The Effects of Canopy Morphology on Flow Over a Two‐Dimensional Isolated Ridge

Abstract

Momentum and mass exchanges between the atmosphere and forests situated on complex terrain continue to draw significant research attention primarily because of their significance to a plethora of applications. In this paper, we investigated flows behavior on the leeward side of a twodimensional forested ridge under neutrally stratified conditions using large-eddy simulations (LESs). The goal is to understand how variations in leaf area index (LAI), vertical canopy foliage distributions, and forest edge positions affect mean/turbulent flow statistics, momentum fluxes, and onset of recirculation patterns. Although pressure perturbations are dominated by the hill shape, it is demonstrated here that changes in canopy foliage distribution modulate intensities and patterns of the leeward adverse pressure gradients. Such changes in the adverse pressure gradients alter the mean velocity streamlines including the patterns and magnitudes of the leeward downward mean vertical velocity and the velocity variances and momentum flux in the wake region. While a downwind recirculation zone develops in all cases, the details regarding the incipient location and recirculation zone size vary including positions of the separation and reattachment points. Furthermore, changes in the strength and depth of the zone occur due to canopyinduced changes in adverse pressure gradients, advection, and canopy drag. Because the recirculation zone impacts the local mean advective terms in momentum and scalar exchanges, the simulations here indicate that canopy morphology-induced changes in the leeward flows have significant implications to both measurements and models of biosphere-atmosphere exchange over complex terrain.

Large-Eddy Simulation of Wind Turbine Wakes in Forest Terrain

“…where h = 20 m is the canopy height and α(z) is the vertical LAD profile data. The three distributions of the LAD profiles are shown in Figure 3, which are similar with those employed by Ma et al [59]. The considered LAI are LAI = 4.25, 2.8, and 1.4 for the dense, sparse, and very sparse forest cases, respectively [32,57].…”

A Surface Flux Estimation Scheme Accounting for Large‐Eddy Effects for Land Surface Modeling

“…In addition, we use the same LES model, that is, LES of the Weather Research and Forecasting model (WRF) (Skamarock et al., 2008), to generate more new data to evaluate the simplified formula. The WRF‐LES model has been widely used for studying atmospheric boundary layer turbulence (e.g., Catalano & Moeng, 2010; G. Liu et al., 2011; Ma & Liu, 2017; Ma et al., 2020) and land‐atmosphere interactions (e.g., Ma & Liu, 2019; Shao et al., 2013; S. Liu & Shao, 2013; S. Liu et al., 2016, 2017).…”

“…4 of 8 atmospheric boundary layer turbulence (e.g., Catalano & Moeng, 2010;G. Liu et al, 2011;Ma & Liu, 2017;Ma et al, 2020) and land-atmosphere interactions (e.g., Ma & Liu, 2019;Shao et al, 2013;S. Liu et al, 2016S.…”

A rapid method for computing 3-D high-resolution vegetative canopy winds in weakly complex terrain

“…Large-eddy simulations (LES) solve the NSE for scales where the turbulence contains the most energy and apply parameterizations to the filtered quantities (Piomelli, 1999;Stoll et al, 2020). These techniques have contributed considerably to our understanding of how the wind flows in a complex environment, e.g., along slopes, within canopies, and on hillsides with vegetation (Bailey and Stoll, 2016;Ma et al, 2020;Sharma and García-Mayoral, 2020). Despite their achievements, DNS and LES remain highly time-consuming and require hours of computation on supercomputers to simulate domains of tens of meters.…”