The vertical turbulent structure within the surface boundary layer above a Vineyard in California’s Central Valley during GRAPEX

Water is already a limited resource in California, and meeting the competing water needs, there will be only more challenges in the coming decades. Thus, sustaining the production of wine grapes, which are among the highest value specialty crops in the state, requires water to be used efficiently as possible. At the same time, improving irrigation management in vineyards requires spatially distributed information regarding vine water use or evapotranspiration (ET) at the sub-field scale that can only be collected via remote sensing. However, due to their unique canopy structure, current remote sensing models may not accurately describe the underlying turbulent exchange controlling ET from vineyards. To address that knowledge gap, this study investigates the vertical turbulent structure over a vineyard in the Central Valley of California. Using data from a profile of sonic anemometers (2.5 m, 3.75 m, 5 m, and 8 m, above the surface) collected during 2017 as a part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX), this study characterized the relationship between the turbulent flow at different heights using spectral analysis. It was found that the turbulent structure is strongly influenced by the underlying canopy. It also showed that the characteristics of the vertical structure differ significantly from what would be expected over other types of crops because of the unique configuration of vineyards, i.e., the concentration of the biomass in the upper part of the canopy and wide inter-row spacing. As a result, surface energy balance modeling using remote sensing data will likely require modifications to formulations of the turbulent energy exchange of the inter-row-canopy system with the lower atmosphere to reliably estimate vine ET. An example of this effect is shown for the mean wind profile which deviates from predicted profile using classical Monin–Obukhov similarity theory (MOST) used in remote sensing-based energy balance models resulting in errors in heat flux exchange which in turn affects modeled ET.