DataSheet6_v1_Early Uncertainty Quantification for an Improved Decision Support Modeling Workflow: A Streamflow Reliability and Water Quality Example.PDF

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.

资源管理的有效决策往往依托预测模型与不确定性分析的结合来实现。该组合可实现管理策略有效性与风险的量化评估。通常而言，研究人员会通过开展历史匹配（history matching）以提升模型预测的可靠性。然而，鲜有研究探讨历史匹配的潜在效益是否能够兑现，抑或是其效益能否覆盖实施成本这一问题。历史匹配会增加建模工作的复杂度，因为需要将系统历史观测得到的信息，与系统先验特征表征进行合理融合。因此，历史匹配的实施成本往往较高。若实施方式不当，历史匹配反而可能破坏模型的预测结果。此外，现有数据可能几乎无法提供与决策相关的信息，尤其当数据与预测类型各异，或是二者所表征的胁迫状态差异极大时。本文提出了一套决策支持建模工作流，其中对模型不确定性的早期量化可指导后续的模型设计与部署决策。该工作流包括为开展（或放弃）历史匹配提供依据，从而规避不必要的建模成本并提升模型性能。本工作流通过新西兰怀拉拉帕谷（Wairarapa Valley）的区域尺度建模案例研究进行演示，该案例以溪流枯竭与硝态氮污染风险评估为支撑，助力用水与土地利用管理决策。通过对比模型预测概率分布与基于生态学目标设定的决策阈值的接近程度，可评估管理方案成功或失败的概率。本研究揭示了开展早期不确定性量化可获得的多项重要结论，具体包括：i）结合历史观测数据的一致性，验证系统的先验数值特征；ii）验证模型设计合理性，或是指出模型存在缺陷的领域；iii）评估管理决策阈值与预测概率分布的相对接近程度，为终止建模提供合理依据。