Field Monitoring, GIS, Remote Sensing, Geophysical Techniques, and Hydrochemical Analysis in Groundwater Investigations

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 14853

Special Issue Editors


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Guest Editor
Institute of Geophysics and Meteorology, University of Cologne, Pohligstrasse 3, 50969 Cologne, Germany
Interests: magnetic; gravity; ERT and TEM data acquisition; processing & inversion techniques of applied geophysics; environmental and groundwater geophysics

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Guest Editor
Water and Water Structures Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
Interests: sustainability studies and sustainable use and management of water resources
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Special Issue Information

Dear Colleagues,

Integrating remote sensing (RS) and geophysical techniques in hydrogeophysical investigations has proven to be a powerful approach to understanding and characterizing subsurface water systems. By combining the strengths of both disciplines, researchers can comprehensively understand groundwater resources, their spatial distribution, and their hydrogeological properties. RS techniques, such as satellite imagery, airborne sensors, and radar systems, provide valuable data pertaining to the Earth's surface and its surroundings. These techniques offer a wide range of information, including land cover, vegetation patterns, surface water bodies, and thermal properties. RS data serve as a foundation for identifying potential areas of groundwater occurrence, recharge zones, and discharge areas. On the other hand, geophysical methods provide insights into the subsurface characteristics and structures. By measuring and analyzing the physical properties of the subsurface, geophysical techniques can help determine the depth and thickness of aquifers, identify the geological formations and structures influencing groundwater flow, and estimate hydrogeological parameters such as porosity and permeability. Moreover, advances in data fusion and geospatial analysis enhance the integration of RS and geophysical methods. Geographic Information Systems (GIS) play a key role in integrating, visualizing, and analyzing these data for a comprehensive understanding of subsurface hydrogeology. Data fusion combines datasets, thereby enhancing the reliability of hydrogeophysical investigations. This integration has significant implications for groundwater management, resource assessment, and environmental monitoring, enabling the comprehensive characterization of water systems, supporting sustainable strategies, and assessing contamination risks. Successful fusion requires careful attention to data compatibility, scale, and validation through field measurements.

This Special Issue of Water aims to provide an extensive overview of integrating RS and geophysical techniques in hydrogeophysical investigations. This approach offers a robust means of understanding and characterizing groundwater resources. By coupling RS data/tools with geophysical measurements/techniques, researchers and scientific communities can better understand subsurface hydrogeological conditions, leading to enhanced resource management and more informed environmental decision-making processes. Based on the above introduction, this Special Issue focuses on original high-quality articles addressing one or more of the following topics, including state-of-the-art reviews:

  • Assessment of using RS, geophysical, and GIS in groundwater investigation;
  • Groundwater monitoring;
  • Groundwater-surface water interaction monitoring;
  • Assessment of existing groundwater resources;
  • Assessment of groundwater recharges and aquifer sustainability;
  • Three-dimensional mapping and characterizing the aquifer heterogeneities;
  • Identification/delineation and assessment of groundwater contamination sources;
  • Mapping groundwater contamination;
  • Groundwater advanced modeling to support its sustainability;
  • Connectivity between groundwater and aquatic ecosystems;
  • Connectivity between groundwater and sustainable agriculture;
  • Groundwater and environmental change;
  • Regional and transboundary groundwater (monitoring, assessment, challenges, etc.).

Dr. Ismael Ibraheem
Prof. Dr. Abdelazim Negm
Guest Editors

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Keywords

  • remote sensing
  • geophysical methods
  • geographical information system
  • joint interpretation
  • hydrogeophysical investigations
  • groundwater investigation
  • groundwater monitoring
  • groundwater assessment
  • groundwater sustainability
  • aquifer characterization
  • sustainable water management
  • groundwater quality
  • groundwater contamination
  • groundwater–surface water interaction
  • groundwater resources
  • advanced groundwater modeling
  • investigation of aquifer heterogeneities
  • regional and transboundary groundwater

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Published Papers (8 papers)

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Research

16 pages, 11620 KiB  
Article
Shallow Hydrostratigraphy Beneath Marsh Platforms: Insights from Electrical Resistivity Tomography
by Jacque L. Kelly and Christine M. Hladik
Water 2025, 17(2), 144; https://doi.org/10.3390/w17020144 - 8 Jan 2025
Viewed by 566
Abstract
Salt marshes are ecologically and economically valuable ecosystems, yet are vulnerable to marsh dieback, the rapid death of marsh vegetation, which has affected coastal areas along the southeastern and Gulf coasts of the United States in recent decades. This study used multichannel electrical [...] Read more.
Salt marshes are ecologically and economically valuable ecosystems, yet are vulnerable to marsh dieback, the rapid death of marsh vegetation, which has affected coastal areas along the southeastern and Gulf coasts of the United States in recent decades. This study used multichannel electrical resistivity tomography (ERT) surveys to investigate the shallow hydrostratigraphy (up to 39.2 m depth) of three dieback-affected salt marshes along the Georgia coast to evaluate the influence of site location, vegetation status (dieback versus healthy), and tidal conditions on ERT profiles. ERT profiles revealed consistent subsurface resistivity patterns across the marsh platforms, with low resistivity (0.2 ohm-m) at shallow depths indicating saltwater saturation and a transition to higher resistivity (up to 8.1 ohm-m) at greater depths, potentially signifying a shift to brackish conditions and/or sandy strata. The ERT data indicated that the hydrostratigraphy is similar across all study sites. Furthermore, the ERT data remained consistent regardless of vegetation status, tidal variations, and seasonal changes, suggesting that the processes driving the recovery of marsh dieback are independent of the shallow marsh stratigraphy. These findings enhance our understanding of marsh subsurface conditions, supporting efforts to better understand marsh resilience and guide future research on salt marshes. Full article
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34 pages, 12008 KiB  
Article
Harnessing Explainable AI for Sustainable Agriculture: SHAP-Based Feature Selection in Multi-Model Evaluation of Irrigation Water Quality Indices
by Enas E. Hussein, Bilel Zerouali, Nadjem Bailek, Abdessamed Derdour, Sherif S. M. Ghoneim, Celso Augusto Guimarães Santos and Mofreh A. Hashim
Water 2025, 17(1), 59; https://doi.org/10.3390/w17010059 - 29 Dec 2024
Viewed by 1135
Abstract
Irrigation water quality is crucial for sustainable agriculture and environmental health, influencing crop productivity and ecosystem balance globally. This study evaluates the performance of multiple deep learning models in classifying the Irrigation Water Quality Index (IWQI), addressing the challenge of accurate water quality [...] Read more.
Irrigation water quality is crucial for sustainable agriculture and environmental health, influencing crop productivity and ecosystem balance globally. This study evaluates the performance of multiple deep learning models in classifying the Irrigation Water Quality Index (IWQI), addressing the challenge of accurate water quality prediction by examining the impact of increasing input complexity, particularly through chemical ions and derived quality indices. The models tested include convolutional neural networks (CNN), CNN-Long Short-Term Memory networks (CNN-LSTM), CNN-bidirectional Long Short-Term Memory networks (CNN-BiLSTM), and CNN-bidirectional Gated Recurrent Unit networks (CNN-BiGRUs). Feature selection via SHapley Additive exPlanations (SHAP) provided insights into individual feature contributions to the model predictions. The objectives were to compare the performance of 16 models and identify the most effective approach for accurate IWQI classification. This study utilized data from 166 wells in Algeria’s Naama region, with 70% of the data for training and 30% for testing. Results indicate that the CNN-BiLSTM model outperformed others, achieving an accuracy of 0.94 and an area under the curve (AUC) of 0.994. While CNN models effectively capture spatial features, they struggle with temporal dependencies—a limitation addressed by LSTM and BiGRU layers, which were further enhanced through bidirectional processing in the CNN-BiLSTM model. Feature importance analysis revealed that the quality index (qi) qi-Na was the most significant predictor in both Model 15 (0.68) and Model 16 (0.67). The quality index qi-EC showed a slight decrease in importance, from 0.19 to 0.18 between the models, while qi-SAR and qi-Cl maintained similar importance levels. Notably, Model 16 included qi-HCO3 with a minor importance score of 0.02. Overall, these findings underscore the critical role of sodium levels in water quality predictions and suggest areas for enhancing model performance. Despite the computational demands of the CNN-BiLSTM model, the results contribute to the development of robust models for effective water quality management, thereby promoting agricultural sustainability. Full article
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14 pages, 12217 KiB  
Article
Identification and Validation of Groundwater Potential Zones in Al-Madinah Al-Munawarah, Western Saudi Arabia Using Remote Sensing and GIS Techniques
by Abdelbaset S. El-Sorogy, Talal Alharbi, Khaled Al-Kahtany, Naji Rikan and Yousef Salem
Water 2024, 16(23), 3421; https://doi.org/10.3390/w16233421 - 27 Nov 2024
Cited by 2 | Viewed by 1044
Abstract
Groundwater is an essential water resource utilized for agricultural, industrial, and home applications. Evaluating the variability of groundwater is essential for the conservation and management of this resource, as well as for mitigating the reduction in groundwater levels resulting from excessive extraction. This [...] Read more.
Groundwater is an essential water resource utilized for agricultural, industrial, and home applications. Evaluating the variability of groundwater is essential for the conservation and management of this resource, as well as for mitigating the reduction in groundwater levels resulting from excessive extraction. This study aimed to define the groundwater potential zones (GWPZ) in Al-Madinah Al-Munawarah, Western Saudi Arabia, utilizing remote sensing and geographic information system (GIS) techniques, alongside meteorological data. Seven thematic maps were produced based on the regulatory characteristics of geology, drainage density, height, slope, precipitation, soil, and normalized difference vegetation index (NDVI). The influence of each theme and subunit/class on groundwater recharge was evaluated by weighted overlay analysis, including previous research and field data. The groundwater potential map was created via the weighted index overlay approach within a GIS. The groundwater potentials were classified into three categories: very poor, moderate, and good zones. The low groundwater potential regions encompass 805.81 km2 (44.91%) of the research area, located in mountainous basement rocks, characterized by high drainage density and steep gradients. The moderate zones comprise 45.67% of the total area, covering 819.31 km2, and are situated in low-lying regions at the base of mountainous mountains. Conversely, the favorable zones, comprising 9.42% of the total area, span 169.06 km2 and are located within the alluvial deposits of the lowlands next to the Wadi Al-Hamd basin and agricultural farms. The results’ accuracy was confirmed by overlaying data from 26 wells onto the designated groundwater potential categories, revealing that all wells corresponded with regions of high groundwater potential. The generated map would contribute to the systematic and efficient management of groundwater resources in this area to meet the rising water demands of Al-Madinah. The groundwater potential map is one aspect of groundwater management. It is also very important to assess this potential further via groundwater temporal monitoring, groundwater balance, and modeling. Full article
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24 pages, 14238 KiB  
Article
Unveiling Groundwater Potential in Hangu District, Pakistan: A GIS-Driven Bivariate Modeling and Remote Sensing Approach for Achieving SDGs
by Abdur Rehman, Lianqing Xue, Fakhrul Islam, Naveed Ahmed, Saleh Qaysi, Saihua Liu, Nassir Alarifi, Youssef M. Youssef and Mahmoud E. Abd-Elmaboud
Water 2024, 16(22), 3317; https://doi.org/10.3390/w16223317 - 18 Nov 2024
Cited by 1 | Viewed by 1430
Abstract
Sustainable groundwater development stands out as a contemporary concern for growing global populations, particularly in stressed riverine arid and semi-arid regions. This study integrated satellite-based (Sentinel-2, ALOS-DEM, and CHIRPS rainfall) data with ancillary lithology and infrastructure datasets using Weight of Evidence (WoE) and [...] Read more.
Sustainable groundwater development stands out as a contemporary concern for growing global populations, particularly in stressed riverine arid and semi-arid regions. This study integrated satellite-based (Sentinel-2, ALOS-DEM, and CHIRPS rainfall) data with ancillary lithology and infrastructure datasets using Weight of Evidence (WoE) and Frequency Ratio (FR) models to delineate Groundwater Potential Zones (GWPZs) in the Hangu District, a hydrologically stressed riverine region in northern Pakistan, to support the Sustainable Development Goals (SDGs). Ten key variables, including elevation, slope, aspect, distance to drainage (DD), rainfall, land use/land cover, Normalized Difference Vegetation Index, lithology, and road proximity, were incorporated into the Geographic information system (GIS) environment. The FR model outperformed the WoE model, achieving success and prediction rates of 89% and 93%, compared to 82% and 86%. The GWPZs-FR model identified 23% (317 km2) as high potential, located in highly fractured pediment fans below 550 m, with gentle slopes (<5 degrees), DD (within 200 m), and high rainfall in areas of natural trees and vegetation on valley terrace deposits. The research findings significantly support multiple SDGs, with estimated achievement potentials of 37.5% for SDG 6 (Clean Water and Sanitation), 20% for SDG 13 (Climate Action), 15% for SDG 8 (Decent Work and Economic Growth), 12.5% for SDG 9 (Industry, Innovation, and Infrastructure), and notable contributions of 10% for SDG 2 and 5% for SDG 3. This approach provides valuable insights for policymakers, offering a framework for managing groundwater resources and advancing sustainable practices in similar hydrologically stressed regions. Full article
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21 pages, 3364 KiB  
Article
Integrated Geospatial and Analytical Hierarchy Process Approach for Assessing Sustainable Management of Groundwater Recharge Potential in Barind Tract
by Md. Zahed Hossain, Sajal Kumar Adhikary, Hrithik Nath, Abdulla Al Kafy, Hamad Ahmed Altuwaijri and Muhammad Tauhidur Rahman
Water 2024, 16(20), 2918; https://doi.org/10.3390/w16202918 - 14 Oct 2024
Cited by 3 | Viewed by 1714
Abstract
Groundwater depletion in Bangladesh’s Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, [...] Read more.
Groundwater depletion in Bangladesh’s Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, considering seven factors influencing groundwater recharge: rainfall, soil type, geology, slope, lineament density, land use/land cover, and drainage density. The AHP method was employed to assess the variability of groundwater recharge potential within the 7586 km2 study area. Thematic maps of relevant factors were processed using ArcGIS software. Results indicate that 9.23% (700.22 km2), 47.68% (3617.13 km2), 37.12% (2816.13 km2), and 5.97% (452.70 km2) of the study area exhibit poor, moderate, good, and very good recharge potential, respectively. The annual recharge volume is estimated at 2554 × 106 m3/year, constituting 22.7% of the total precipitation volume (11,227 × 106 m3/year). Analysis of individual factors revealed that geology has the highest influence (33.57%) on recharge potential, followed by land use/land cover (17.74%), soil type (17.25%), and rainfall (12.25%). The consistency ratio of the pairwise comparison matrix was 0.0904, indicating acceptable reliability of the AHP results. The spatial distribution of recharge zones shows a concentration of poor recharge potential in areas with low rainfall (1200–1400 mm/year) and high slope (6–40%). Conversely, very good recharge potential is associated with high rainfall zones (1800–2200 mm/year) and areas with favorable geology (sedimentary deposits). This study provides a quantitative framework for assessing groundwater recharge potential in the Barind tract. The resulting maps and data offer valuable insights for policymakers and water resource managers to develop targeted groundwater management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to agricultural water demand and climate change adaptation. Full article
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15 pages, 710 KiB  
Article
Trends and Drivers of Flood Occurrence in Germany: A Time Series Analysis of Temperature, Precipitation, and River Discharge
by Mohannad Alobid, Fatih Chellai and István Szűcs
Water 2024, 16(18), 2589; https://doi.org/10.3390/w16182589 - 12 Sep 2024
Viewed by 2302
Abstract
Floods in Germany have become increasingly frequent and severe over recent decades, with notable events in 2002, 2013, and 2021. This study examines the trends and drivers of flood occurrences in Germany from 1990 to 2024, focusing on the influence of climate-change-related variables, [...] Read more.
Floods in Germany have become increasingly frequent and severe over recent decades, with notable events in 2002, 2013, and 2021. This study examines the trends and drivers of flood occurrences in Germany from 1990 to 2024, focusing on the influence of climate-change-related variables, such as temperature, precipitation, and river discharge. Using a comprehensive time series analysis, including Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models and correlation and regression analyses, we identify significant correlations between these climatic variables and flood events. Our findings indicate that rising temperatures (with a mean of 8.46 °C and a maximum of 9 °C) and increased precipitation (averaging 862.26 mm annually)are strongly associated with higher river discharge (mean 214.6 m3/s) and more frequent floods (mean 197.94 events per year). The ANN model outperformed the ARIMA model in flood forecasting, showing lower error metrics (e.g., RMSE of 10.86 vs. 18.83). The analysis underscores the critical impact of climate change on flood risks, highlighting the necessity of adaptive flood-management strategies that incorporate the latest climatic and socio-economic data. This research contributes to the understanding of flood dynamics in Germany and provides valuable insights into future flood risks. Combining flood management with groundwater recharge could effectively lower flood risks and enhance water resources’ mitigation and management. Full article
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23 pages, 9106 KiB  
Article
Assessment of Groundwater Quality through Hydrochemistry Using Principal Components Analysis (PCA) and Water Quality Index (WQI) in Kızılırmak Delta, Turkey
by Sema Arıman, Nazire Göksu Soydan-Oksal, Neslihan Beden and Hayatullah Ahmadzai
Water 2024, 16(11), 1570; https://doi.org/10.3390/w16111570 - 30 May 2024
Cited by 4 | Viewed by 2284
Abstract
This study aimed to characterize the chemical composition and spatial distribution of groundwater in the Kızılırmak Delta of Turkey and to evaluate the suitability of groundwater in the Kızılırmak Delta for drinking water use through a Water Quality Index (WQI) assessment. Eleven water [...] Read more.
This study aimed to characterize the chemical composition and spatial distribution of groundwater in the Kızılırmak Delta of Turkey and to evaluate the suitability of groundwater in the Kızılırmak Delta for drinking water use through a Water Quality Index (WQI) assessment. Eleven water parameters, including nitrate (NO3), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), chloride (Cl), potassium (K+), bicarbonate (HCO3), sulfate (SO42−), hardness (measured as CaCO3), electrical conductivity (EC), and pH were analyzed to determine the water quality of each groundwater sample. The WQI was determined using the weighted arithmetic index method and the method specified by the Canadian Council of Ministers of the Environment (CCME). The spatial distribution of the result for all observation wells was plotted. Principal Component Analysis (PCA) was generated utilizing the analytical data from eleven selected samples. As a result of the study, according to the calculated WQI values, the water in most of the wells was not suitable for drinking purposes. The minimum Ca2+ concentration in the study area was 108,817 mg/L, and the maximum was 692,382 mg/L, which showed that the samples in all wells exceeded the WHO limit. The same situation is valid for Mg2+, and the values vary between 100.383 and 5183.026 mg/L. From the spatial distribution of the water quality parameters it has been understood that the eastern part of the region is more suitable than the western part for drinking purposes. The results from correlation analysis showed the strongest positive correlation between Mg2+ and Na+ and Na+ and EC as 0.989. The present study shows that the groundwater of the delta, which has deteriorating water quality, should be treated before it is used for drinking water and protected from contamination hazards. Full article
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18 pages, 8239 KiB  
Article
Groundwater Storage Variations in the Main Karoo Aquifer Estimated Using GRACE and GPS
by Hussein A. Mohasseb, Wenbin Shen, Jiashuang Jiao and Qiwen Wu
Water 2023, 15(20), 3675; https://doi.org/10.3390/w15203675 - 20 Oct 2023
Cited by 3 | Viewed by 1886
Abstract
The Gravity Recovery and Climate Experiment (GRACE) provided valuable insights into variations in Groundwater Storage (GWS). However, the sensitivity of utilizing Global Positioning System (GPS) time series displacement data for detecting changes in GWS remains a subject of ongoing discussion. In order to [...] Read more.
The Gravity Recovery and Climate Experiment (GRACE) provided valuable insights into variations in Groundwater Storage (GWS). However, the sensitivity of utilizing Global Positioning System (GPS) time series displacement data for detecting changes in GWS remains a subject of ongoing discussion. In order to estimate the spatiotemporal GWS, we selected a vertical displacement from 65 GPS stations located in the Main Karoo Aquifer (MKA). We performed total water storage (TWS) inversion on GPS vertical displacement components; after that, we deducted surface water components based on the Global Land Data Assimilation System (GLDAS) from January 2013 to December 2021. Additionally, for validation, we compared our GWS estimates with the GRACE-derived GWS and observed GWS values derived from the WaterGAP Global Hydrology Model (WGHM) compartments. We discovered that the TWS and GWS trends derived from GPS and GRACE exhibited similar behaviors with trend values overestimated by GRACE and WGHM. Our findings demonstrate relatively typical behavior between GPS and GRACE in the first and second principal component behaviors (PCs) and empirical orthogonal function (EOF) loadings (or spatial patterns). With a contribution of 71.83% to GPS-derived GWS variability and 68.92% to GRACE-derived GWS variability, EOF-1 is a relatively potent factor. For Principal Components PC1 and PC2, the GRACE and GPS PCs have correlation coefficients of 0.75 and 0.84, respectively. Finally, with higher temporal resolution, GPS can perform the same task as GRACE in hydrological applications. In addition, GPS can add important and valuable information to assess regional GWS change. Full article
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