How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.
How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.
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DOI：
10.1021/acs.est.2c02023
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发表时间：
2022-09-20
影响因子：
11.4
通讯作者：
Fitton, Nuala
中科院分区：
文献类型：
--
作者：
Albanito, Fabrizio;McBey, David;Harrison, Matthew;Smith, Pete;Ehrhardt, Fiona;Bhatia, Arti;Bellocchi, Gianni;Brilli, Lorenzo;Carozzi, Marco;Christie, Karen;Doltra, Jordi;Dorich, Christopher;Doro, Luca;Grace, Peter;Grant, Brian;Leonard, Joel;Liebig, Mark;Ludemann, Cameron;Martin, Raphael;Meier, Elizabeth;Meyer, Rachelle;Migliorati, Massimiliano De Antoni;Myrgiotis, Vasileios;Recous, Sylvie;Sandor, Renata;Snow, Val;Soussana, Jean-Francois;Smith, Ward N.;Fitton, Nuala
关键词：
人们越来越认识到，模型集合研究的复杂性不仅取决于所使用的模型，还取决于模型师校准和验证结果所使用的经验和方法，这仍然是不确定性的一个来源。在这里，我们应用多准则决策方法来调查模型师在模型集成研究中应用的基本原理，该研究在五个连续的校准阶段对12种基于过程的不同生物地球化学模型类型进行了比较。对于用于初始化他们的模型以进行校准的变量的重要性，建模师们有着共同的共识。然而，我们发现，在判断不同校准阶段输入变量的重要性时，建模者之间存在不一致。随着所提供变量的范围和数量的增加，建模人员对校准数据的主观权重水平依次降低。在这种情况下，根据模型类型进行分类时，诸如施肥量、灌溉制度、土壤质地、pH以及土壤有机碳和氮储存的初始水平等变量所赋予的重要性在统计上是不同的。实验持续时间、初级生产总值和净生态系统交换等输入变量的重要性因建模者经验的长短而显著不同。我们认为，逐步获得五个校准阶段的输入数据对建模者做出的解释的一致性产生了负面影响，在“试错”校准程序中存在认知偏差。我们的研究强调，忽视人类和社会属性在建模和模型相互比较研究的结果中是至关重要的。虽然在模型算法和参数化中捕获的过程的复杂性很重要，但我们认为(1)建模者对参数应该改变的程度的假设和(2)建模者对模型参数重要性的认识对于获得质量模型校准与数值或分析细节一样重要。这项研究概述了在模型校准中使用的变量的优先顺序的主观不一致，以及对建模和模型相互比较研究的结果的影响。
There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details. This study outlines subjective inconsistencies in the prioritization of variables used in model calibration, with implications in the outcomes of modeling and model intercomparison studies.
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影响因子：
11.6
作者：
Harrison, Matthew Tom;Cullen, Brendan Richard;Mayberry, Dianne Elizabeth;Cowie, Annette Louise;Bilotto, Franco;Badgery, Warwick Brabazon;Liu, Ke;Davison, Thomas;Christie, Karen Michelle;Muleke, Albert;Eckard, Richard John
通讯作者：
Eckard, Richard John
影响因子：
6.6
作者：
Christie, K. M.;Smith, A. P.;Eckard, R. J.
通讯作者：
Eckard, R. J.
影响因子：
4.9
作者：
Holzworth, Dean P.;Huth, Neil I.;Keating, Brian A.
通讯作者：
Keating, Brian A.
影响因子：
5.2
作者：
Haas, Edwin;Klatt, Steffen;Butterbach-Bahl, Klaus
通讯作者：
Butterbach-Bahl, Klaus
影响因子：
4.9
作者：
Harrison, Matthew Tom;Roggero, Pier Paolo;Zavattaro, Laura
通讯作者：
Zavattaro, Laura