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

1. Introduction

Groundwater represents a critical component of the Earth’s freshwater system, sustaining human populations, agriculture, and ecosystems worldwide. However, increasing water demand; contamination from agricultural, industrial, and urban activities; and climate variability have increased the vulnerability of groundwater resources, creating an urgent need for comprehensive, data-driven assessment and management strategies. Effective groundwater management is, therefore, pivotal not only for local water security but also for achieving global sustainable development goals related to water availability, food security, and ecosystem resilience. In this context, combining field monitoring, remote sensing (RS), geophysical surveys, Geographic Information Systems (GIS), and hydrochemical analysis has emerged as a transformative strategy for understanding and managing groundwater resources at multiple scales.

RS provides synoptic and temporal perspectives on surface processes influencing groundwater, including land use changes, evapotranspiration, and recharge patterns. Geophysical methods, such as electrical resistivity tomography (ERT) and transient electromagnetic (TEM) surveys, enable non-invasive imaging of subsurface structures and groundwater dynamics, while hydrochemical and isotopic analyses reveal the quality and origin of groundwater. GIS serves as the integrative framework linking these datasets, enabling spatial modeling, visualization, and multi-criteria decision-making essential for sustainable groundwater management.

This Special Issue brings together thirteen papers that exemplify the convergence of these approaches across diverse hydrogeological and climatic settings, including arid and semi-arid basins, coastal aquifers, humid deltas, post-mining landscapes, and agricultural watersheds. The contributions illustrate how the integration of geophysical surveys, hydrochemical assessment, GIS-based mapping, remote sensing, and data-driven modeling can address key challenges in groundwater science, such as depletion, salinization, contamination, and climate-induced variability. Collectively, the studies highlight the global relevance of interdisciplinary approaches for sustainable groundwater management and provide methodological guidance for practitioners, researchers, and policymakers aiming to secure water resources in the face of increasing environmental and anthropogenic pressures.

2. Main Contributions of This Special Issue

The importance of this Special Issue lies in its content, which discusses four interconnected themes, namely, (i) geophysical and RS applications for subsurface characterization, (ii) hydrochemical analysis and water quality assessment, (iii) groundwater recharge and potential zone mapping, and (iv) groundwater storage, climate variability, and flood dynamics.

2.1. Geophysical and Remote Sensing Applications for Subsurface Characterization

Several studies in this Special Issue showcase the power of geophysical methods for subsurface imaging and groundwater assessment, particularly in regions where borehole data are sparse or inaccessible.

Bou-Rabee et al. (Contribution 1) conducted a TEM survey in Kuwait’s Subiya Peninsula, a strategic region for the Silk City megaproject. Using 63 TEM stations and applying both Occam and Levenberg–Marquardt inversions, they delineated saline intrusion pathways and fault-controlled fluid movement, revealing higher salinity toward the southern coast and fresher groundwater at depth in the north. These results underscore the growing relevance of TEM in coastal aquifer monitoring and management.

Extending this geophysical approach to broader contaminated landscapes, Siemon et al. (Contribution 2) applied helicopter-borne electromagnetic (HEM) surveys in the post-mining landscape of Lusatia, Germany, mapping low-resistivity zones associated with acid mine drainage. The correlation between conductivity and dissolved sulfate and iron concentrations demonstrated how airborne electromagnetic methods can effectively link physical parameters to hydrochemical processes in inaccessible terrains. Together, the Kuwaiti and German case studies illustrate the efficiency of electromagnetic techniques in detecting subsurface salinity and contamination at different scales.

Kelly and Hladik (Contribution 3) further demonstrated the capabilities of geophysical techniques in a coastal ecological context using multi-channel ERT to investigate salt marsh degradation in Georgia, USA. Their results revealed that tidal inundation rather than lithological variability governs vegetation loss and recovery, emphasizing hydrological controls on subsurface salinity dynamics. Also, Hu et al. (Contribution 4) introduced a multi-attribute channel wave tomography technique that integrates spectral and geometric attributes to detect water-bearing fractures and collapse columns in coal mines. Collectively, these studies show how advances in geophysical imaging provide vital insights into groundwater–surface water interactions, contamination, and hazard prevention.

2.2. Hydrochemical Analysis and Water Quality Assessment

While geophysical imaging defines the physical structure of aquifers, hydrochemical and statistical analyses reveal the processes shaping groundwater quality.

Arıman et al. (Contribution 5) assessed groundwater in Turkey’s Kızılırmak Delta using Principal Component Analysis (PCA) and the Water Quality Index (WQI) in a GIS framework. Eleven physicochemical parameters from observation wells were analyzed to assess drinking suitability and spatial variability. Most samples were unsuitable for consumption due to elevated Ca²⁺, Mg²⁺, Na⁺, and SO₄²⁻ levels, with poor quality concentrated in the west and northeast. PCA revealed two main components that explain most variance, linking salinization and mineral dissolution to water deterioration. The study highlights the influence of geogenic and agricultural factors and demonstrates the effectiveness of integrated WQI–PCA for groundwater management in deltaic settings.

At a broader scale, Williams et al. (Contribution 6) connected hydrochemistry with socio-hydrological drivers by analyzing groundwater sustainability across 1881 drinking water systems in Arizona, USA. They found that rural and minority communities face higher depletion and contamination risks, linking aquifer stress to governance and social equity. The study bridges the physical–social gap by demonstrating that groundwater challenges are as much institutional as they are hydrogeological, an important consideration for future policy-oriented groundwater research.

Furthermore, Hussein et al. (Contribution 7) evaluated deep learning models for classifying the Irrigation Water Quality Index in Algeria’s Naama region using data from 166 wells. Among 16 tested architectures, the CNN-BiLSTM achieved the highest accuracy (0.94) and AUC (0.994), effectively capturing spatial and temporal patterns. SHapley Additive exPlanations (SHAP) analysis identified sodium-related parameters as key predictors. Despite higher computational demands, the model proved robust and reliable, demonstrating the value of hybrid neural networks for data-driven irrigation management in semi-arid regions.

2.3. Groundwater Recharge and Potential Zone Mapping

Within this Special Issue, several studies employed GIS-based multi-criteria modeling to map groundwater recharge and potential zones, an essential step for sustainable water planning.

Hossain et al. (Contribution 8) mapped groundwater recharge zones in Bangladesh’s Barind Tract using an integrated GIS–AHP approach with seven controlling factors. The results show that about 43% of the area has good-to-very-good recharge potential, driven mainly by geology, land use, and soil type. The study estimates annual recharge at 2554 × 10⁶ m³ (22.7% of rainfall) and highlights the value of multi-criteria modeling for groundwater conservation and climate-resilient water management. Similarly, El-Sorogy et al. (Contribution 9) identified groundwater potential zones in Al-Madinah Al-Munawarah, western Saudi Arabia, through weighted overlay analysis of seven thematic layers derived from RS, GIS, and meteorological data. The results highlight the dominance of drainage density, slope, and soil type in controlling recharge distribution, with favorable zones concentrated in alluvial deposits near Wadi Al-Hamd. Validation using well data confirmed the reliability of the delineation and demonstrated the applicability of GIS-based approaches for groundwater exploration in arid environments.

In addition, Rehman et al. (Contribution 10) applied Frequency Ratio (FR) and Weight of Evidence (WoE) models to delineate groundwater potential zones in Pakistan’s Hangu District, integrating Sentinel-2, ALOS-DEM, and CHIRPS rainfall data. The FR model (AUC = 0.93) effectively identified high-potential zones near drainage networks, linking recharge potential to Sustainable Development Goals (SDGs) 6 (Clean Water and Sanitation) and 13 (Climate Action). In line with these efforts, Halder et al. (Contribution 11) introduced an integrated SWAT–VIKOR framework to prioritize agricultural land use in India’s Upper Kansai Basin, explicitly coupling hydrological modeling with decision analysis. Their results highlight strong groundwater–surface water interactions, with baseflow contributing 64% of the total discharge, and identify sub-watersheds (SW4 and SW5) as optimal for sustainable agriculture and water management. Together, these four studies demonstrate how GIS, RS, and Multi-Criteria Decision Analysis (MCDA) frameworks, applied across varied climatic and geomorphological settings, can guide both recharge enhancement and agricultural planning.

2.4. Groundwater Storage, Climate Variability, and Flood Dynamics

While recharge mapping focuses on potential input, groundwater storage and its climatic drivers govern system resilience. Mohasseb et al. (Contribution 12) integrated GRACE satellite gravimetry and GPS-derived vertical displacement to estimate groundwater storage variations in the Main Karoo Aquifer, southern Africa, between 2013 and 2021. Their findings showed positive storage trends with fine-scale variability captured by GPS, validating the synergy between gravimetric and geodetic datasets for long-term groundwater monitoring in data-scarce regions.

Alobid et al. (Contribution 13) examined flood frequency trends in Germany (1990–2024) using ARIMA and ANN models to link climate variables with flood frequency. The results show strong correlations between rising temperatures, increased rainfall, and more frequent floods, with ANN outperforming ARIMA in prediction accuracy. The study underscores the need for adaptive, climate-informed flood management and nature-based solutions such as wetland restoration and groundwater recharge enhancement. This thematic continuity between climate resilience, recharge dynamics, and storage variability underscores the integrated hydrological vision that unites this Special Issue.

3. Conclusions

This Special Issue demonstrates that integrating geophysical, hydrochemical, and geospatial approaches is essential for advancing groundwater science. The combined use of field monitoring, RS, and modeling enhances spatial and temporal understanding of aquifer systems, improves predictive accuracy, and supports sustainable resource management. Data-driven and machine learning methods enable adaptive, scalable monitoring, while GIS- and RS-based analyses guide recharge mapping, groundwater zoning, and land use planning. Together, these studies highlight that groundwater sustainability depends on linking hydrological, environmental, and social dimensions. The collective outcome is a unified framework for groundwater assessment that directly supports global efforts toward SDGs 2, 6, 13, and 15.