Groundwater Modeling with Nonlinear Uncertainty Analyses to Enhance Remediation Design Confidence

Brouwers, Martinus; Martin, Paul J.; Abbey, Daron G.; White, Jeremy T.

doi:10.1111/gwat.12669

Cited by 2 publications

(2 citation statements)

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“…The case study contributes to the small body of real-world decision support worked examples that are currently available in the international literature (e.g., Kunstmann et al, 2002;Enzenhoefer et al, 2014;Sepúlveda and Doherty, 2015;Brouwers et al, 2018;Sundell et al, 2019;White et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Early Uncertainty Quantification for an Improved Decision Support Modeling Workflow: A Streamflow Reliability and Water Quality Example

2020

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Effective decision making for resource management is often supported by combining predictive models with uncertainty analyses. This combination allows quantitative assessment of management strategy effectiveness and risk. Typically, history matching is undertaken to increase the reliability of model forecasts. However, the question of whether the potential benefit of history matching will be realized, or outweigh its cost, is seldom asked. History matching adds complexity to the modeling effort, as information from historical system observations must be appropriately blended with the prior characterization of the system. Consequently, the cost of history matching is often significant. When it is not implemented appropriately, history matching can corrupt model forecasts. Additionally, the available data may offer little decision-relevant information, particularly where data and forecasts are of different types, or represent very different stress regimes. In this paper, we present a decision support modeling workflow where early quantification of model uncertainty guides ongoing model design and deployment decisions. This includes providing justification for undertaking (or forgoing) history matching, so that unnecessary modeling costs can be avoided and model performance can be improved. The workflow is demonstrated using a regional-scale modeling case study in the Wairarapa Valley (New Zealand), where assessments of stream depletion and nitrate-nitrogen contamination risks are used to support water-use and land-use management decisions. The probability of management success/failure is assessed by comparing the proximity of model forecast probability distributions to ecologically motivated decision thresholds. This study highlights several important insights that can be gained by undertaking early uncertainty quantification, including: i) validation of the prior numerical characterization of the system, in terms of its consistency with historical observations; ii) validation of model design or indication of areas of model shortcomings; iii) evaluation of the relative proximity of management decision thresholds to forecast probability distributions, providing a justifiable basis for stopping modeling.

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Section: Introductionmentioning

confidence: 99%

Early Uncertainty Quantification for an Improved Decision Support Modeling Workflow: A Streamflow Reliability and Water Quality Example

2020

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“…Groundwater quality remains a critically important problem impacting the worlds water resources. Several papers explore better ways to understand the progress of remediation efforts through improved confidence (Brouwers et al ), data assimilation (Ross and Andersen ), and the use of non‐Fickian transport techniques (Burnell et al ). Groundwater transit time links all these concepts together and Abrams and Haitjema () explore the role that aquifer geometry plays in water age and flow paths.…”

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confidence: 99%