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<p>The coastal aquifer of the Mekong Delta (MD) is facing groundwater challenges exacerbated by the joint impacts of anthropogenic activities and climate change. Groundwater sustainability in MD needs a clear understanding of both natural and human-related factors that cause risks, a knowledge that is currently lacking in MD. This study introduced a framework to separately quantify physical and anthropogenic groundwater risks in MD’s multi-layered coastal aquifer system. Leveraging a weighted overlay analysis, we combined thematic layers (e.g., distance to sea, distance to drain, aquifer thickness, groundwater level, extraction capacity etc.,) using multiscale geographically weighted regression (MGWR) model and observed groundwater nitrate data of 216 locations. Results were compared to traditional Analytical Hierarchy Process (AHP)-Frequency Ratio (FR) methods. The MGWR model demonstrated superior accuracy in predicting nitrate levels, with R² values ranging from 0.81 to 0.83 on the test dataset. We found that physical vulnerability dominated the groundwater system, with 52% and 57% of the area classified as medium to high risk based on the AHP-FR and MGWR methods, respectively. Anthropogenic vulnerability accounted for a smaller share, with 48% and 37% of the area rated as medium to high risk based on the AHP-FR and MGWR methods, respectively. However, the integration of hazard variables leads to divergent risk category distributions. When combining physical and anthropogenic vulnerabilities, the AHP-FR scheme identified 53% of the area as medium-to-high risk for physical vulnerability, 25% for anthropogenic vulnerability, and 47% for the combined effects. In contrast, the MGWR-based spatial rating scheme reported 10% lower for physical vulnerability, a 17% higher value for anthropogenic vulnerability, and a nearly unchanged estimate for the combined effects. It is worth noting that the MGWR method tends to rate areas as higher risk compared to the AHP-FR method during combined risk assessment (physical + anthropogenic). Validation results of area under the curve (AUC) show slight superiority for MGWR (78%) over AHP-FR (75%). This work underscores the importance of methodological discrimination in groundwater evaluation, assisting decision-makers in executing effective and sustainable groundwater management strategies.</p> Graphical Abstract <p>This work introduced the first effort to quantify physical and anthropogenic groundwater risks in MD’s multi-layered coastal aquifer system, significantly assisting decision-makers and water management authorities in understanding the region’s groundwater challenges related to natural and anthropogenic changes. In this study, we implemented multiscale geographically weighted regression (MGWR), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR) modeling approaches along with observational nitrate data to delineate and quantify groundwater risk. Ultimately, we identified high-risk zones for groundwater vulnerability, providing valuable insights for sustainable management of coastal aquifer system.</p>

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Quantifying Groundwater Risk Associated with Physical and Anthropogenic Vulnerabilities Across Coastal Aquifer of the Mekong Delta Using Data-driven Approach

  • 期刊论文

  • 发表日期：

    2025年8月23日

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  • Arfan Arshad,
  • Muhammad Umar Akbar,
  • Thanh Nhan Duc Tran,
  • Fazlullah Akhtar,
  • Md Masudur Rahman,
  • Dang An Tran,
  • Md Tajuddin Khan,
  • Trieu Anh Ngoc

摘要

The coastal aquifer of the Mekong Delta (MD) is facing groundwater challenges exacerbated by the joint impacts of anthropogenic activities and climate change. Groundwater sustainability in MD needs a clear understanding of both natural and human-related factors that cause risks, a knowledge that is currently lacking in MD. This study introduced a framework to separately quantify physical and anthropogenic groundwater risks in MD’s multi-layered coastal aquifer system. Leveraging a weighted overlay analysis, we combined thematic layers (e.g., distance to sea, distance to drain, aquifer thickness, groundwater level, extraction capacity etc.,) using multiscale geographically weighted regression (MGWR) model and observed groundwater nitrate data of 216 locations. Results were compared to traditional Analytical Hierarchy Process (AHP)-Frequency Ratio (FR) methods. The MGWR model demonstrated superior accuracy in predicting nitrate levels, with R² values ranging from 0.81 to 0.83 on the test dataset. We found that physical vulnerability dominated the groundwater system, with 52% and 57% of the area classified as medium to high risk based on the AHP-FR and MGWR methods, respectively. Anthropogenic vulnerability accounted for a smaller share, with 48% and 37% of the area rated as medium to high risk based on the AHP-FR and MGWR methods, respectively. However, the integration of hazard variables leads to divergent risk category distributions. When combining physical and anthropogenic vulnerabilities, the AHP-FR scheme identified 53% of the area as medium-to-high risk for physical vulnerability, 25% for anthropogenic vulnerability, and 47% for the combined effects. In contrast, the MGWR-based spatial rating scheme reported 10% lower for physical vulnerability, a 17% higher value for anthropogenic vulnerability, and a nearly unchanged estimate for the combined effects. It is worth noting that the MGWR method tends to rate areas as higher risk compared to the AHP-FR method during combined risk assessment (physical + anthropogenic). Validation results of area under the curve (AUC) show slight superiority for MGWR (78%) over AHP-FR (75%). This work underscores the importance of methodological discrimination in groundwater evaluation, assisting decision-makers in executing effective and sustainable groundwater management strategies.

Graphical Abstract

This work introduced the first effort to quantify physical and anthropogenic groundwater risks in MD’s multi-layered coastal aquifer system, significantly assisting decision-makers and water management authorities in understanding the region’s groundwater challenges related to natural and anthropogenic changes. In this study, we implemented multiscale geographically weighted regression (MGWR), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR) modeling approaches along with observational nitrate data to delineate and quantify groundwater risk. Ultimately, we identified high-risk zones for groundwater vulnerability, providing valuable insights for sustainable management of coastal aquifer system.