Groundwater Depletion Research Articles

Assessing Groundwater Drought Hazard in Groundwater Depletion Regions: Recommendations for Large‐Scale Drought Early Warning Systems

AbstractIn groundwater depletion (GWD) regions, negative trends in groundwater storage (GWS) are problematic for groundwater drought detection, since they mask climate‐induced drought signals. As this is not yet considered in any large‐scale drought early warning system (LDEWS), we used GWS from the global hydrological model WaterGAP 2.2e to investigate, for the first time at the global scale, how groundwater drought can best be quantified in GWD regions. We analyzed two methods: (a) Linear detrending of monthly GWS time series and (b) analysis of naturalized GWS computed by assuming no human water use. We found that linear detrending is unsuitable for global‐scale groundwater drought monitoring and forecasting as even small deviations from a pronounced linear trend can lead to a systematic over‐ and underestimation of the drought hazard. In contrast, indicators from naturalized GWS can identify climate‐induced GWS anomalies. We recommend to provide, in LDEWS, indicators of the magnitude, duration, and severity of groundwater drought that are based on monthly time series of model‐derived GWS simulated with (“ant” variant) and without water use (“nat” variant). In both GWD and non‐GWD regions, the “nat” variants inform about the occurrence of climate‐induced droughts. In GWD regions, they specify periods in which the negative GWS trend is either exacerbated or alleviated by climate‐induced variations of groundwater recharge, while “ant” variants are of very limited informative value. In non‐GWD regions, the joint analysis of “nat” and “ant” variants informs whether a climate‐induced drought is aggravated or alleviated due to human activities.

Hydraulic properties and groundwater depletion of a nonlinear consolidated aquitard considering vertical heterogeneity with drawdown in adjacent aquifers

Hydraulic properties and groundwater depletion of a nonlinear consolidated aquitard considering vertical heterogeneity with drawdown in adjacent aquifers

Trade-offs associated with changing cropping patterns in semi-arid areas of Morocco.

Trade-offs associated with changing cropping patterns in semi-arid areas of Morocco.

Groundwater depletion intensified by irrigation and afforestation in the Yellow River Basin: A spatiotemporal analysis using GRACE and well monitoring data with implications for sustainable management

Groundwater depletion intensified by irrigation and afforestation in the Yellow River Basin: A spatiotemporal analysis using GRACE and well monitoring data with implications for sustainable management

Simulation of pesticide transport in 70-m-thick soil profiles in response to large water applications.

Simulation of pesticide transport in 70-m-thick soil profiles in response to large water applications.

Declining Freshwater Availability in the Colorado River Basin Threatens Sustainability of Its Critical Groundwater Supplies

AbstractThe Colorado River Basin (CRB) is experiencing persistent aridification due to a complex interplay of natural and anthropogenic activities, resulting in significant groundwater depletion across the region. We used over two decades of NASA GRACE and GRACE Follow‐On (GRACE‐FO) observations (April 2002–October 2024), land surface models and observed data, to document pronounced groundwater depletion in the CRB. We estimate that the CRB lost 52.2 ± 4.0 km3 of terrestrial water storage over the study period, of which groundwater accounted for 65% (34.3 ± 9.2 km3). Of this, the Upper Basin lost 14.6 ± 3.5 km3 of terrestrial water storage (53% from groundwater, 7.8 ± 5.3 km3) while the Lower Basin lost 36.0 ± 6.2 km3 of terrestrial water storage (71% from groundwater, 25.5 ± 7.4 km3). Progress toward groundwater sustainability could be achieved by reducing annual extraction in line with the annual depletion rates presented here (0.35 km3/yr in Upper Basin and 1.15 km3/yr in the Lower Basin).

Predicting Groundwater Levels Using Borehole Data for Sustainable Management Systems

Abstract Groundwater depletion presents a major obstacle to achieving sustainable agriculture, especially in areas such as Karnataka, India. Conventional groundwater monitoring techniques tend to be manual, sporadic, and do not support real-time data integration, which often results in inefficient management of water resources. This project introduces an affordable IoT-based system designed for continuous monitoring and management of groundwater levels. By leveraging ESP8266 microcontrollers, the system combines soil moisture sensors with ultrasonic water level sensors to gather up-to-date information on soil moisture and water availability. The data is transmitted over Wi-Fi to the Blynk IoT cloud platform, enabling users to remotely monitor and control the system through a user-friendly mobile application. Furthermore, the integration of weather information via APIs supports predictive analytics, assisting in more informed irrigation and water management decisions. The automation of pump control based on sensor readings and weather forecasts helps maximize water efficiency, reduce over-extraction, and support sustainable groundwater practices. This solution is designed to be scalable and easily accessible, providing farmers and stakeholders with an effective tool to improve the sustainability of water resources in agriculture.

Climate Change, Industrialization, and Environmental Governance in Udham Singh Nagar, Uttarakhand (2003–2025): A Review

Abstract This review paper presents a comprehensive analysis of the environmental transformation experienced by the Udham Singh Nagar district of Uttarakhand, with a particular focus on Rudrapur, a rapidly developing town within the district. The transformation follows the wave of industrialization initiated by the establishment of the State Industrial Development Corporation of Uttarakhand Limited (SIDCUL) in 2003. The paper investigates the environmental repercussions of this industrial surge, focusing on key climatic and ecological parameters such as changes in average annual rainfall, fluctuations in temperature, and variations in groundwater levels over the past two decades. By drawing on a wide array of sources—including scientific research articles, government databases, satellite imagery, and field reports—the study provides a nuanced understanding of the environmental trajectory of the region. The review highlights the correlation between accelerated industrial growth, rapid urbanization, deforestation, and the subsequent environmental degradation that has ensued. Forest cover loss, encroachment on agricultural land, air and water pollution, and increased carbon emissions have all emerged as by-products of unchecked industrial expansion. The paper further explores how urban sprawl has altered land use patterns and contributed to ecological stress in and around Rudrapur. In response to these challenges, the paper examines the implementation and effectiveness of various government policies and mitigation strategies. These include the State Action Plan on Climate Change (SAPCC), the National Action Plan on Climate Change (NAPCC), biogas plant initiatives aimed at sustainable energy production, rejuvenation of traditional water bodies and ponds, and community-based afforestation and tree protection programs. By synthesizing past and current research findings with policy evaluations, the study identifies critical gaps in environmental governance and planning. It concludes by proposing actionable strategies for sustainable development and environmental conservation, with recommendations focused on integrated land use planning, stricter environmental regulation enforcement, and stronger community participation in ecological preservation efforts. Keywords Climate Change, Industrialization, Environmental Governance, Groundwater Depletion, Deforestation, Climate Resilience

Ground Water Monitoring of East Haryana Cities

Abstract: The National Capital Region (NCR), including East Haryana, is facing a critical water crisis due to rapid urbanization, industrial growth, and over-extraction of groundwater. This study investigates the status of groundwater quality and dynamics in East Haryana cities—specifically Faridabad, Palwal, and Mewat—within the NCR. Through a comprehensive review of literature, site-specific monitoring, and data analysis, the research examines the impact of land use changes, pollution, and climatic variations on groundwater levels and quality. The study focuses on key water quality parameters such as Total Dissolved Solids, hardness, pH, and contaminants like nitrates and heavy metals. The findings emphasize the urgent need for real-time monitoring, sustainable water management practices, and policy integration for long-term water security in the region. Keywords: Groundwater Monitoring, Delhi NCR, East Haryana, Water Quality, Groundwater Depletion

An Ensemble Machine Learning Approach for High-Resolution Estimation of Groundwater Storage Anomalies

Groundwater depletion has emerged as a pressing global challenge, yet the low spatial resolution (0.25°) of Gravity Recovery and Climate Experiment (GRACE) satellite data limits its application in regional groundwater monitoring. In this study, based on 0.25° spatial resolution groundwater storage anomalies (GWSAs) data derived from GRACE satellite observations and GLDAS hydrological model outputs, supplemented with hydrological data, humanities data, and other geographic parameters, we constructed a Stacking-based ensemble machine learning model that achieved a 1 km spatial resolution of GWSAs distribution data across the contiguous United States (CONUS) from 2010 to 2020. The ensemble model integrates eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) models using an Attention-Based Dynamic Weight Allocation (ADWA) approach, along with a ridge regression model. The results indicate that our ensemble model outperforms individual machine learning (ML) models, achieving a coefficient of determination (R2) of 0.929, root mean square error (RMSE) of 25.232 mm, mean absolute error (MAE) of 19.125 mm, and Nash–Sutcliffe efficiency (NSE) of 0.936, validated by 10-fold cross-validation. In situ measurements indicate that, compared with the original data, approximately 61.7% of the monitoring wells (266 out of 431) exhibit a higher correlation after downscaling, with the overall correlation coefficient increasing by about 18.7%, which suggests that the downscaled product exhibits an appreciable improvement in accuracy. The ensemble model proposed in this study, by integrating the advantages of various ML algorithms, is better able to address the complexity and uncertainty of groundwater storage variations, thus providing scientific support for the sustainable management of groundwater resources.

Improving the Accuracy of Groundwater Level Forecasting by Coupling Ensemble Machine Learning Model and Coronavirus Herd Immunity Optimizer

Abstract Groundwater levels are under severe pressure globally due to over-extraction, pollution, and climate change necessitating continuous monitoring for sustainable aquifer management. This study introduces a novel ensemble machine learning (En) model that integrates shallow and deep machine learning (ML) models, optimized through the coronavirus herd immunity optimizer (CHIO), for accurate groundwater level forecasting. This En model was applied to the Ergene River Basin, Türkiye, a region facing severe groundwater depletion and contamination due to intensive agricultural and industrial activities. Groundwater level data spanning 1966 to 2023 on a weekly basis from four wells were used, split into 70% for training and 30% for testing under short- and long-term scenarios. Using the partial autocorrelation function and gamma test the best lag numbers were determined for input data, reflecting aquifer heterogeneity. Score analysis, supported by statistical metrics such as the coefficient of determination (R²) and root mean square error (RMSE), was employed alongside visual aids to assess the developed En model performance. Results demonstrated that deep ML models outperformed shallow ML models achieving R² ~ 0.99 and RMSE ~ 0.5 m. The developed En model outperformed all individual ML models, with score values exceeding 200, and its predictions closely aligned with measured water levels during both testing phases. The findings underscored the developed En model’s contribution to achieving sustainable development goals (SDGs) by enhancing water-use efficiency and addressing environmental, economic, and social sustainability challenges. The proposed approach offers a reliable and adaptable solution for groundwater level forecasting, applicable to other aquifers worldwide. Graphical Abstract

Facing groundwater depletion in India: The role of human activities and climate extremes

Facing groundwater depletion in India: The role of human activities and climate extremes

Application of semi-supervised models for groundwater level simulation in arid regions with small sample sizes.

Application of semi-supervised models for groundwater level simulation in arid regions with small sample sizes.

Sustainable Water-Related Hazards Assessment in Open Pit-to-Underground Mining Transitions: An IDRR and MCDM Approach at Sijiaying Iron Mine, China

The transition from open pit to underground mining intensifies water-related hazards such as Acid Mine Drainage (AMD), groundwater contamination, and aquifer depletion, threatening ecological and socio-economic sustainability. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework using a Multi-Dimensional Risk (MDR) approach to holistically assess water hazards in China’s mining regions, integrating environmental, social, governance, economic, technical, community-based, and technological dimensions. A Multi-Criteria Decision-Making (MCDM) model combining the Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluates risks, enhanced by a Z-number Fuzzy Delphi AHP (ZFDAHP) spatiotemporal model to dynamically weight hazards across temporal (short-, medium-, long-term) and spatial (local to global) scales. Applied to the Sijiaying Iron Mine, AMD (78% severity) and groundwater depletion (72% severity) emerge as dominant hazards exacerbated by climate change impacts (36.3% dynamic weight). Real-time IoT monitoring systems and AI-driven predictive models demonstrate efficacy in mitigating contamination, while gender-inclusive governance and community-led aquifer protection address socio-environmental gaps. The study underscores the misalignment between static regulations and dynamic spatiotemporal risks, advocating for Lifecycle Assessments (LCAs) and transboundary water agreements. Policy recommendations prioritize IoT adoption, carbon–water nexus incentives, and Indigenous knowledge integration to align mining transitions with Sustainable Development Goals (SDGs) 6 (Clean Water), 13 (Climate Action), and 14 (Life Below Water). This research advances a holistic strategy to harmonize mineral extraction with water security, offering scalable solutions for global mining regions facing similar ecological and governance challenges.

Unraveling the causal influences of drought and crop production on groundwater levels across the contiguous United States.

Groundwater depletion in agricultural-dominated regions is attributed to climate and irrigation withdrawals that support crop production. However, despite decades of effort, knowledge gaps remain in understanding the relative influence of drought and crop production on groundwater levels at the continental scale. Here, utilizing empirical observations, we simultaneously track how long-term trajectories of groundwater levels, crop production of seven crops, and drought have evolved over time, and then integrate these observations with a causality-based attribution framework to unravel the relative impact of drought and crop production on groundwater levels across the contiguous United States (CONUS). We find a dominant pattern of decreases in groundwater levels with increases (25-61%) or no change (1-15%) in crop production across the CONUS. We estimate a significant (P < 0.1) causal influence of crop production and drought on groundwater levels in ∼32% (n = 101) and ∼20% (n = 62) of counties, respectively. Further, the extent of impact of crop production on groundwater varies with region and is most pronounced for cotton (42%, n = 18) and wheat (17%, n = 39). The memory effects of crop production (median: 7 years) and drought (median: 3 years) on groundwater levels imply that their impact could last much longer than the annual crop production cycle or the drought exposure period. Further, these findings allude to circular causality between groundwater and crop production, where both entities depend on each other at different time scales. Our work builds on past work and contributes to the growing understanding of food security and groundwater availability to manage these commodities to meet future demands.

Advancing Geohazard Monitoring: Sentinel‐1 InSAR Observations of Land Subsidence in Northern and Central Bangladesh

ABSTRACTBangladesh in the Bengal Delta faces complex environmental issues, including sea‐level rise, coastal flooding, high population density, and widespread poverty. These factors lead to severe land loss, saltwater intrusion, water scarcity, and biodiversity decline, further exacerbated by climate change. These challenges significantly risk groundwater availability and increase the likelihood of natural hazards such as subsidence, landslides, and flooding. This study quantitatively maps the spatial distribution of subsidence in urban and agricultural settings by utilising Differential Interferometric Synthetic Aperture Radar (DInSAR) and Persistent Scatter Interferometric Synthetic Aperture Radar (PSI) techniques with ascending Sentinel‐1 satellite data. We analysed 55 pairs of images with DInSAR and 142 pairs with PSI from March 2017 to October 2022, focusing on five target locations for DInSAR and urban Dhaka for PSI. Findings reveal consistent subsidence in urban Dhaka at an average rate of 16 mm/year, along with semi‐seasonal subsidence variability in five agricultural locations. Specific rates are 7 mm/year in Dhaka, 8 mm/year in both Rajshahi and Mymensingh, and 9 mm/year in Rangpur. Sylhet subsides at a rate of 5 mm/year, potentially linked to the fold and thrust belt and the Dauki Fault. Our research highlights the significant environmental impacts of human activities like groundwater withdrawal and land‐use changes, which contribute to subsidence and groundwater depletion via the Bengal Water Machine. While further study is required to comprehensively understand the relationship between LOS indicated subsidence rates, geological factors, and geomorphological changes, our findings offer crucial insights into the current impacts of climate change and ongoing environmental degradation in the region.

Assessing groundwater drought in Iran using GRACE data and machine learning

Groundwater serves as a critical freshwater reservoir globally, essential for ecosystem conservation and human well-being. Drought conditions adversely impact groundwater systems by first reducing recharge, followed by declines in groundwater levels and withdrawal potential, which can result in agricultural setbacks and irreversible consequences such as land subsidence. The introduction of the Gravity Recovery and Climate Experiment (GRACE) project marked a significant advancement in monitoring terrestrial water storage anomalies (TWSA), encompassing both surface and subsurface water. Traditional methods for assessing groundwater storage anomalies (GWSA), such as piezometric wells, have proven to be costly and inefficient, often lacking sufficient spatial and temporal coverage. Although GRACE data offers valuable insights, its large-scale nature presents challenges for localized basin and aquifer studies, compounded by data gaps resulting from a 15-month interruption during the transition to the GRACE-FO project. This study investigates the status of groundwater across six major river basins in Iran utilizing data from GRACE and its complementary Global Land Data Assimilation System (GLDAS) over a 255-month period from 2002 to 2023. The Extreme Gradient Boosting (XGBoost) algorithm is employed for downscaling TWSA to a resolution of 0.25°, achieving a high Pearson correlation (R) of 0.99 and a root mean square error (RMSE) of 22 mm. The downscaled GWSA, derived from the balance equation, exhibits an average correlation (R) of 0.93 and RMSE of 39 mm with observational data. Following the application of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to fill GWSA time series gaps, this study models and forecasts GWSA trends through 2030 using historical data and SSP2 scenario projections of the canESM5 climate model. Results indicate an average groundwater depletion of 29 cm per year across Iran’s aquifers from 2002 to 2023, with the Caspian Sea basin experiencing the most significant decline. The GRACE Groundwater Drought Index (GGDI) is calculated and compared with the Standardized Precipitation Index (SPI), revealing an 8-month lag in drought propagation from meteorological to groundwater sources in Iran. Furthermore, correlations between the GGDI and teleconnection indices highlight their substantial influence on drought conditions in basins adjacent to major water sources. The results of this study, by emphasizing the reliability of satellite data and machine learning models in groundwater drought monitoring, can assist policymakers in enhancing groundwater resource management, strategic planning, and identifying critical basins, particularly in regions with limited observational data.

Impact of groundwater extraction on subsurface thermal regimes

Abstract Groundwater is widely exploited, leading to groundwater depletion, and a reduction of river flows. While the impact of climate change and hydrologic forcings on the hydrogeological cycle has long been demonstrated, a lack of observations of subsurface thermal changes on the scale of decades is hampering the accurate understanding how human activities change subsurface thermal regimes (i.e., the spatiotemporal evolution of the subsurface temperature). In this paper, we analyse new temperature data illustrating the complex interplay of climate warming and anthropogenically enhanced groundwater flow on subsurface thermal regimes from two sites in different hydrogeological settings. A heuristic numerical model is used for the interpretation of the observed temperature anomalies. We demonstrate that pumping may have a significant impact on the thermal regime of an aquifer, and that depending on hydrogeological conditions and the natural geothermal gradient, this impact might be even more important than that of climate warming.

Assessing and addressing groundwater depletion in yemen’s Ibb sub-basins: an original study on challenges and sustainable management strategies

Assessing and addressing groundwater depletion in yemen’s Ibb sub-basins: an original study on challenges and sustainable management strategies

Hydrogeochemical characterization and geospatial assessment of groundwater quality in the alluvial aquifer of southwestern Punjab in association with health risk assessment due to nitrate and fluoride pollution.

Groundwater contamination is a significant threat to human health and hygiene, particularly when high levels of nitrate and fluoride are present. Punjab is currently experiencing a crisis of groundwater depletion and contamination, particularly severe in the southwestern region due to the limited availability of surface water. So, the present study was planned in the Mansa district of Punjab to know the status of groundwater with special reference to nitrate and fluoride and its associated health risks. The study found that all drinking water parameters in the district exceeded the desirable limits. The order of cations found was Na+ > Mg2+ > Ca2+ > K+, and the anions were ordered as HCO3- > SO42- > Cl- > NO3- > F-. The variation in Na+, K+, Mg2+, and HCO3- levels is attributed to processes such as silicate weathering, evaporation, and direct ion exchange. The dominant types of groundwater in the area are mixed type and Na-Cl type. The concentration of NO3- varied from 13.3 to 56.9 mg L-1 with mean 33.3 mg L-1, while F- ranged from 0.09 to 1.81 mg L-1 with mean 1.07 mg L-1. The study revealed that 23.2% and 12.6% of water samples in the district exceeded the acceptable limits for nitrate and fluoride, respectively. According to the water quality index (WQI), the majority of groundwater in the study area is categorized as very poor (36.6%) for drinking, with the largest contribution from the Budhlada (43.9%) and Sardoolgarh (41.1%) blocks. The hazard quotient (HQ) for NO3- was below 1 for adults. However, HQ was greater than 1 for children in all blocks except for the Bhikhi. Similarly, for fluoride, HQ was less than 1 in all blocks for both children and adults, except for children in Sardoolgarh block. The cancer risk due to nitrate exceeded acceptable limits in all blocks. Conclusively, the study results indicate that the Mansa district of Punjab is facing severe groundwater contamination due to nitrate and fluoride, with the highest contamination levels in Jhunir (47.4% of samples exceeded the acceptable limit of NO3-) and Sardoolgarh (87.18% samples exceeded the desirable limit of F-) blocks of the district. The study recommends implementing strict policies to regulate the use of agrochemicals in fields to mitigate nitrate pollution in groundwater and reduce associated risks.