A fast-response, wind angle-sensitive model for predicting mean winds in row-organized canopies

Parallel arrays of long, porous, fence-like objects (row-organized canopies) are common in agricultural and rural settings. Examples include trellised canopies like vineyards, series of windbreaks, snow fences, and some orchards. A computationally efficient model of the mean flow in such arrays has been developed and deployed in a GPU-accelerated, fast-response, mass-consistent diagnostic wind solver called Quick Environmental Simulation-Winds (QES-Winds). QES-Winds reduces computational cost by conserving mass only. The effects of forces and momentum conservation, such as drag, wakes, recirculation, and buoyancy, are added to the mean velocity field around objects and terrain using low-order models. The row-organized canopy (ROC) model imposes a wake and upwind pressure zone around each row and models the roughness adjustment near the canopy’s leading edge. Rather than solving equations of motion, the model accomplishes this using algebraic expressions, some of which are derived from scale analysis. The model output is compared and some parameters tuned to particle image velocimetry (PIV) data from a wind-tunnel experiment. Sonic anemometer data from a field experiment in a vineyard was used for validation. The model responded appropriately to changes in incident wind direction and captured transport-critical features of the mean wind with similar accuracy as other models implemented in QES. The model offers significant improvements over existing computationally inexpensive models of canopy flow that ignore the highly ordered structure of the array elements; in the field data validation, mean relative error in wind speeds was reduced by 42% and mean absolute error in wind direction in the canopy was reduced by 45% compared with a homogeneous canopy model. These results highlight the importance of resolving spatial heterogeneity in this class of canopies.