Managed Aquifer Recharge Operations and Design under Hydrologic Uncertainty.

Authors

Wolfgang Schmid, Adrian Dela Cruz, Adam Siade, Michael Donn, Olga Barron

Published in

Ground water. Oct 05, 2026. Epub Oct 05, 2026.

Abstract

As climate change intensifies, managed aquifer recharge (MAR) can enhance water security and strengthen drought resilience by capturing surface water, such as drain flows. Assessments of MAR impact and risks to groundwater-dependent receptors require numerical modeling. Yet many studies do not account for contrasting climatic conditions, surface-water and groundwater integration, and dual ensembles of MAR operations and of hydrologic/landscape parameters when quantifying uncertainty using the iterative ensemble smoother (IES) as done here for a proposed MAR in the Myalup region of Western Australia. For each MAR scenario, decision metrics-groundwater mounding, inundation and waterlogging, travel times to environmental receptors-were evaluated across the parameter ensemble. Spatial analysis from a likely IES realization showed drain discharge constraining mound expansion, recovery mitigating small increases in drain flow, localized inundation risk, and MAR plumes reaching downgradient wetlands within two decades. Predictive uncertainty of MAR scenarios varied substantially, with climate conditions as dominant control. Wettest and driest cases bracket divertible flow conditions. Inundation risk was greater for wet than dry scenarios, suggesting drier climates may reduce MAR impacts, although wet extremes must be accommodated. Recovery reduced mounding but barely altered transport pathways. Relocating MAR away from a drain lowered uncertainty and environmental impacts. MAR designs can be selected by minimizing probabilities of exceeding stakeholder-defined thresholds or balancing trade-offs between impact metrics. This dual-ensemble methodology of MAR scenarios and IES parameters provides robust and transferable uncertainty quantification for evidence-based MAR planning under hydrologic uncertainty and climatic extremes.

PMID:
42831856
Bibliographic data and abstract were imported from PubMed on 05 Oct 2026.

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