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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >Balancing multiple constraints in model-data integration: Weights and the parameter block approach
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Balancing multiple constraints in model-data integration: Weights and the parameter block approach

机译：平衡模型数据集成中的多个约束：权重和参数块方法

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摘要

Model data integration (MDI) studies are key to parameterize ecosystem models that synthesize our knowledge about ecosystem function. The use of diverse data sets, however, results in strongly imbalanced contributions of data streams with model fits favoring the largest data stream. This imbalance poses new challenges in the identification of model deficiencies. A standard approach for balancing is to attribute weights to different data streams in the cost function. However, this may result in overestimation of posterior uncertainty. In this study, we propose an alternative: the parameter block approach. The proposed method enables joint optimization of different blocks, i.e., subsets of the parameters, against particular data streams. This method is applicable when specific parameter blocks are related to processes that are more strongly associated with specific observations, i.e., data streams. A comparison of different approaches using simple artificial examples and the DALEC ecosystem model is presented. The unweighted inversion of a DALEC model variant, where artificial structural errors in photosynthesis calculation had been introduced, failed to reveal the resulting biases in fast processes (e.g., turnover). The posterior bias emerged only in parameters related to slower processes (e.g., carbon allocation) constrained by fewer data sets. On the other hand, when weighted or blocked approaches were used, the introduced biases were revealed, as expected, in parameters of fast processes. Ultimately, with the parameter block approach, the transfer of model error was diminished and at the same time the overestimation of posterior uncertainty associated with weighting was prevented.
机译：模型数据集成（MDI）研究对于参数化综合我们对生态系统功能的知识的生态系统模型至关重要。但是，使用不同的数据集会导致数据流的严重失衡，而模型拟合则有利于最大的数据流。这种不平衡给识别模型缺陷提出了新的挑战。平衡的标准方法是将权重分配给成本函数中的不同数据流。但是，这可能导致对后验不确定性的高估。在这项研究中，我们提出了一种替代方法：参数块方法。所提出的方法使得能够针对特定数据流联合优化不同块，即参数的子集。当特定参数块与与特定观察值（即数据流）更紧密相关的过程相关时，此方法适用。使用简单的人工示例和DALEC生态系统模型比较了不同方法。 DALEC模型变体的未加权反演（已在光合作用计算中引入人为结构错误）未能揭示快速过程中产生的偏差（例如周转率）。后偏仅出现在与较少数据集约束的较慢过程（例如碳分配）有关的参数中。另一方面，当使用加权或阻塞方法时，正如预期的那样，在快速过程的参数中会发现引入的偏差。最终，通过参数块方法，减少了模型误差的传递，同时避免了过高估计与加权相关的后验不确定性。

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