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Updated: May 16, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Stochastic hydro-economic model for groundwater quality management using Bayesian networks.

José-Luis Molina¹, Manuel Pulido-Velázquez, Carlos Llopis-Albert

¹Department of Hydraulic Engineering, Salamanca University, High Polytechnic School of Engineering Avila, Av. de los Hornos Caleros, 50, 05003 Ávila, Spain. jlmolina@usal.es

Water Science and Technology : a Journal of the International Association on Water Pollution Research

|December 4, 2012

Summary

This study developed a Bayesian network model to manage groundwater quality and nitrate pollution under uncertainty. The hydro-economic model aids water managers in balancing competing objectives for sustainable water resource management.

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Last Updated: May 16, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Environmental Science
Water Resource Management
Hydrogeology

Background:

European water policies, including the Nitrate Directive and Water Framework Directive (WFD), mandate good water status.
Assessing impacts of water policies to reduce groundwater nitrate pollution requires balancing conflicting objectives.
Intensive groundwater irrigation in Spain's El Salobral-Los Llanos aquifer has led to declining water levels and river flow.

Purpose of the Study:

To design a hydro-economic model for assessing groundwater quality control under uncertainty.
To support water managers in decision-making for nitrate pollution reduction.
To evaluate the effectiveness of water policies in achieving environmental and economic goals.

Main Methods:

Development of an annual lumped probabilistic model using Bayesian networks (BNs).
Integration of diverse data sources: groundwater flow and mass transport simulations, hydro-economic models, stakeholder input, and expert opinion.
Application of the BN model to the large El Salobral-Los Llanos aquifer in Spain's Júcar River Basin.

Main Results:

The Bayesian network model effectively integrates various data streams for groundwater quality assessment.
The model provides a robust framework for hydro-economic analysis of nitrate pollution control strategies.
Demonstrated utility as a Decision Support System for water resource managers facing complex challenges.

Conclusions:

Bayesian networks offer a powerful tool for managing groundwater quality under uncertain conditions.
The developed model aids in balancing competing water management objectives, crucial for regulatory compliance (e.g., WFD).
This approach supports sustainable groundwater resource management in regions with intensive agricultural demands.