Assessment and Prediction of Groundwater Vulnerability Based on Land Use Change—A Case Study of the Central Urban Area of Zhengzhou

Driven by the rapid advancement of the economy and urbanization, substantial alterations in land use patterns have taken place, exerting certain impacts on groundwater. This study examines the land use changes in Zhengzhou’s central urban area from 2000 to 2020 and projects these changes to 2030 using the PLUS model. It optimizes the groundwater vulnerability assessment methodology from two key aspects, namely the evaluation indicators and the associated weights, to enhance its suitability for the study area. This study employs a multi-indicator and dual-method validation approach to verify the groundwater vulnerability assessment results, ensuring the accuracy and reliability of the findings. Urban, rural, and construction lands increased significantly, while paddy fields, drylands, and forests decreased. The 2030 prediction suggests a continuation of these trends. The groundwater vulnerability in 2020 correlated strongly with the groundwater quality, particularly with chloride ions (AUC = 0.804, Spearman’s rho = 0.83). The 2030 projection indicates a minimal change in the vulnerability distribution but anticipates an increase in high- and very-high-vulnerability areas, particularly in regions with land use changes, potentially increasing the groundwater contamination risk. This suggests the need for targeted groundwater protection policies to mitigate contamination risks.

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Publication Details

Journal
Water
Published
2024-12-23
DOI
https://doi.org/10.3390/w16243716
Citations
7
Primary Topic
Regional Development and Environment
Type
article
Field-Weighted Citation Impact
7.92
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article

Assessment and Prediction of Groundwater Vulnerability Based on Land Use Change—A Case Study of the Central Urban Area of Zhengzhou

Tianlin Zhang, Zelong Liu, Fengxia An, Zhiyu Wang et al.
7 citations
Water
Regional Development and Environment
7.92
article

Assessment and Prediction of Groundwater Vulnerability Based on Land Use Change—A Case Study of the Central Urban Area of Zhengzhou

Tianlin Zhang, Zelong Liu, Fengxia An, Zhiyu Wang, Yanna Xiong, Wenchao Yuan, Yan Ma
article en
7 citations

Abstract

Driven by the rapid advancement of the economy and urbanization, substantial alterations in land use patterns have taken place, exerting certain impacts on groundwater. This study examines the land use changes in Zhengzhou’s central urban area from 2000 to 2020 and projects these changes to 2030 using the PLUS model. It optimizes the groundwater vulnerability assessment methodology from two key aspects, namely the evaluation indicators and the associated weights, to enhance its suitability for the study area. This study employs a multi-indicator and dual-method validation approach to verify the groundwater vulnerability assessment results, ensuring the accuracy and reliability of the findings. Urban, rural, and construction lands increased significantly, while paddy fields, drylands, and forests decreased. The 2030 prediction suggests a continuation of these trends. The groundwater vulnerability in 2020 correlated strongly with the groundwater quality, particularly with chloride ions (AUC = 0.804, Spearman’s rho = 0.83). The 2030 projection indicates a minimal change in the vulnerability distribution but anticipates an increase in high- and very-high-vulnerability areas, particularly in regions with land use changes, potentially increasing the groundwater contamination risk. This suggests the need for targeted groundwater protection policies to mitigate contamination risks.

WaterVol. 16(24)
Ministry of Ecology and Environment (CN), China University of Mining and Technology (CN), China Energy Science and Technology Research Institute Co., Ltd. (China)
Sustainable cities and communities
Openalex Percentile: Top 3%
Regional Development and Environment
7.92
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Assessment and Prediction of Groundwater Vulnerability Based on Land Use Change—A Case Study of the Central Urban Area of Zhengzhou — Tianlin Zhang, Zelong Liu, et al. · Water (2024) | TGRS Research Map | TGRS