results can be helpful to the development and planning of groundwater resources.

A three-dimensional numerical groundwater flow model to assess the feasibility of managed aquifer recharge in the Tamne River basin of Ghana

Increasing population growth and global climatic changes threaten water security in semiarid regions such as Northern Ghana. The Tamnean Plutonic Suite aquifer is the main source of water supply for the inhabitants of the Tamne River basin, which is a transboundary subbasin of the White Volta Basin, Ghana. The basin is a flood-prone area where flooding occurs every rainy season, but there is water scarcity during the dry season, mainly due to poor groundwater resources planning. It is expected that the population will increase in the next 10 years, implying a greater water demand. A steady-state and transient groundwater flow model has been developed to understand the hydrogeological conditions and assess the feasibility of managed aquifer recharge (MAR) in the area. A single granitic aquifer formation was delineated from the three-dimensional lithology modelling. The calibrated aquifer recharge through precipitation is very low due to high evapotranspiration and low rainfall. A MAR injection scenario was tested using the available treated floodwater that is registered during the rainy season in the area. The results show the total volume of water injected at the end of the 4-month study period is 11,000 m3/day (approximately 1.3 × 106 m3), which significantly increases aquifer storage and groundwater levels. The volume of water recovered at the end of 8 months (1.4 × 106 m3) is enough for domestic and irrigation purposes during the dry season. In general, MAR is feasible in augmenting the water levels in the area when combined with controllable irrigation and domestic withdrawals.

Identification of groundwater potential zones using remote sensing, GIS, machine learning and electrical resistivity tomography techniques in Guelma basin, northeastern Algeria

In this research we assess and map groundwater potential in the Guelma Basin (northeastern Algeria) using an approach combining remote sensing, GIS, statistical and machine learning models. Four models were used including the frequency ratio model with both conventional (CFR) and modified (MFR) versions, the decision tree (DT), and the random forest (RF). For this purpose, firstly, thirteen hydro-geo-morphological variables influencing groundwater potential have been mapped using GIS and remote sensing techniques including elevation, slope, aspect, topographic wetness index, slope Length and Steepness factor, profile curvature, plan curvature, drainage density, distance to river, lineament and fault density, distance to faults and lineaments, lithology, and land use/land cover. Secondly, the groundwater potential was assessed and mapped based on the four models using the training data. Finally, the obtained groundwater potential maps of the four models have been validated using two approaches: (i) a statistical approach based on the receiver operating characteristics curves (ROC); (ii) a geophysical approach by interpreting the electrical resistivity tomography (ERT) results. The validation process gives the Random Forest method as the most accurate. The obtained map by this model is the main finding of this research, where the very high groundwater potential class occupies 8.25%. It is located mostly in the Guelma plain centre and in the northern part of the study area. The used approach and the obtained results may serve for water resource managers to improve groundwater resource planning and to resolve regional scale issues in this area or elsewhere.

Estimation of spatiotemporal groundwater recharge distribution in humid regions with tropical climate

The precise determination of groundwater recharge variation is a fundamental task for the sustainable planning of groundwater resources, particularly in heavily pressured aquifers. In order to determine the spatial and temporal variability of groundwater recharge in an urban aquifer with a humid climate, such as the San Salvador aquifer, two Mass Balance methods were used (one in the subsurface zone, Soil Water Balance (SWB) and another in the saturated zone, Chloride Mass Balance (CMB)). The SWB was calculated on a daily scale for four years (2012-2015) through the modified Thornthwaite and Mather method, using a set of daily climatic data grids and physical data from the study area. The CMB was used to determine the groundwater recharge in drilled wells and springs samples taken during 2009 and 2016, in the upper part of the basin, where various studies suggest that the main aquifer recharge occurs. The results of the SWB indicate a strong temporal and spatial variation of the recharge in the study area, which can vary between 326 and 561 mm year-1, in dry and wet years, respectively. The CMB results showed consistency with the SWB, groundwater recharge values ranged between 313 and 693 mm year-1. In both methods the mean annual recharge is similar and represents between 20% and 30% of the precipitation. The application of both methods could be used in similar areas, the selection of the method will depend on the objectives of the study.