Process consistency in models: The importance of system signatures, expert knowledge, and process complexity
Process consistency in models: The importance of system signatures, expert knowledge, and process complexity
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DOI：
10.1002/2014wr015484
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发表时间：
2014-09
影响因子：
5.4
通讯作者：
M. Hrachowitz;O. Fovet;L. Ruiz;T. Euser;S. Gharari;R. Nijzink;J. Freer;H. Savenije;C. Gascuel-Odoux
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文献类型：
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作者：
M. Hrachowitz;O. Fovet;L. Ruiz;T. Euser;S. Gharari;R. Nijzink;J. Freer;H. Savenije;C. Gascuel-Odoux
水文模型经常遭受有限的预测能力，尽管有足够的校准性能。这可能表明底层过程的表示不充分。因此，寻求方法来增加模型的一致性，同时满足对比的优先级增加模型的复杂性和有限的等同性。在这项研究中，系统地使用水文特征和专家知识，以提高模型的一致性的价值进行了测试。结果发现，一个简单的概念模型，由四个校准目标函数的约束下，能够充分再现校准期间的过程线。然而，该模型无法再现一套水文特征，表明缺乏模型的一致性。随后，测试了11个模型，模型的复杂性以逐步的方式增加，并通过从专家知识中推断出的“先验约束”来平衡，以确保模型在建模者对系统的感知方面表现良好。我们发现，尽管校准性能不变，但最复杂的模型设置在独立测试期间表现出更高的性能，并且能够更好地再现所有测试签名，表明系统代表性更好。结果表明，尽管在多个校准目标方面表现良好，但模型可能是不够的，并且如果通过先前的约束来平衡，则增加模型的复杂性可以显着提高模型的预测性能及其重现水文特征的技能。这些结果有力地说明了需要平衡自动模型校准与更具专家知识驱动的约束模型策略。
Hydrological models frequently suffer from limited predictive power despite adequate calibration performances. This can indicate insufficient representations of the underlying processes. Thus, ways are sought to increase model consistency while satisfying the contrasting priorities of increased model complexity and limited equifinality. In this study, the value of a systematic use of hydrological signatures and expert knowledge for increasing model consistency was tested. It was found that a simple conceptual model, constrained by four calibration objective functions, was able to adequately reproduce the hydrograph in the calibration period. The model, however, could not reproduce a suite of hydrological signatures, indicating a lack of model consistency. Subsequently, testing 11 models, model complexity was increased in a stepwise way and counter‐balanced by “prior constraints,” inferred from expert knowledge to ensure a model which behaves well with respect to the modeler's perception of the system. We showed that, in spite of unchanged calibration performance, the most complex model setup exhibited increased performance in the independent test period and skill to better reproduce all tested signatures, indicating a better system representation. The results suggest that a model may be inadequate despite good performance with respect to multiple calibration objectives and that increasing model complexity, if counter‐balanced by prior constraints, can significantly increase predictive performance of a model and its skill to reproduce hydrological signatures. The results strongly illustrate the need to balance automated model calibration with a more expert‐knowledge‐driven strategy of constraining models.