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Water Resources, Environment, and Ecosystems: Application of New Technology
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Water Resources, Environment, and Ecosystems: Application of New Technology

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 11878

Special Issue Editors

Prof. Dr. Yuanzheng Zhai

E-Mail Website
Guest Editor
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Interests: hydrogeology; hydrogeochemistry; environmental hydrogeology; riverbank filtration; geogenic pollution; anthropogenic pollution; groundwater pollution; groundwater remediation
Special Issues, Collections and Topics in MDPI journals
Dr. Jin Wu

E-Mail Website
Guest Editor
Faulty of Geographical Science, Beijing Normal University, Beijing, China
Interests: groundwater monitoring; groundwater quality assessment; risk assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Hu

E-Mail Website
Guest Editor Assistant
Stake Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Science, Chinese Academy of Sciences, Beijing 100085, China
Interests: water quality improvement by riverbank filtration system; environmental behavior of PFASs; surface water-groundwater interaction; hydrogeochemistry; hydrogeology

Special Issue Information

Dear Colleagues,

Efficient use of water resources is the key to ensuring sustainable development. Due to the complex relationship between resource utilization, economy and environment, the scientific and accurate evaluation of water resource carrying capacity has good social benefits. Climate change and intensifying human activity are projected to affect water quality and exacerbate many forms of ecological degradation. The mechanisms underlying climate change on water resources and ecosystems, its major driving factors and responses of ecosystems to water pollution under changing climatic conditions is still not fully understood. On the other hand, the study of water resources, water environment and water ecosystems is undergoing a revolution owing to the development and application of a diverse range of new technologies and methods. However, these latest technologies and methods are still supported by relatively limited scientific evidence. There is an increasing need to understand how climate change affects water resources and ecosystems. The Special Issue ‘Water Resources, Environment, and Ecosystems: Application of New Technology’ seeks to create such a platform to review and present advanced methodologies, current progress and challenges, as well as future opportunities in water ecosystem management.

Prof. Dr. Yuanzheng Zhai
Dr. Jin Wu
Guest Editors

Dr. Bin Hu
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water resource management
  • water environmental assessment
  • water ecosystem risk
  • artificial intelligence

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Published Papers (9 papers)

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Research

22 pages, 5228 KiB  
Article
Hydrogeochemical Characteristics and Formation Mechanisms of High-Arsenic Groundwater in the North China Plain: Insights from Hydrogeochemical Analysis and Unsupervised Machine Learning
by Xiaofang Wu, Weijiang Liu, Yi Liu, Ganghui Zhu and Qiaochu Han
Water 2024, 16(22), 3215; https://doi.org/10.3390/w16223215 - 8 Nov 2024
Viewed by 478
Abstract
Hydrochemical data were utilized in this study to elucidate the hydrogeochemical characteristics and genesis of high-arsenic groundwater in the North China Plain, employing both traditional hydrogeochemical analysis and unsupervised machine learning techniques. The findings indicate that the predominant hydrochemical types of groundwater in [...] Read more.
Hydrochemical data were utilized in this study to elucidate the hydrogeochemical characteristics and genesis of high-arsenic groundwater in the North China Plain, employing both traditional hydrogeochemical analysis and unsupervised machine learning techniques. The findings indicate that the predominant hydrochemical types of groundwater in the study area are HCO3-Ca·Na and SO4·Cl-Na·Ca. The primary mechanism influencing groundwater chemistry has been identified as rock weathering. The unsupervised machine learning framework incorporates various methods, such as principal component analysis (PCA), non-negative matrix factorization (NMF), machine learning models (gradient boosting trees and random forests), and cluster analysis to explore the characteristics and genesis of groundwater hydrochemical types within the study area. This study demonstrated that the formation mechanism of high-arsenic groundwater results from multiple interacting factors. Full article
(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)
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18 pages, 2358 KiB  
Article
Development on Surrogate Models for Predicting Plume Evolution Features of Groundwater Contamination with Natural Attenuation
by Yajing Wang, Mingyu Wang and Runfeng Liu
Water 2024, 16(19), 2861; https://doi.org/10.3390/w16192861 - 9 Oct 2024
Viewed by 564
Abstract
Predicting the key plume evolution features of groundwater contamination are crucial for assessing uncertainty in contamination control and remediation, while implementing detailed complex numerical models for a large number of scenario simulations is time-consuming and sometimes even impossible. This work develops surrogate models [...] Read more.
Predicting the key plume evolution features of groundwater contamination are crucial for assessing uncertainty in contamination control and remediation, while implementing detailed complex numerical models for a large number of scenario simulations is time-consuming and sometimes even impossible. This work develops surrogate models with an effective and practicable pathway for predicting the key plume evolution features, such as the distance of maximum plume spreading, of groundwater contamination with natural attenuation. The representative various scenarios of the input parameter combinations were effectively generated by the orthogonal experiment method and the corresponding numerical simulations were performed by the reliable Groundwater Modeling System. The PSO-SVM surrogate models were first developed, and the accuracy was gradually enhanced from 0.5 to 0.9 under a multi-objective condition by effectively increasing the sample data size from 54 sets to 78 sets and decreasing the input variables from 25 of all the considered parameters to a smaller number of the key controlling factors. The statistical surrogate models were also constructed with the fitting degree all above 0.85. The achieved findings provide effective generic surrogate models along with a scientific basis and investigation approach reference for the environmental risk management and remediation of groundwater contamination, particularly with limited data. Full article
(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)
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24 pages, 6681 KiB  
Article
A Machine Learning Approach to Monitor the Physiological and Water Status of an Irrigated Peach Orchard under Semi-Arid Conditions by Using Multispectral Satellite Data
by Pasquale Campi, Anna Francesca Modugno, Gabriele De Carolis, Francisco Pedrero Salcedo, Beatriz Lorente and Simone Pietro Garofalo
Water 2024, 16(16), 2224; https://doi.org/10.3390/w16162224 - 6 Aug 2024
Cited by 1 | Viewed by 1445
Abstract
Climate change is making water management increasingly difficult due to rising temperatures and unpredictable rainfall patterns, impacting crop water availability and irrigation needs. This study investigated the ability of machine learning and satellite remote sensing to monitor water status and physiology. The research [...] Read more.
Climate change is making water management increasingly difficult due to rising temperatures and unpredictable rainfall patterns, impacting crop water availability and irrigation needs. This study investigated the ability of machine learning and satellite remote sensing to monitor water status and physiology. The research focused on predicting different eco-physiological parameters in an irrigated peach orchard under Mediterranean conditions, utilizing multispectral reflectance data and machine learning algorithms (extreme gradient boosting, random forest, support vector regressor); ground data were acquired from 2021 to 2023 in the south of Italy. The random forest model outperformed in predicting net assimilation (R2 = 0.61), while the support vector machine performed best in predicting electron transport rate (R2 = 0.57), Fv/Fm ratio (R2 = 0.66) and stomatal conductance (R2 = 0.56). Random forest also proved to be the most effective in predicting stem water potential (R2 = 0.62). These findings highlighted the potential of integrating machine learning techniques with high-resolution satellite imagery to assist farmers in monitoring crop health and optimizing irrigation practices, thereby addressing the challenges determined by climate change. Full article
(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)
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22 pages, 4900 KiB  
Article
Advanced Machine Learning and Water Quality Index (WQI) Assessment: Evaluating Groundwater Quality at the Yopurga Landfill
by Hongmei Zheng, Shiwei Hou, Jing Liu, Yanna Xiong and Yuxin Wang
Water 2024, 16(12), 1666; https://doi.org/10.3390/w16121666 - 12 Jun 2024
Cited by 1 | Viewed by 1227
Abstract
As industrial development and population growth continue, water pollution has become increasingly severe, particularly in rapidly industrializing regions like the area surrounding the Yopurga landfill. Ensuring water resource safety and environmental protection necessitates effective water quality monitoring and assessment. This paper explores the [...] Read more.
As industrial development and population growth continue, water pollution has become increasingly severe, particularly in rapidly industrializing regions like the area surrounding the Yopurga landfill. Ensuring water resource safety and environmental protection necessitates effective water quality monitoring and assessment. This paper explores the application of advanced machine learning technologies and the Water Quality Index (WQI) model as a comprehensive method for accurately assessing groundwater quality near the Yopurga landfill. The methodology involves selecting water quality indicators based on available data and the hydrochemical characteristics of the study area, comparing the performance of Decision Trees, Random Forest, and Xgboost algorithms in predicting water quality, and identifying the optimal algorithm to determine indicator weights. Indicators are scored using appropriate sub-index (SI) functions, and six different aggregation functions are compared to find the most suitable one. The study reveals that the Xgboost model surpasses Decision Trees and Random Forest models in water quality prediction. The top three indicator weights identified are pH, Manganese (Mn), and Nickel (Ni). The SWM model, with a 0% overestimation eclipsing rate and a 34% underestimation eclipsing rate, is chosen as the most appropriate WQI model for evaluating groundwater quality at the Yopurga landfill. According to the WQI results from the SWM aggregation function, the overall water quality in the area ranges from moderately polluted to slightly polluted. These assessment results provide a scientific basis for regional water environment protection. Full article
(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)
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14 pages, 6540 KiB  
Article
Identification of Environmental Damage Process of a Chromium-Contaminated Site in China
by Xiaoyuan Cao, Bin Wang, Litang Hu, Jin Wu, Dan Zhao, Yuanzheng Zhai, Kexue Han and Mingming Wang
Water 2024, 16(11), 1578; https://doi.org/10.3390/w16111578 - 31 May 2024
Viewed by 857
Abstract
Identifying the source and impact pathways of soil heavy-metal pollution is critical for its assessment and remediation. Numerical simulation has been widely used to simulate soil heavy-metal pollution processes and predict risks. However, traditional numerical simulation software requires a large number of parameters, [...] Read more.
Identifying the source and impact pathways of soil heavy-metal pollution is critical for its assessment and remediation. Numerical simulation has been widely used to simulate soil heavy-metal pollution processes and predict risks. However, traditional numerical simulation software requires a large number of parameters, which are difficult to obtain in site-scale studies. This study proposes a rapid method for identifying soil heavy-metal pollution processes using the TOUGH2/EOS7 software. It has automatic calibration and uncertainty analysis capabilities, which can effectively reduce the demand for parameters. This study established a method, including model selection, simulation, validation, and error analysis, to verify the effectiveness of the proposed method. This study identified the most realistic scenario for chromium pollution and simulated its release over 20 years, and the results met accuracy requirements with a best-case fit of 0.9998. The results showed that the method can quickly identify the source and impact pathways of soil heavy-metal pollution, providing strong evidence for environmental damage assessment. Full article
(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)
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17 pages, 7194 KiB  
Article
Hydrochemical Characteristics and Indication to Geothermal Genesis of Low–Medium-Temperature Convection Geothermal Field in Yanshan Orogenic Basin, China
by Wenzhen Yuan, Yifei Xing, Meihua Wei, Xinran Guo, Jin Liu, Jun Gao, Changsheng Zhang and Yuanzheng Zhai
Water 2024, 16(3), 433; https://doi.org/10.3390/w16030433 - 29 Jan 2024
Viewed by 1217
Abstract
The central part of the Zhangjiakou area is occupied by the Yanshan orogenic basin. A large number of piedmont faults developed over time, controlling the exposure of geothermal anomalies. The fluid chemistry characteristics and their influence on the heat generation mechanism of the [...] Read more.
The central part of the Zhangjiakou area is occupied by the Yanshan orogenic basin. A large number of piedmont faults developed over time, controlling the exposure of geothermal anomalies. The fluid chemistry characteristics and their influence on the heat generation mechanism of the medium- and low-temperature convective geothermal field in the area are not fully understood. In this study, the geothermal fluid was sampled and tested, and the hydrogeological background conditions were analyzed. The results show that the sulfate in geothermal fluid originates from the dissolution of gypsum or H2S oxidation in deep magma. The geothermal fluid in the faulted basin flows upward after deep circulation and interacts with shallow groundwater. The main source of geothermal fluid is atmospheric precipitation. The temperature of the hot reservoir is between 82 °C and 121 °C, and the depth of geothermal water circulation is more than 3200 m. It can be seen that the geothermal resources in this area are formed by the long-term contact of residual magma, geothermal heating and mechanical heating of neotectonic movement after atmospheric precipitation recharge. Full article
(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)
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17 pages, 8287 KiB  
Article
Occurrence and Formation Mechanisms of High-Fluoride Groundwater in Xiong’an New Area, Northern China
by Yihan Dong, Ziqian Wang, Dong Wang, Kai Zhao and Bin Hu
Water 2024, 16(2), 358; https://doi.org/10.3390/w16020358 - 22 Jan 2024
Viewed by 1322
Abstract
While extant research has predominantly focused on elucidating the mechanisms of fluorine (F−) enrichment in groundwater within the North China Plain, the occurrence and formation mechanisms of high-F− groundwater in Xiong’an New Area remain unexplored. Consequently, 365 groundwater samples (172 [...] Read more.
While extant research has predominantly focused on elucidating the mechanisms of fluorine (F−) enrichment in groundwater within the North China Plain, the occurrence and formation mechanisms of high-F− groundwater in Xiong’an New Area remain unexplored. Consequently, 365 groundwater samples (172 from shallow groundwater, 193 from deep groundwater) were collected from Xiong’an New Area. Hydrochemical analysis, geochemical modeling, and statistical analysis were used to explore the occurrence and formation mechanisms of high-F− groundwater. The results reveal that the highest F− concentrations in shallow and deep groundwater were up to 3.22 mg/L and 1.79 mg/L, respectively. High-F− groundwater was primarily located at the eastern part of the study area. The distribution area of high-F− shallow groundwater was much greater than that of deep groundwater. F-bearing minerals dissolution and ion exchange were the principal formation mechanisms of high-F− groundwater in both shallow and deep aquifers. Moreover, competitive adsorption, evaporation, and the impacts of Ca2+ and Mg2+ dissolution equilibrium on F-bearing dissolution were crucial to the formation of high-F− groundwater in shallow aquifers. Desorption in an alkaline environment, evaporites dissolution and salt effects were vital to the formation of high-F− groundwater in deep aquifers. These findings can contribute to the support of local groundwater security and management. Full article
(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)
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19 pages, 3916 KiB  
Article
A Study on the Carrying Capacity of Water Resources Utilizing the Fuzzy Comprehensive Evaluation Model—Illustrated by a Case from Guantao County
by Ying Lv, Yuxin Wang, Xiaokai Zhang and Dasheng Zhang
Water 2023, 15(24), 4277; https://doi.org/10.3390/w15244277 - 14 Dec 2023
Cited by 4 | Viewed by 1499
Abstract
The efficient utilization of water resources is the key to ensuring sustainable development. Due to the complex relationship between resource utilization and economy and the environment, there are positive societal effects from a scientific and precise assessment of the carrying capacity of water [...] Read more.
The efficient utilization of water resources is the key to ensuring sustainable development. Due to the complex relationship between resource utilization and economy and the environment, there are positive societal effects from a scientific and precise assessment of the carrying capacity of water supplies. This study aims to investigate the uncertainty associated with the selection of evaluation parameters in assessing the carrying capacity of water resources. To achieve this, the fuzzy comprehensive evaluation model is adopted, and two distinct weighting methods, namely hierarchical analysis and entropy weighting, are applied to analyze the sources of uncertainty in the evaluation results under the framework of the established evaluation indicators. Aiming at the traditional water resources carrying capacity, evaluation indexes are redundant and the correlation is not very close. Thus, the sensitivity analysis method based on the weights of the indexes is proposed to eliminate the indexes that have the greatest impact in order to decrease the uncertainty of the evaluation results. The results indicate that the correlationship coefficient of the comprehensive evaluation results obtained through the two weighting ways is 0.4542, which is not a large correlation, so the uncertainty of the assignment of indicator weights exists. The calculation of the sensitivity index shows that the weights of the three indicators of the utilization ratio of water resources development, water consumption per unit of GDP and per capita water resources are the most sensitive, which are 40.62%, 27.58%, and 23.61%, respectively, and these are the key influencing factors. This demonstrates that improving the accuracy of the primary control indices and the quality control of weight assignment can assist with lowering the error of the carrying capacity assessment of water resources and also point the fuzzy evaluation model in the right direction. Full article
(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)
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21 pages, 11454 KiB  
Article
Revelation and Projection of Historic and Future Precipitation Characteristics in the Haihe River Basin, China
by Litao Huo, Jinxia Sha, Boxin Wang, Guangzhi Li, Qingqing Ma and Yibo Ding
Water 2023, 15(18), 3245; https://doi.org/10.3390/w15183245 - 12 Sep 2023
Cited by 1 | Viewed by 1265
Abstract
Precipitation, as one of the main components of the hydrological cycle, is known to be significantly impacted by global climate change. In recent years, the frequency of extreme precipitation has increased, resulting in greater destructiveness. Atmospheric circulation has a significant impact on extreme [...] Read more.
Precipitation, as one of the main components of the hydrological cycle, is known to be significantly impacted by global climate change. In recent years, the frequency of extreme precipitation has increased, resulting in greater destructiveness. Atmospheric circulation has a significant impact on extreme precipitation in a region. This study aims to investigate the prospective changes in extreme precipitation and their relationship with large-scale atmospheric circulation in the Haihe River Basin. The Haihe River Basin is located in the North China Plain. Mountains and plains can be found in both the eastern and western parts of the study region. The summer seasons experience the most precipitation. The monthly and extreme precipitation (based on daily precipitation) results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models were evaluated using observed precipitation data, which was utilized as a reference. The CMIP6 models were used to assess future changes in the characteristics of extreme precipitation in the study region. The relationship between extreme precipitation and large-scale atmospheric circulation was also analyzed using historical observation data. Remote sensing results regarding land cover and soil erosion were used to analyze the risks of extreme precipitation and their influences in the study region. According to the results, their multi-model ensembles (MME) and BCC-CSM2-MR models, respectively, outperformed all other CMIP6 models in simulating monthly and extreme (based on daily precipitation) precipitation over the study region. Extreme precipitation demonstrated a rising degree of contribution and future risk under numerous scenarios. The degrees of contribution of R95p and R99p are anticipated to increase in the future. BCC-CSM2-MR predicted that Rx1day and Rx5day would decline in the future. Generally, extreme precipitation increased to a greater degree under SSP585 than under SSP245. Both the El Niño–Southern Oscillation and the Pacific Decadal Oscillation displayed substantial resonance with the extreme precipitation from 1962 to 1980 and around 1995, respectively. This study not only improves our understanding of the occurrence of extreme precipitation, but it also serves as a reference for flood control and waterlogging prevention in the Haihe River Basin. Full article
(This article belongs to the Special Issue Water Resources, Environment, and Ecosystems: Application of New Technology)
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