Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data
Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data
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DOI：
10.1029/2020wr027948
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发表时间：
2021-06-01
影响因子：
5.4
通讯作者：
Savenije, Hubert H. G.
中科院分区：
文献类型：
--
作者：
Gharari, Shervan;Gupta, Hoshin V.;Savenije, Hubert H. G.
基于过程的水文模型试图代表流域中的主要水文过程。然而，由于不可避免的知识的不完全性，“fidelius”基于过程的模型的构建在很大程度上依赖于专家的判断。我们提出了一个系统的方法，将模型视为假设（守恒原理，系统架构，过程参数化方程和参数规范）的层次组合，这使得研究模型开发决策的层次结构如何影响模型的保真度。每个模型开发步骤提供的信息，逐步改变我们的不确定性（增加，减少或改变）关于系统的输入-状态-输出行为。遵循最大熵的原则，我们引入了“最低限度的限制过程参数化方程-MR-PPE”的概念，这使我们能够提高系统过程可以表示的灵活性，从而研究系统架构假设（将系统离散成子系统元素）在确定模型行为中所起的重要作用。我们用合成和真实数据研究来说明和探索这些概念，使用从简单的通用桶构建的模型作为构建模块，从而为使用复杂的基于过程的水文模型进行更详细的调查铺平了道路。我们还讨论了如何提出MR-PPE可以弥合当前基于过程的建模和机器学习之间的差距。最后，我们建议模型校正需要从“参数空间”的搜索发展到“函数空间”的搜索。"
Process-based hydrological models seek to represent the dominant hydrological processes in a catchment. However, due to unavoidable incompleteness of knowledge, the construction of "fidelius" process-based models depends largely on expert judgment. We present a systematic approach that treats models as hierarchical assemblages of hypotheses (conservation principles, system architecture, process parameterization equations, and parameter specification), which enables investigating how the hierarchy of model development decisions impacts model fidelity. Each model development step provides information that progressively changes our uncertainty (increases, decreases, or alters) regarding the input-state-output behavior of the system. Following the principle of maximum entropy, we introduce the concept of "minimally restrictive process parameterization equations-MR-PPEs," which enables us to enhance the flexibility with which system processes can be represented, and to thereby investigate the important role that the system architectural hypothesis (discretization of the system into subsystem elements) plays in determining model behavior. We illustrate and explore these concepts with synthetic and real-data studies, using models constructed from simple generic buckets as building blocks, thereby paving the way for more-detailed investigations using sophisticated process-based hydrological models. We also discuss how proposed MR-PPEs can bridge the gap between current process-based modeling and machine learning. Finally, we suggest the need for model calibration to evolve from a search over "parameter spaces" to a search over "function spaces."