Potential Groundwater Recharge Research Articles

Groundwater recharge potential zone modeling in the Wadi Al-Lith Basin, Saudi Arabia

The management of groundwater resources is crucial in arid and semi-arid regions such as Al-Lith Basin, and therefore, the identification of suitable areas for groundwater recharge is important in solving the problem of water scarcity and ensuring the long-term sustainability of aquifers. In this study, the Analytic Hierarchy Process (AHP) technique, in conjunction with GIS, was applied to locate potential zones for groundwater recharge in the Al-Lith basin. The research methodology includes collecting the essential data, including lineament density, slope, rainfall, drainage density, LU/LC, soil, elevation, and TWI. AHP is used to assign relative weights to different qualities, considering their significance in influencing groundwater recharge. The ArcGIS was employed to process and analyze the weighted layers, which resulted in the creation of a comprehensive map illustrating the potential for groundwater recharge. The study results suggested that the Al-Lith Subbasin has several zones that exhibit different levels of groundwater recharge capability. The results obtained from this study indicated that 12.83% of the basin area has significant groundwater recharge potential zones (GWPZ), ranging from high to very high levels. The groundwater recharge potential zones in the basin are categorized as very poor to poor, covering 19.4% of the area, while the moderate groundwater recharge potential zones represent 67.77% of the basin area. The groundwater protection zones were validated using 19 wells distributed across the basin. The validation findings indicate there is an overlap between the GWPZ classes and the wells in the specified location. Overall, the findings of this study can enhance groundwater resource management and inform policy development for targeted interventions aimed at enhancing groundwater recharge.

An underground drip water monitoring network to characterize rainfall recharge of groundwater at different geologies, environments, and climates across Australia

Abstract. Understanding when and why groundwater recharge occurs is of fundamental importance for the sustainable use of this essential freshwater resource for humans and ecosystems. However, accurately capturing this component of the water balance is widely acknowledged to be a major challenge. Direct physical measurements identifying when groundwater recharge is occurring are possible by utilizing a sensor network of hydrological loggers deployed in underground spaces located in the vadose zone. Through measurements of water percolating into these spaces from above, we can record the potential groundwater recharge process in action. By using automated sensors, it is possible to precisely determine when recharge occurs (which event, month, or season and for which climate condition). Combined with daily rainfall data, it is possible to quantify the “rainfall recharge threshold”, the amount of rainfall needed to generate groundwater recharge, and its temporal and spatial variability. Australia's National Groundwater Recharge Observing System (NGROS) provides the first dedicated sensor network for observing groundwater recharge at an event scale across a wide range of geologies, environments, and climate types representing a wide range of Australian hydroclimates. Utilizing tunnels, mines, caves, and other subsurface spaces located in the vadose zone, the sensors effectively record “deep drainage”, water that can move beyond the shallow subsurface and root zone to generate groundwater recharge. The NGROS has the temporal resolution to capture individual recharge events, with multiple sensors deployed at each site to constrain the heterogeneity of recharge between different flow paths, and to quantify (including uncertainty bounds) rainfall recharge thresholds. Established in 2022, the network is described here together with examples of data being generated.

Investigating ground water recharge potential through various analytical methodologies: A case study at Babina block, Bundelkhand region

Investigating ground water recharge potential through various analytical methodologies: A case study at Babina block, Bundelkhand region

Mapping natural groundwater potential recharge zones using GIS-AHP in the Upper Cheliff alluvial aquifer, Algeria

Water scarcity is a big issue in arid and semi-arid regions. This challenge is particularly evident in the Upper Cheliff plain in Algeria, where the alluvial aquifer plays a vital role in drinking water supply and supporting irrigation. This aquifer faces high demand and quality issues. A study was conducted in this context, employing a cartographic approach to assess potential groundwater recharge from precipitation into the alluvial aquifer. The current study aimed at mapping zones with potential natural groundwater recharge zones by applying the Analytic Hierarchy Process (AHP) integrated within a Geographic Information System (GIS) environment, combining various factors that can influence recharge, such as rainfall, surface soil type, slope degree, land use and land cover, unsaturated zone, groundwater depth, and curve number. The map resulting from the analysis indicates that only 22% of the assessed area covers zones with very low and low potential recharge, 35% with moderate potential recharge zones, and 43% with high and very high potential recharge zones. This map reveals that the eastern region of the plain, from the cities of Djendel to Ain Soltane, is moderately to highly favorable for recharge. This is due to the natural recharge from rainfall and watercourse infiltration during dam release periods, excess irrigation water, and recharge from the Miocene sandstone aquifer in areas with direct aquifer contact. A validation process was performed using data from 66 wells distributed in this plain and it indicated that 48 wells exhibited good agreement with the resulting map, while 18 wells showed slight deviations. The results indicate an agreement of 72.72% between expected and exist number value of wells which confirming the good prediction of the AHP technique.

Mapping Favorable Groundwater Potential Recharge Areas Using a GIS-Based Analytical Hierarchical Process: A Case Study of Ferkla Oasis, Morocco

Mapping Favorable Groundwater Potential Recharge Areas Using a GIS-Based Analytical Hierarchical Process: A Case Study of Ferkla Oasis, Morocco

High spatiotemporal asynchrony between water deficit and nitrate accumulation under apple orchards in deep loess deposits

Understanding the relationship between water and nitrate in soils is critical to agricultural production and aquifer vulnerability assessments, especially in regions with intensive agricultural activities and thick unsaturated zones. Deep soils usually record long-term variations in coupling water and nitrate processes, influenced by multiple environmental factors. As such, this study aims to investigate water and nitrate-nitrogen (NO3–−N) contents and their relationships in soil profiles of 13–23 m deep under farmland and neighboring apple orchards with different ages. Soil properties were considered to explore the individual effect of a single factor or combined effects of multiple factors by wavelet analysis and partial least squares path models. Compared with farmland, apple orchards had large water deficit and NO3–−N accumulation due to root water uptake and synthetic fertilizer inputs, with high spatiotemporal asynchrony in deep unsaturated zones. Specifically, water deficit and NO3–−N accumulation were respectively observed in relatively stable (5–15 m) and active layers (0–5 m, spatial asynchrony), with higher water deficit rates under young apple trees (e.g., 12 and 22 years old) but larger NO3–−N accumulation rates under old apple trees (e.g., 23–32 years old, temporal asynchrony), respectively. Under farmland and orchard, soil texture-organic carbon regulated soil water movement, while soil water-organic carbon affected nitrate transport and transformation processes, which further altered soil pH and electric conductivity conditions in deep vadose zones. The growth of apple trees leads to asynchronous water depletion and fertilizer accumulation in soils, and the > 20 years apple trees are limited by insufficient water despite the excessive fertilizer. The dried soil layers not only reduce potential groundwater recharge, but also prolong the residence time of nitrate bombs in thick unsaturated zones. This study provides essential information for sustainable management of vegetation and water resources, and benefits the complicated hydrological and biogeochemical cycles in thick vadose zones.

Aquifers Recharge Potentiality Evaluation using Bivariate and Multivariate Statistical Analysis: A Case Study in Malwathu Oya River Basin

An aquifer is a body of porous rock or sediment saturated with groundwater that plays a vital role in the hydrological cycle. Due to rapid population growth and urbanization, these valuable resources face threats like climate changes, pollution, and over-exploitation. Overdraft, where extraction exceeds recharge, can lead to groundwater depletion. Although Sri Lanka is not considered a water- scarce country, it faces rising water demands due to rapid population growth. Especially in dry zones, groundwater is crucial for drinking and agriculture; unauthorized agro-wells exacerbate groundwater issues. Therefore, this research aims to assess groundwater recharge potential zones in Malwathu Oya river basin using Bivariate and Multivariate statistical analysis. Comparing these techniques will lead to the precise identification of potential recharge areas. And this study primarily focuses on the implementation of GIS to find solutions for this kind of challenge without performing field investigations. Therefore, eight conditioning factors; water level, electrical conductivity, lithology, soil, slope, lineament density, drainage density and land use were selected as groundwater recharge influencing factors. The well inventory database consisted of 380 well locations and, out of these, 70% was considered as the training dataset, and the remaining 30% was taken as the testing data set. Groundwater recharge potential maps (GRP) were developed in ArcGIS 10.8, based on the results obtained from Frequency Ratio (FR), Logistic Regression (LR) and their ensemble method. For testing the accuracy of the FR model, test well locations were cross tabulated with the GRP map, and the highest well percentage was achieved in high groundwater recharge potential zone (37.39%). In the LR model, cross-tabulation results showed that 62.61% of testing wells were in the very high potential zones. The ensemble method of FR and LR revealed that 53.04% of test wells fall into the very high potential class. Hence, it can be assumed that LR predictions are more reliable than the FR model. To further validate the LR model and ensemble model, Receiver Operating Characteristics curve method was used. The Area Under the Curve (AUC) values for Success rate curve (SRC) and prediction rate curve (PRC) in LR method were 63% and 57% respectively. In ensemble method, the AUC values for SRC and PRC were 89% and 75%. These results indicate that the LR model is relatively poor estimator, whereas FR and LR ensemble method is relatively good estimator to evaluate groundwater recharge potential in Mulwathu Oya river basin.
 
 Keywords: Area under curve, Frequency ratio, GIS, Groundwater recharge, Logistic regression

Regional Low Flow Hydrology: Model Development and Evaluation

AbstractLow flows result from the interplay of climatic variability and catchment storage dynamics, but it is unclear which of these variables is more relevant for explaining low flow spatial patterns. Here, we develop a new conceptual model that integrates process‐based hydrological knowledge with statistics and test it for 1,400 Brazilian catchments. Through comparative hydrology, we isolate the low flow generating mechanisms and estimate their components using linear model trees. The model explains 58% of the spatial variance in 7‐day minimum annual flows (Qmin) based on climate and catchment characteristics. The primary Qmin controls depend on the spatial scale of analysis. Catchment characteristics govern Qmin up to the continental scale (107 km2), where their relative importance matches that of climate. At subcontinental scales, catchment characteristics are twice as important as climate in predicting Qmin, suggesting that low flows are governed by the catchment's capacity to attenuate the climatic variability through water storage. Geological properties are the most important catchment characteristics, particularly bedrock type, lithology and topographic slope, determining streamflow recession rates in the dry season. Soil properties, primarily soil class and depth, are half as important as geology. Climate impacts Qmin mainly through mean annual rainfall minus evaporation, representing the potential groundwater recharge, while dry‐season length has the lowest impact. These results hold mainly for highly seasonal and snow‐free climates. Low flow hydrology that combines statistics with process understanding offers a promising framework for understanding regional low flow generating mechanisms and could support other estimation models than that presented here.

Cost-Effective Groundwater Potential Mapping by Integrating Multiple Remote Sensing Data and the Index–Overlay Method

The Choushui River groundwater basin (CRGB) in Yunlin County, Taiwan, is a significant groundwater source for the western part of the region. However, increasing groundwater demand and human activities have triggered a potential crisis due to overexploitation. Therefore, groundwater potential zone (GWPZ) maps are crucial for mapping groundwater resources and water resource management. This study employs the normalized index–overlay method and fuzzy extended analytical hierarchy process (FE-AHP) to map GWPZs cost-effectively. The methodology objectively incorporates weightings from various thematic layers by normalizing and correlating parameters with observed groundwater availability (GA). Site-specific observations, including aquifer thickness, depth to the groundwater level, and porosity, inform GA calculations. Seven comprehensive layers derived from remote sensing (RS) data are processed to obtain weightings and ratings for the groundwater potential index (GWPI) in the CRGB. Selected parameters are categorized into hydrological processes, human interventions, geological, and surface profiles. Hydrological processes include precipitation, modified normalized difference water index (MNDWI), and drainage density. Human interventions consist of the enhanced vegetation index (EVI) and normalized difference building index (NDBI). Surface profiles encompass the terrain ruggedness index (TRI) and slope, enhancing the study’s multi-criteria approach. The observed GA validates the GWPZ accuracy, classifying zones into five categories. According to the GWPI of FE-AHP, about 59.56% of the CRGB area can be categorized as “moderate” to “very good” potential groundwater recharge zones. Pearson’s correlation coefficient between GWPI and GA, based on FE-AHP, outperforms the conventional AHP. This RS-based approach efficiently evaluates GA in aquifers with limited wells, highlighting crucial zones in CRGB’s proximal-fan and southeastern mid-fan for informed groundwater management strategies.

Comparison between MODFLOW Groundwater Modeling with Traditional and Distributed Recharge

Groundwater models serve the function of predicting and analyzing aquifer behavior. They require input information, such as hydrogeological parameters like hydraulic conductivity and storage coefficient, which are used to calibrate the model, and elementary actions that include recharge and extracted volumes. There are cases in which it is insufficient to know the homogeneous recharge entering through the surface basin, referred to as traditional recharge, since, in many instances, the distribution is altered by changes in land use. For this reason, based on the geomorphological characteristics of the basin, weighting is proposed for sites with greater recharge capacity. The present work shows a solution to the recharge distribution using the potential groundwater recharge (PGR) map, which is formed by weighting spatially distributed information: (i) drainage, (ii) precipitation, (iii) land use, (iv) geological faults, (v) soil type, (vi) slope, and (vii) hydrogeology. A comparison is made between groundwater modeling using traditional recharge and PGR recharge. It is noted that the modeling perform similarly for both recharges, and the errors do not exceed 5% absolute error, which validates the model’s reliability. This manuscript demonstrates how to model and calibrate groundwater in aquifers with scarce information and variable recharge, making it reproducible.

Groundwater Recharge Potentiality Mapping in Wadi Qena, Eastern Desert Basins of Egypt for Sustainable Agriculture Base Using Geomatics Approaches

In arid and hyper-arid areas, groundwater is a precious and rare resource. The need for water supply has grown over the past few decades as a result of population growth, urbanization, and agricultural endeavors. This research aims to locate groundwater recharge potential zones (GWPZs) using multi-criteria evaluation (MCE) in the Wadi Qena Basin, Eastern Desert of Egypt, which represents one of the most promising valleys on which the government depends for land reclamations and developments. These approaches have been used to integrate and delineate the locations of high groundwater recharge and the potential of the Quaternary aquifer in the Wadi Qena basin. After allocating weight factors to identify features in each case based on infiltration, land use/land cover, slope, geology, topology, soil, drainage density, lineament density, rainfall, flow accumulation, and flow direction, these thematic maps were combined. The results of the GIS modeling led to the division of the area’s groundwater recharge potential into five groups, ranging from very high (in the western part) to very low (in the eastern part of the basin). The zones with the best prospects for groundwater exploration turned out to be the alluvial and flood plains, with their thick strata of sand and gravel. The groundwater recharge potential map was validated using data from the field and earlier investigations. The promising recharging areas show high suitability for soil cultivation. The results overall reveal that RS and GIS methodologies offer insightful instruments for more precise assessment, planning, and monitoring of water resources in arid regions and anywhere with similar setups for groundwater prospecting and management.

A Case Study of Groundwater Potential for Agricultural Sustainability in Ambala District, Haryana by using Geospatial Approach

This study focuses on analyzing the behaviour of groundwater in the Ambala District using a Geographic Information Systems (GIS) approach. The Ambala District, located in 30.2862° N, 76.9643° E faces challenges related to groundwater availability and sustainability due to increasing water demand and potential environmental impacts. The research integrates various datasets, including hydrogeological parameters, groundwater level measurements, land use/land cover data, and geological information specific to the Ambala District. These datasets are processed and analyzed using GIS tools, enabling the identification of spatial patterns, trends, and relationships associated with groundwater behaviour. The analysis encompasses key aspects such as groundwater recharge areas, flow direction, aquifer characteristics, and vulnerability to contamination. By overlaying different thematic layers and conducting spatial analysis, the study identifies areas with high potential for groundwater recharge, areas of groundwater flow convergence or divergence, and regions prone to contamination risks from anthropogenic activities.

Predicting Groundwater Level using Temporal Attention Enhanced Graph Neural Network

A revolutionary approach for groundwater management is essential. To predicting the water levels using for integrating IoT sensors, cloud computing, and advanced data analysis methods. IoT sensors are employed for real-time measurement of groundwater levels, creating a robust dataset. The paper focuses on predicting future groundwater levels, a crucial aspect of sustainable resource management. To enhance predictive accuracy, a preprocessing algorithm, such as Min-Max normalization, is introduced to clean and normalize the collected data, ensuring its reliability. Additionally, a feature extraction algorithm, such as Principal Component Analysis (PCA), is implemented to identify relevant patterns and trends within the dataset, enhancing the efficiency of subsequent analysis. A novel classification algorithm, Spatial-Temporal Graph Convolutional Network, is introduced, enabling the identification of potential groundwater recharge areas. This classification algorithm leverages historical data and extracted features to categorize regions based on their suitability for groundwater renewal. Finally, this research uses a Temporal attention-enhanced graph Neural Network machine learning algorithm to predict groundwater levels in the next few years. This algorithm utilizes the preprocessed data and extracted features, identifying intricate patterns and trends in historical data to generate precise predictions for groundwater levels in the upcoming years.

Identification of groundwater potential recharge zones in a Tunisian anthropogenic coastal region: Insights from multi-criteria decision-making techniques

In coastal areas, increasing population and expanding urbanization are triggering remarkable pressure on groundwater (GW) resources, consequently posing a threat to their long-term sustainability. This is the case for the Chiba coastal region (northeast of Tunisia) where excessive extraction of GW has led to decreasing GW levels and an acceleration of seawater intrusion. Artificial recharge can serve as a prevention and control method to address these challenges and preserve the regional GW reserves. In this paper, favorable GW recharge zones (GWRZs) in the Chiba region are identified using the Analytical Hierarchy Process (AHP) and the Multi-Influencing Factors (MIF) approaches; two widely acknowledged multi-criteria decision-making (MCDM) techniques. Within a Geographic Information System (GIS) environment, the field and remote sensing data are used to prepare, classify, and assign weightage to thematic layers of lithology, land use/land cover, slope, topography, lineament density, rainfall, drainage density, and soil texture. Furthermore, GWRZ maps were elaborated using overlay weighted sum method to the eight different layers. Using AHP, 18.6%, 72% and 9.4% of the study zone was classified into poor, moderate and good potential GWRZ. While using MIF, the classification of the study area into poor, moderate and good potential GWRZ was 27.3%, 56.5% and 16.2% respectively. The GWRZ maps were validated using the average GW level and field yield data of 32 wells distributed across the study area. They achieved accuracy rates of 78.12% and 87.5% using the AHP and MIF methods, respectively. The GW recharge estimated using AHP method yielded a mean value of 67.09 mm/year (15.25%), closely aligned with the 68.28 mm/year (15.51%) derived through the MIF technique. The findings from this study provide valuable insights for devising strategies aimed at ensuring the long-term sustainable management of GW resources underneath this environmentally sensitive coastal area.

Identification of Groundwater Potential Recharge and Recharge Zones of Tumakuru District Using GIS

Identification of Groundwater Potential Recharge and Recharge Zones of Tumakuru District Using GIS

Groundwater recharge assessment using GIS approach in Chiang Mai basin

The geographic information system (GIS) techniques were used to groundwater recharge estimation and groundwater recharge potential map in Chiang Mai Basin. The groundwater recharge estimation use slope, rainfall and infiltration factor for calculation. Amount of groundwater recharge are 597.20 – 896.19 m3/year. The study used lithology, land use, lineaments, drainage and slope gradient as influence factors to the groundwater recharge potential. The groundwater potentiality was integrated from thematic maps and derived from the sum of the weighted factor using weight overlay method in the GIS system. Almost of the study area is classified as the high groundwater recharge potential zone. The moderate groundwater recharge potential zone is located in the western part of study area and along with the Ping River.

Identification of potential artificial groundwater recharge sites using GIS and the analytical hierarchy process: case study of Tamellalt plain, Morocco

Identification of potential artificial groundwater recharge sites using GIS and the analytical hierarchy process: case study of Tamellalt plain, Morocco

Soil Conservation Service-Curve Number method-based historical analysis of long-term (1936-2016) temporal evolution of city-scale potential natural groundwater recharge from precipitation: case study Algiers (Algeria).

Managing groundwater resources in urban areas requires an adequate understanding and assessment of urban hydrogeological systems (structure, components, connections, and imposed conditions) as a part of a larger, dynamically evolving environment. Urbanization and climate change are amongst the widely recognized signs of such a continuous evolution. Within this context, the present study gives a quantitative assessment of the impact of these two factors threatening water resources in urban environments. The Soil Conservation Service-Curve Number (SCS-CN) method is used to conduct a long-term quantitative analysis of the temporal evolution of the potential natural groundwater recharge from precipitation at the scale of Algiers city for an 80-year-long period (1936-2016). The length of the study period allowed us to account for and analyze important changes in urban settings and climatic conditions within the study zone. Overall, two trend shifts over three distinct periods were found to characterize the temporal evolution of precipitation, several climate change indicators defined for the study, and the potential natural aquifer recharge. A strong, approximately 1:4, linear correlation between the estimated city-scale potential natural aquifer recharge and precipitation was observed for the studied period (R2 = 0.748). Moreover, even though the urban area has known a rapid (2nd order polynomial) increase from 1936 to 2016, climate change (accounted for via the changes in precipitation regime) impacted the city-scale potential natural groundwater recharge with higher magnitudes than urbanization. Finally, the computed climate change indicators show that starting in the mid-1980s, Algiers has started receiving less precipitations, with fewer heavy rain events and longer dry condition periods.

Multi-criteria analysis using satellite images and GIS for mapping groundwater recharge zones in Saudi Arabia: A case study of the Riyadh Region

Abstract Water scarcity is well pronounced in arid and semiarid regions where potential evapotranspiration exceeds precipitation. Saudi Arabia is one of the most water-scarce regions where there are 2 billion m3 of annual renewable freshwater resources, besides 24 billion m3 of freshwater withdrawal, especially from the aquifers with fossil water. In Saudi Arabia, floods often occur and the harvest of flood water for groundwater recharge became an issue of discussion; however, this requires determining groundwater recharge potential (GWRP) zones where water naturally percolates and feeds the depleted aquifers. This study aims to produce a detailed (digital) map for GWRP zones for the Riyadh Region by using a multicriteria approach where datasets were derived from satellite images (Landsat 7 ETM+, Spot-5, and Aster) associated with thematic maps and field surveys. The geo-information system (GIS) was also used to manipulate and integrate the geospatial data layers which represent the controlling factors on GWRP. Results show five classes of GWRP zones, where about 36,448 km2 (89.2% of the Riyadh Region) belong to high and very high GWRP. The resulting map will be key information for decision-makers to select suitable localities for groundwater artificial recharge techniques as an adaptive measure for the changing climate.

Publisher Correction: Geospatial technology based morphometric analysis and watershed prioritization of lower Satluj basin in India for groundwater recharge potential

Publisher Correction: Geospatial technology based morphometric analysis and watershed prioritization of lower Satluj basin in India for groundwater recharge potential