Review of strategies for handling geological uncertainty in groundwater flow and transport modeling
Review of strategies for handling geological uncertainty in groundwater flow and transport modeling
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DOI：
10.1016/j.advwatres.2011.04.006
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发表时间：
2012-02
影响因子：
4.7
通讯作者：
J. Refsgaard;S. Christensen;T. Sonnenborg;Dorte Seifert;A. Højberg;L. Troldborg
中科院分区：
文献类型：
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作者：
J. Refsgaard;S. Christensen;T. Sonnenborg;Dorte Seifert;A. Højberg;L. Troldborg
地下水建模中与地质相关的不确定性有两个主要来源：地质结构和这些结构内的水力参数值。在地质结构要素内，参数值总是表现出局部尺度的异质性，在评估预测不确定性时可以考虑这一点，但常常被忽略。评估由地质相关不确定性引起的预测不确定性的策略可分为三个主要类别，考虑由于以下原因引起的不确定性： (a) 地质结构； (b) 有效模型参数； (c) 模型参数，包括局部尺度异质性。简要描述了每个类别中最常见的不确定性评估方法，例如多重建模、蒙特卡罗分析、回归分析和矩方程方法，并重点介绍了它们的关键特征。根据对先前研究的回顾，评估了三个不确定性类别对于不同类型模型预测的相对重要性。此外，还讨论了这些方法的优点、局限性和相互作用，并就确定需要进一步研究的关键主题得出结论。当通过探索几个可能的地质模型结构的模型参数和局部尺度异质性不确定性来分析所有不确定性来源时，可以使用模型平均技术（例如贝叶斯模型平均（BMA））来评估联合不确定性。讨论了模型平均中关于选择互斥和集体详尽的选择模型的一般挑战，以及当模型超出其校准基础使用时分配权重。
The geologically related uncertainty in groundwater modeling originates from two main sources: geological structures and hydraulic parameter values within these structures. Within a geological structural element the parameter values will always exhibit local scale heterogeneity, which can be accounted for, but is often neglected, in assessments of prediction uncertainties. Strategies for assessing prediction uncertainty due to geologically related uncertainty may be divided into three main categories, accounting for uncertainty due to: (a) the geological structure; (b) effective model parameters; and (c) model parameters including local scale heterogeneity. The most common methodologies for uncertainty assessments within each of these categories, such as multiple modeling, Monte Carlo analysis, regression analysis and moment equation approach, are briefly described with emphasis on their key characteristics. Based on reviews of previous studies, assessments are made on the relative importance of the three uncertainty categories for different types of model predictions. Furthermore, the strengths, limitations and interactions of these methodologies are discussed and conclusions are made with respect to identifying key subjects for which further research is needed. When all sources of uncertainty are analyzed by exploring model parameter and local scale heterogeneity uncertainty for several plausible geological model structures the joint uncertainties can be assessed by use of model averaging techniques, such as Bayesian Model Averaging (BMA). General challenge in model averaging with respect to choosing mutually exclusive and collectively exhaustive choice models, as well as to assign weights when models are used beyond their calibration base, are discussed.