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Revealing Causal Controls of Storage-Streamflow Relationships With a Data-Centric Bayesian Framework Combining Machine Learning and Process-Based Modeling by Tsai, Wen-Ping; Fang, Kuai; Ji, Xinye; Lawson, Kathryn; Shen, Chaopeng is a Environmental Science article available to read on EtoBox.

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Some machine learning (ML) methods such as classification trees are useful tools to generate hypotheses about how hydrologic systems function. However, data limitations dictate that ML alone often cannot differentiate between causal and associative relationships. For example, previous ML analysis suggested that soil thickness is the key physiographic factor determining the storage-streamflow correlations in the eastern US. This conclusion is not robust, especially if data are perturbed, and there were alternative, competing explanations including soil texture and terrain slope. However, typical causal analysis based on process-based models (PBMs) is inefficient and susceptible to human bias. Here we demonstrate a more efficient and objective analysis procedure where ML is first applied to generate data-consistent hypotheses, and then a PBM is invoked to verify these hypotheses. We employed a surface-subsurface processes model and conducted perturbation experiments to implement these competing hypotheses and assess the impacts of the changes. The experimental results strongly support the soil thickness hypothesis as opposed to the terrain slope and soil texture ones, which are co-va

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Author

Tsai, Wen-Ping; Fang, Kuai; Ji, Xinye; Lawson, Kathryn; Shen, Chaopeng

Publisher

Frontiers Media SA; Lausanne: Frontiers Media SA, [2019]- (ISSN 2624-9375)

Published

2020

Language

EN

Field

Environmental Science (Physical Sciences)