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A Framework for Uncertainty Quantification in One-Dimensional Plant Canopy Flow

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Abstract

Although the fidelity of computational-fluid-dynamics (CFD) models for the study of flow in plant canopies has significantly increased over the past decades, the inability to exactly measure the canopy structure and its material and physiological properties introduces a degree of uncertainty in model results that is often difficult to quantify. The present work addresses this problem by proposing a Bayesian uncertainty quantification (UQ) framework for evaluating the impact of uncertain canopy geometry on selected microscale flow statistics (the quantities of interest, QoIs, of the problem). The framework links available in-situ measurements of flow statistics to the uncertainty stemming from foliage spatial distribution and orientation, as well as from the aerodynamic plant response. The uncertainty is first characterized via a Markov chain Monte Carlo procedure, and then propagated to the QoIs through the Monte Carlo sampling method, which returns mean profiles and two-standard-deviation-(2SD-)intervals for the QoIs. The UQ framework relies on a one-dimensional CFD solver to simulate the flow over the Duke Forest, located near Durham, North Carolina, USA. Model results are compared against a standard deterministic solution in terms of mean velocity, Reynolds stress and turbulence-kinetic-energy profiles, as well as canopy aerodynamic parameters. For the considered QoIs, it is found that the 2SD-intervals obtained with the UQ procedure cover \(80\%\) of the experimental intervals, whereas the deterministic solution overlaps with only \(47 \%\) of them. Overall, this study highlights the potential of UQ to advance CFD capabilities for predicting exchange processes between realistic plant canopies and the surrounding atmosphere.

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Data availability

Scripts and datasets generated as part of this study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors acknowledge computing resources from Columbia University’s Shared Research Computing Facility project, which is supported by NIH Research Facility Improvement Grant 1G20RR030893-01, and associated funds from the New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR) Contract C090171, both awarded the 15th of April 2010. The authors are thankful to G. G. Katul and T. Duman for sharing the dataset and useful inputs on the manuscript, and to the LI-COR technical team for providing insight on the LAI-2000 instrument.

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  1. Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, USA

    Beatrice Giacomini & Marco G. Giometto

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  1. Beatrice Giacomini
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Correspondence to Marco G. Giometto.

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Giacomini, B., Giometto, M.G. A Framework for Uncertainty Quantification in One-Dimensional Plant Canopy Flow. Boundary-Layer Meteorol 184, 441–462 (2022). https://doi.org/10.1007/s10546-022-00718-5

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