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Model uncertainty - parameter uncertainty versus conceptual models

机译:模型不确定性-参数不确定性与概念模型

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Uncertainties in model structures have been recognised often to be the main source of uncertainty in predictive model simulations. Despite this knowledge, uncertainty studies are traditionally limited to a single deterministic model and the uncertainty addressed by a parameter uncertainty study. The extent to which a parameter uncertainty study may encompass model structure errors in a groundwater model is studied in a case study. Three groundwater models were constructed on the basis of three different hydrogeological interpretations. Each of the models was calibrated inversely against groundwater heads and streamflows. A parameter uncertainty analysis was carried out for each of the three conceptual models by Monte Carlo simulations. A comparison of the predictive uncertainties for the three conceptual models showed large differences between the uncertainty intervals. Most discrepancies were observed for data types not used in the model calibration. Thus uncertainties in the conceptual models become of increasing importance when predictive simulations consider data types that are extrapolates from the data types used for calibration.
机译:在预测模型仿真中,模型结构的不确定性通常被认为是不确定性的主要来源。尽管有这些知识,但不确定性研究传统上仅限于单个确定性模型,而不确定性则由参数不确定性研究解决。在案例研究中,研究了参数不确定性研究可能涵盖地下水模型中模型结构误差的程度。在三种不同的水文地质解释的基础上,构建了三种地下水模型。每个模型都针对地下水压头和水流进行了反标定。通过蒙特卡洛模拟对三个概念模型中的每一个进行了参数不确定性分析。对三个概念模型的预测不确定性进行比较,发现不确定性区间之间存在较大差异。对于模型校准中未使用的数据类型,观察到大多数差异。因此,当预测模拟考虑从用于校准的数据类型推断出的数据类型时,概念模型中的不确定性变得越来越重要。

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