We present a framework for design and deployment of decision support modeling based on metrics which have their roots in the scientific method. Application of these metrics to decision support modeling requires recognition of the importance of data assimilation and predictive uncertainty quantification in this type of modeling. The difficulties of implementing these procedures depend on the relationship between data that is available for assimilation and the nature of the prediction(s) that a decision support model is required to make. Three different data/prediction contexts are identified. Unfortunately, groundwater modeling is generally aligned with the most difficult of these. It is suggested that these difficulties can generally be ameliorated through appropriate model design. This design requires strategic abstraction of parameters and processes in a way that is optimal for the making of one particular prediction but is not necessarily optimal for the making of another. It is further suggested that the focus of decision support modeling should be on the ability of a model to provide receptacles for decision‐pertinent information rather than on its purported ability to simulate environmental processes. While models are compromised in both of these roles, this view makes it clear that simulation should serve data assimilation and not the other way around. Data assimilation enables the uncertainties of decision‐critical model predictions to be quantified and maybe reduced. Decision support modeling requires this.

中文翻译：

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决策支持建模：数据同化，不确定性量化和战略抽象

我们提出了一个基于度量的设计和部署决策支持模型的框架，这些度量源于科学方法。将这些指标应用于决策支持建模需要认识到在这种类型的建模中数据同化和预测不确定性量化的重要性。实施这些程序的困难取决于可用于同化的数据与需要决策支持模型做出的预测的性质之间的关系。确定了三种不同的数据/预测上下文。不幸的是，地下水模型通常与最困难的模型相吻合。建议通过适当的模型设计可以缓解这些困难。这种设计需要以一种对于进行一个特定预测最佳的方式对参数和过程进行战略性抽象，而对于进行另一种预测却不一定是最佳的。进一步建议，决策支持建模的重点应该放在模型为决策相关信息提供容器的能力上，而不是在其声称的模拟环境过程的能力上。尽管模型在这两个角色中都受到影响，但这种观点清楚地表明，仿真应该为数据同化服务，而不是相反。数据同化可以量化并减少决策关键模型预测的不确定性。决策支持建模需要这样做。