Abstract
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.
Highlights
Numerical models are routinely used to inform environmental management decision making by exploring possible system responses to proposed management strategies
This follows the philosophy “How can a model be robust if it isn’t calibrated?” This philosophy has its basis in the expectation that history matching, which can be considered an implementation of Bayes equation, will result in a reduction of parameter and predictive uncertainty
In this paper we explore the benefits of recasting the typical modeling workflow, which starts with a conceptual system model and ends with a calibrated numerical model (e.g., Barnett et al, 2012), such that uncertainty quantification is undertaken at an early stage in the project, before attempting comprehensive history matching
Summary
Numerical models are routinely used to inform environmental management decision making by exploring possible system responses to proposed management strategies Probabilistic assessment of these system responses are a further requirement of model-based decision support (e.g., Freeze et al, 1990; Doherty and Simmons, 2013). It is widely considered that history matching ( known as “model calibration” or “data assimilation”) is a prerequisite for such decision support model deployment (e.g., Barnett et al, 2012) This follows the philosophy “How can a model be robust if it isn’t calibrated?” This philosophy has its basis in the expectation that history matching, which can be considered an implementation of Bayes equation, will result in a reduction of parameter and predictive uncertainty (often expressed in terms of predictive variance; e.g., Moore and Doherty, 2005; Dausman et al, 2010). The ability of the history matching process to improve the reliability of parameter estimations, and to appropriately reduce decision-relevant forecast uncertainty, may be limited by a number of important factors
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