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

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.