A Multi Scaling Framework of Airflow Canopy Interaction скачать в хорошем качестве

A Multi Scaling Framework of Airflow Canopy Interaction

ABSTRACT: Wind behaviors within vegetation canopies critically regulate the exchange of mass, momentum, and energy between the land surface and atmosphere, thereby influencing microclimatic conditions across a wide range of scales. These dynamics govern temperature and humidity control, gas exchange, nutrient transport, and numerous biological and ecological mechanisms. However, the inherent multiscale heterogeneity in natural systems poses substantial challenges to accurately modeling canopy flows. Atmospheric models, particularly those at the global climate model (GCM) scale, typically rely on one-dimensional (1D) parameterizations—such as Monin-Obukhov similarity theory and 1D analytic profiles—that assume isotropy and spatial homogeneity in canopy distributions. Similarly, even in high-resolution modeling based on the large-eddy simulation at the microclimate scale, vegetation structure is often simplified through locally homogeneous assumptions. While such simplifications may be acceptable in regions far from canopies (i.e., far-field modeling), they become unreliable in the vicinity of canopies where most mass and energy exchanges occur. Moreover, when the scale separation is weak, the deformation (stretching and compression) of flows induced by micro-scale heterogeneity can significantly affect macroscopic phenomena. Although progress has been limited by model complexity and data constraints, understanding the influence of heterogeneity on canopy flows remains a critical direction for future research. To address this challenge, the present study introduces a multiscale modeling framework capable of resolving canopy flows across heterogeneous canopy distributions spanning multiple scales. Through a systematic multi-step upscaling—from leaf scale to GCM scale—the model comprises three sub-models corresponding to micro-, meso-, and macro-scales. Upscaling is rigorously conducted using volume-averaging theory, and machine learning techniques are employed at each scale transition to incorporate sub-grid-scale information into the coarser-scale models. In particular, the mesoscale model, which bridges the micro- and macroscale processes, leverages the concept of porosity to explicitly distinguish between sub-grid and resolvable scales. This design minimizes contamination of parameterizations arising from the scale mixing. To ensure stable and accurate solutions even under highly heterogeneous or discontinuous porosity distributions, a robust discontinuity-resolving solver is developed. The numerical scheme is formulated in an explicit form without iterative procedures, enabling future extension to differentiable models for machine learning-based applications. It is hoped that this study will ultimately contribute to improved parameterizations of atmosphere–vegetation interactions in GCMs.