Groundwater Sustainability: Innovations in Resource Management and Environmental Protection

Topic Information

Dear Colleagues,

Groundwater is a vital resource for ecosystems, agriculture, and human consumption, yet its sustainable management and protection remain critical global challenges. Addressing environmental issues related to groundwater—such as over-extraction, contamination, seawater intrusion, and climate change impacts—requires innovative approaches and interdisciplinary collaboration. This Topic aims to showcase cutting-edge research and advancements in groundwater resources and environmental science.

We invite contributions that explore new technologies, methodologies, and strategies for groundwater resource management and environmental protection. Topics of interest include, but are not limited to, the occurrence characteristics and flow mechanisms of groundwater in different aquifer media, groundwater pollution mechanisms and source identification, advanced monitoring and remediation techniques, sustainable water resource optimization, the impact of climate change on groundwater systems, the interaction between groundwater and surface water, and AI and Data-Driven Solutions for Groundwater Monitoring. We also welcome studies on the application of artificial intelligence technologies in groundwater research.

By bringing together diverse perspectives and innovative solutions, this topic seeks to advance our understanding of groundwater systems and contribute to their sustainable management. We look forward to your valuable submissions.

Prof. Dr. Jiannan Luo
Dr. Yu Wang
Dr. Yongkai An
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Earth earth	4.0	5.3	2020	19 Days	CHF 1400	Submit
Environments environments	4.3	5.7	2014	18.6 Days	CHF 1800	Submit
Hydrology hydrology	3.1	6.0	2014	16.5 Days	CHF 1800	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit
Water water	3.5	6.7	2009	17.7 Days	CHF 2600	Submit

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Published Papers (4 papers)

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All Environments Hydrology Sustainability Water Earth

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20 pages, 5434 KB  

Open AccessArticle

Understanding Long-Term Groundwater Storage Variability Using GRACE Data and Explainable Machine Learning

by Mehmet Ali Çelik, Adile Bilik and Yasin Paşa

Hydrology 2026, 13(8), 224; https://doi.org/10.3390/hydrology13080224 - 21 Aug 2026

Viewed by 240

Abstract

The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data [...] Read more.

The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data (GRACE, GLDAS, TerraClimate, and MODIS) with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor Iğdır Basin. Three different modeling approaches were developed: XGBoost, Long Short-Term Memory (LSTM) networks, and their combined model, and interpreted using the Shapley Additive Explanations (SHAP) method. The results showed a significant long-term decreasing trend in groundwater storage anomalies at a rate of −0.87 mm per month during the 2002–2016 period, indicating continuous depletion. The LSTM model demonstrated the best performance with R² of 0.59, RMSE of 19.5 mm, and MAE of 15.1 mm, revealing the dominant role of temporal dependencies in groundwater systems. SHAP analysis identified lagged groundwater anomalies (especially GWS_lag3) as the most effective predictors; this may reflect the memory effect and lagged response specific to semi-arid aquifer systems, but this interpretation needs to be validated in different study areas. Snow water equivalent and total water storage anomalies also emerged as significant determinants, while the direct effect of instantaneous precipitation was found to be limited. This study addresses significant gaps in the literature by combining sequence-based modeling with model interpretability in a semi-arid closed basin. The findings highlight the necessity of using system memory and explainable artificial intelligence together for reliable groundwater prediction. While the proposed hybrid approach has the potential for application in other semi-arid regions, its broader usability needs to be supported by independent validation studies under different hydrogeological and climatic conditions. Full article

(This article belongs to the Topic Groundwater Sustainability: Innovations in Resource Management and Environmental Protection)

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26 pages, 20888 KB  

Open AccessArticle

Groundwater Nitrate Source Apportionment in the Pineland Sand Region Using Bayesian Modeling

by Xiang Li, Kerry Holmberg, Leikun Yin, Leslie Ludtke, Brady Hartmann, Joseph Anderson, John Nieber and Joe Magner

Environments 2026, 13(8), 418; https://doi.org/10.3390/environments13080418 - 24 Jul 2026

Viewed by 308

Abstract

Groundwater nitrate contamination imposes a safety concern. Mitigating groundwater nitrate levels requires a quantitative assessment of source contributions, which is also challenging to estimate considering the data uncertainty and the agriculture environmental complexity. The Pineland Sand Region in north central Minnesota, United States, [...] Read more.

Groundwater nitrate contamination imposes a safety concern. Mitigating groundwater nitrate levels requires a quantitative assessment of source contributions, which is also challenging to estimate considering the data uncertainty and the agriculture environmental complexity. The Pineland Sand Region in north central Minnesota, United States, features sandy soils and has a mixed land-use landscape of agriculture and natural background, with part of the area located within the White Earth Nation Reservation region. We analyzed the land-use patterns and identified nitrate contaminant sources in nature, wetlands, row crops, pasture, and urban areas. To estimate the nitrate source apportionment and its uncertainty in this study area, we first characterized these sources using calcium, sulfate, chloride, and sodium, and then applied a Bayesian mass-balance mixing model using a Monte Carlo Markov Chain approach in 164 groundwater wells and further obtained the probability distributions of the fractional contributions from those specific sources. The results show that nature and wetlands source fractions dominate sites with negligible agriculture impacts, while row crop and pasture sources are estimated with higher contaminant contributions in sites subject to more intensive agriculture management. We also discussed a few specific sites’ contaminant source fractions and suggest a future source characterization scheme to advance groundwater nitrate understanding in the study area. Full article

(This article belongs to the Topic Groundwater Sustainability: Innovations in Resource Management and Environmental Protection)

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25 pages, 19199 KB  

Open AccessArticle

Spatiotemporal Evolution of Groundwater System Sustainability in Northeast China’s Transboundary River Basins Under Agricultural Expansion and Climate Variability: Insights from GRACE Satellite Observations

by Yujia Liu, Yang Liu, Kaiwen Zhang and Changlei Dai

Hydrology 2026, 13(2), 69; https://doi.org/10.3390/hydrology13020069 - 11 Feb 2026

Cited by 1 | Viewed by 1563

Abstract

Groundwater is a critical strategic resource supporting agricultural production and ecological security in the transboundary river basins of Northeast China. However, intensified climate variability and rapid agricultural expansion over the past two decades have imposed increasing pressure on regional groundwater systems. In this [...] Read more.

Groundwater is a critical strategic resource supporting agricultural production and ecological security in the transboundary river basins of Northeast China. However, intensified climate variability and rapid agricultural expansion over the past two decades have imposed increasing pressure on regional groundwater systems. In this study, we integrated GRACE-derived terrestrial water storage anomalies, GLDAS land surface data, meteorological datasets, land-use information, and agricultural statistics to construct a comprehensive assessment framework consisting of groundwater storage anomalies (ΔGWS), the GRACE Groundwater Drought Index (GGDI), and sustainability indicators—REL (Reliability), RES (Resilience), VUL (Vulnerability), and SI (Sustainability Index). By integrating GRACE-derived groundwater dynamics with sustainability indicators (REL, RES, VUL, and SI), enabling a basin-scale, long-term assessment of groundwater sustainability across Northeast China’s transboundary basins, and clarifying the relative roles of climatic variability and intensive human water use. We systematically examined the spatiotemporal evolution of groundwater conditions in the Heilongjiang, Suifen, Tumen, and Yalu River basins from 2002 to 2022, and quantified the relative roles of climatic and anthropogenic drivers. The results indicate that groundwater storage exhibited pronounced seasonal fluctuations alongside a persistent downward trend, with GGDI remaining predominantly negative after 2018, reflecting the development of structural groundwater drought. The SI declined markedly from 0.32 to 0.06, and areas with extremely low sustainability accounted for more than 90% of the study region in recent years. MIC-based dependence analysis showed that sown area (MIC = 0.98) and nighttime light intensity (MIC = 0.92) were the dominant drivers of groundwater degradation, exerting far greater influence than precipitation or potential evapotranspiration. These patterns highlight that policy-driven agricultural expansion and increased irrigation demand have surpassed natural recharge capacity, becoming the fundamental cause of long-term groundwater depletion. This study underscores the urgency of promoting agricultural green transformation, optimizing crop planting structures, improving irrigation efficiency, and enhancing ecological conservation to rebuild groundwater resilience. Moreover, coordinated cross-border groundwater monitoring and management will be essential for ensuring the sustainable use of water resources in Northeast Asia’s transboundary river basins. Full article

(This article belongs to the Topic Groundwater Sustainability: Innovations in Resource Management and Environmental Protection)

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27 pages, 6480 KB  

Open AccessArticle

Optimizing Ecological Water Replenishment in Xianghai Wetlands Using CNN-LSTM and PSO Algorithm Under Secondary Salinization Constraints

by Zhuo Song, Jiannan Luo and Xi Ma

Water 2025, 17(13), 1886; https://doi.org/10.3390/w17131886 - 25 Jun 2025

Viewed by 1062

Abstract

Wetlands play a crucial role in water purification, climate regulation, and biodiversity conservation. However, the Xianghai wetlands (situated in Tongyu County, Jilin Province, China) have experienced severe ecological degradation due to natural factors and unsustainable human activities, leading to declining groundwater levels and [...] Read more.

Wetlands play a crucial role in water purification, climate regulation, and biodiversity conservation. However, the Xianghai wetlands (situated in Tongyu County, Jilin Province, China) have experienced severe ecological degradation due to natural factors and unsustainable human activities, leading to declining groundwater levels and intensified salinity issues. To address these problems, this study aims to optimize ecological water replenishment strategies for the Xianghai nature reserve by integrating groundwater numerical simulation, surrogate modeling (convolutional neural network–long short-term memory neural network, CNN-LSTM), and intelligent optimization algorithms (Particle Swarm Optimization, PSO). During the design of the water replenishment scheme, the objective function maximizes the replenishment volume while considering the secondary salinization of soil in the reserve and its surrounding areas as a constraint. The results show that the surrogate model established using the convolutional neural network–long short-term memory neural network achieved high accuracy, with R² values of 0.9996 and 0.9962 and MREs of 0.0023 and 0.0089 for training and validation sets, respectively; Compared to the random replenishment scheme, the optimized water replenishment scheme significantly reduces secondary salinization. After 10 years water replenishment, the optimized scheme exhibited a 2 km² reduction in the salinized area compared to the randomized scheme, with the degree of salinization being reduced from moderate to mild. This method improves ecological sustainability and can be adapted to meet local water use demands. This simulation-optimization method provides an effective approach for designing water replenishment schemes that address secondary salinization. Full article

(This article belongs to the Topic Groundwater Sustainability: Innovations in Resource Management and Environmental Protection)

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