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Updated: Jul 31, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Improved grey water footprint model based on uncertainty analysis.

Juan Li^1,2, Ma Lin³, Yan Feng⁴

¹School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha, 410114, China.

Scientific Reports

|May 2, 2023

Summary

An improved grey water footprint (GWF) model addresses uncertainty in pollutant thresholds for better water resource management. This new method enhances pollution risk assessment and evaluation accuracy.

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Area of Science:

Environmental Science
Water Resource Management
Ecological Modeling

Background:

Conventional grey water footprint (GWF) models struggle with the inherent uncertainty in allowable pollutant thresholds.
Effective water resource management requires accounting for variability in pollution control standards.

Purpose of the Study:

To develop an improved GWF model and pollution risk evaluation method that incorporates uncertainty analysis.
To enhance the assessment of water pollution by considering the stochastic nature of pollutant thresholds.

Main Methods:

Utilized uncertainty analysis theory and the maximum entropy principle to refine the GWF model.
Defined GWF as the mathematical expectation of virtual water needed to dilute pollution loads within uncertain thresholds.
Deduced pollution risk based on the probability of GWF exceeding local water resources.

Main Results:

Applied the improved model to Jiangxi Province, China (2013-2017), calculating annual GWF and pollution risk grades.
Observed varying GWF values and moderate to low pollution risk grades, with Total Phosphorus (TP) and Total Nitrogen (TN) identified as key determinants.
Demonstrated consistency between the improved GWF model's results and the Water Quality and Quantity Regulation (WQQR).

Conclusions:

The improved GWF model effectively handles uncertainty in pollutant thresholds, offering a more robust water resource evaluation method.
This advanced model shows superior capabilities in identifying pollution grades and recognizing pollution risks compared to conventional GWF models.
The study highlights the importance of accounting for threshold uncertainty in water pollution assessment and management strategies.