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Updated: Jun 12, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

[Groundwater pollution risk mapping method].

Li-na Shen¹, Guang-he Li

¹State Key Joint Laboratory for Environmental Simulation and Pollution Control, Department of Environmental Science & Technology, Tsinghua University, Beijing 100084, China. shenlina02@mail.tsinghua.org.cn

Huan Jing Ke Xue= Huanjing Kexue

|June 10, 2010

Summary

This summary is machine-generated.

This study introduces integrated models to map groundwater pollution risk by combining intrinsic vulnerability with contaminant sources. The approach effectively identifies high-risk areas, aiding groundwater pollution supervision.

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Published on: December 9, 2012

Area of Science:

Hydrogeology
Environmental Science
Geospatial Analysis

Context:

Current groundwater vulnerability assessments often neglect contamination source elements.
Existing groundwater pollution risk mapping techniques lack systemic approaches and parameterization.
Karst groundwater systems present unique challenges for vulnerability and risk assessment.

Purpose:

To develop integrated multi-index models for groundwater pollution risk mapping.
To couple groundwater intrinsic vulnerability with contaminant source characteristics.
To address the lack of effective techniques for pollution risk assessment.

Summary:

Developed integrated multi-index models by analyzing groundwater system structure and contaminant source characteristics.
Coupled groundwater intrinsic vulnerability with contaminant sources to evaluate pollution risk.
Applied the models to a large-scale karst groundwater source in northern China for case study.

Impact:

The study successfully identified high-risk groundwater pollution regions by overlaying vulnerability assessment with risk pollution sources.
The developed methods provide crucial support for effective groundwater pollution supervision.
Demonstrated the utility of integrated models for comprehensive groundwater risk management.