MAR2PROTECT consortium advances AI decision support for managed aquifer recharge

Overview

Managed aquifer recharge (MAR) is an increasingly important tool for securing regional water supplies, particularly in water-stressed areas. However, MAR operations carry significant environmental liability if source water quality changes unexpectedly and contaminants are introduced into receiving aquifers. The international MAR2PROTECT consortium has recently detailed its new M-AI-R Decision Support System at the TIAC 2026 symposium. This AI-driven platform integrates real-time sensor data with machine learning algorithms to predict contamination risks before regulatory thresholds are breached, offering a step change in how MAR schemes are designed, operated, and regulated.

Key details

The M-AI-R Decision Support System represents a significant advance over conventional MAR monitoring approaches. The key technical features include:

• Real-time sensor integration: The system connects to continuous water quality monitoring networks deployed at MAR sites. Sensors measure parameters including turbidity, electrical conductivity, pH, dissolved oxygen, and specific contaminant concentrations. This replaces the traditional reliance on periodic grab sampling, which can miss short-duration contamination events.
• Predictive algorithms: Machine learning models are trained on historical water quality data from the source water, the recharge infrastructure, and the receiving aquifer. These models identify patterns and trends that precede contamination events, enabling operators to take preventive action before a breach occurs.
• Decision support interface: The system provides operators with clear, actionable alerts and recommendations. When the model predicts an elevated risk, it can recommend reducing recharge rates, diverting source water, or initiating additional treatment steps.
• Adaptive learning: The AI models continuously update as new data is collected, improving their predictive accuracy over time. This is particularly valuable for MAR sites where hydrogeological conditions evolve as recharge operations modify the aquifer system.

The consortium includes research institutions and industry partners from across Europe, and the technology has been piloted at several MAR sites in Spain, Portugal, and Germany.

Australian context

Australia has a well-established framework for managed aquifer recharge. The National Water Quality Management Strategy (NWQMS) provides the overarching guidance, while the Australian Guidelines for Water Recycling: Managed Aquifer Recharge (2009) set out the specific requirements for MAR scheme design, operation, and monitoring. State-level regulations add further detail:

• New South Wales: The Aquifer Interference Policy requires proponents to demonstrate that MAR activities will not cause “more than minimal harm” to aquifer water quality and connected water sources.
• Victoria: Environmental Reference Standards (ERS) for groundwater set quality objectives that must be maintained during and after recharge operations.
• South Australia: As the state with the longest history of MAR implementation, SA’s regulatory framework is well developed, with the EPA providing specific guidance on monitoring requirements.
• Western Australia: The Department of Water and Environmental Regulation oversees MAR operations, with particular focus on protecting drinking water source areas.

Australian MAR schemes are expanding rapidly, driven by population growth, climate variability, and the need to diversify water supply portfolios. Significant schemes operate in the Adelaide metropolitan area, Perth’s Gnangara Mound, and various locations across regional Queensland. The integration of AI-driven decision support tools into these operations could strengthen regulatory compliance and reduce the environmental risks associated with unexpected changes in source water quality.

Practical implications

For groundwater consultants and water utility operators, the M-AI-R system highlights several practical considerations:

• Enhanced regulatory submissions: Incorporating predictive monitoring capabilities into MAR scheme proposals could strengthen applications for regulatory approval by demonstrating a higher level of environmental protection.
• Operational risk reduction: Real-time contamination prediction allows operators to halt or modify recharge activities immediately, preventing contaminant migration into receiving aquifers and avoiding costly remediation.
• Insurance and liability: Demonstrating the use of advanced monitoring and decision support systems may assist operators in managing liability and insurance requirements associated with MAR operations.
• Data infrastructure: Implementing AI-driven decision support requires robust sensor networks, reliable data telemetry, and appropriate data management systems. Consultants should factor these requirements into MAR scheme design and costing.

References and related sources

• MAR2PROTECT consortium
• Australian Guidelines for Water Recycling: Managed Aquifer Recharge
• NSW Aquifer Interference Policy

How iEnvi can help

iEnvi’s contaminated land and groundwater specialists provide expert advice on managed aquifer recharge, groundwater quality assessment, and regulatory compliance. Our team has experience working with state regulators across Australia on groundwater-related approvals. For sites where contamination risks need to be managed as part of a MAR scheme, our remediation specialists can design appropriate treatment and monitoring solutions. Contact iEnvi to discuss your groundwater and MAR project requirements.

This is an iEnvi Machete news summary. Prepared by iEnvi to summarise the source article for contaminated land, groundwater, remediation, approvals and site risk professionals.

Need advice on this topic? Speak to an iEnvi expert at hello@ienvi.com.au or 1300 043 684, or contact us online.