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Groundwater Sustainability: Innovations in Resource Management and Environmental Protection
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Topic Editors

College of New Energy and Environment, Jilin University, Changchun 130021, China
Dr. Yu Wang
School of New Energy and Environment, Jilin University, Changchun, China
Dr. Yongkai An
School of Water and Environment, Chang’an University, Xi’an 710054, China
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Groundwater Sustainability: Innovations in Resource Management and Environmental Protection

Abstract submission deadline
31 October 2026
Manuscript submission deadline
31 December 2026
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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

  • hydrogeology
  • groundwater resources
  • groundwater pollution
  • groundwater modeling
  • groundwater management
  • groundwater remediation
  • groundwater monitoring
  • groundwater and surface water interaction
  • artificial intelligence

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Environments
environments 		3.7 5.7 2014 22.8 Days CHF 1800 Submit
Hydrology
hydrology 		3.2 5.9 2014 15.3 Days CHF 1800 Submit
Sustainability
sustainability 		3.3 7.7 2009 19.7 Days CHF 2400 Submit
Water
water 		3.0 6.0 2009 17.5 Days CHF 2600 Submit
Earth
earth 		3.4 5.9 2020 23.7 Days CHF 1200 Submit

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Published Papers (1 paper)

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27 pages, 6480 KiB  
Article
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
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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