Location: Hydrology and Remote Sensing LaboratoryTitle: Influence of wind direction on the surface roughness of vineyards
|Kustas, William - Bill|
|NIETO, H. - Institute De Recerca I Tecnologia Agroalimentaries (IRTA)|
|PRUEGER, J.H. - US Department Of Agriculture (USDA)|
|HIPPS, L.E. - Utah State University|
Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/10/2018
Publication Date: 10/20/2018
Citation: Alfieri, J.G., Kustas, W.P., Nieto, H., Prueger, J., Hipps, L., McKee, L.G. 2018. Influence of wind direction on the surface roughness of vineyards. Irrigation Science. 37(3):359-373. https://doi.org/10.1007/s00271-018-0610-z.
Interpretive Summary: Fresh water is a limited resource in California and many other regions where viticulture is prevalent. Remote sensing-based models are the most viable means of collecting the high-resolution spatially distributed estimates of evaporative water loss needed to manage water effectively, optimize irrigation practices, and ensure the sustainability of both wine grape production and wineries. However, these models may not be able to adequately describe the physical processes driving evapotranspiration (ET) from vineyards. In an effort to enhance the models, this study used data collected from 2014 to 2016 as a part of the GRAPEX field campaign to identify physically-based relationships to estimate the surface roughness, an important parameter for determining the exchange and transport of moisture away for the surface. The resulting relationships were incorporated into the thermal remote sensing Two-Source Energy Balance (TSEB) model. This version of the model was compared to both the standard version of the model and one modified to better represent sparse canopies. While the surface roughness from the different models differed by more than 45%, the change in the estimates of ET was less than 10%. The results suggest that the TSEB model is largely insensitive to changes in the roughness parameters. The also suggest the utility of this approach may be limited for applications using the TSEB model.
Technical Abstract: Remote sensing-based models are the most viable means of collecting the high-resolution spatially distributed estimates of evaporative water loss needed to manage irrigation and ensure the effective use of limited water resources. Due to the unique canopy structure and configuration of vineyards, however, these models may not be able to adequately describe the physical processes driving evapotranspiration from vineyards. Using data collected from 2014 to 2016 as a part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX), the twofold objective of this study was to i. identify the relationship between the roughness parameters, zero-plane displacement height (do) and roughness length for momentum (zo), and local environmental conditions, specifically wind direction and vegetation density and ii. determine the effect of using these relationships on the ability of the remote sensing-based Two-Source Energy Balance (TSEB) model to estimate the sensible (H) and latent ('E) heat fluxes. Although little variation in do was identified during the growing season, a well-defined sigmoidal relationship was observed between zo and wind direction. When the output from a version of the TSEB model incorporating these relationships (TSEBVIN) was compared to output from the standard model (TSEBSTD), there were only modest changes in either the roughness parameters or turbulent fluxes. When the output from TSEBVIN was compared to that of a version using a parameterization scheme representing open canopies (TSEBOPN), the mean absolute difference between the estimates of do and zo were 0.44 m and 0.25 m, respectively. While these values represent differences in excess of 45%, the turbulent fluxes differed by just 13 W m-2 or 10%, on average. The results suggest that the TSEB model is largely insensitive to changes in the roughness parameters. The also suggest the utility of this approach may be limited for applications using the TSEB model.