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United States Department of Agriculture

Agricultural Research Service

Research Project: REMOTE SENSING FOR CROP AND WATER MANAGEMENT IN IRRIGATED AGRICULTURE Title: Estimation of surface energy fluxes using surface renewal and flux variance techniques over an advective irrigated agricultural site

item Hipps, Lawrence -
item Chavez Eguez, Jose
item Howell, Terry

Submitted to: Advances in Water Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 6, 2012
Publication Date: July 7, 2012
Citation: French, A.N., Alfieri, J.G., Kustas, W.P., Prueger, J.H., Hipps, L.E., Chavez Eguez, J.L., Evett, S.R., Howell, T.A., Gowda, P., Hunsaker, D.J., Thorp, K.R. 2012. Estimation of surface energy fluxes using surface renewal and flux variance techniques over an advective irrigated agricultural site. Advances in Water Resources. 50:91-105. doi:10.1016/j.advwatres.2012.07.007.

Interpretive Summary: Estimating the surface energy balance over irrigated lands is important for monitoring and managing the amount of water used for crops. The energy balance is especially important for windy regions where crop water use can sometimes greatly exceed average requirements. One way to accurately measure this surface energy is with an eddy-covariance system, which is a collection of instruments that combines precise observations of energy fluxes in the air, plants and soil. However, these systems are expensive and complex, hindering their deployment beyond more than a few sites. One way that could reduce this obstacle is to deploy monitoring systems that collect air temperature data that can be used to emulate energy flux information obtained in more costly ways. These systems are based on data from fine-wire thermocouples that are combined with models known as ‘surface renewal’ and ‘flux variance’. The accuracies of these models were tested in a field experiment, BEAREX 2008, conducted at Bushland, Texas. Both methods produced good results at midday, and acceptable estimates at other times of day. These outcomes will be important for investigators seeking effective, low-cost ways to measure crop water use in windy environments.

Technical Abstract: Estimation of surface energy fluxes over irrigated agriculture is needed to monitor crop water use. Estimates are commonly done using well-established techniques such as eddy covariance (EC) and weighing lysimetry, but implementing these to collect spatially distributed observations is complex and costly. Two techniques that could simplify flux observations are the surface renewal and flux variance approaches. These methods infer sensible heat fluxes from high frequency observations of near surface air temperatures using low cost thermocouples. In combination with net radiation and soil heat flux observations, surface renewal and flux variance observations produce latent heat fluxes as a residual of the surface energy balance. The viability of these techniques was tested in a strongly advective irrigated agricultural setting as part of the Bushland Evapotranspiration and Remote Sensing Experiment in 2008 (BEAREX08). Using 20Hz air temperature data collected between 12 June and 13 August from two cotton field sites and one senescent grass site, sensible heat flux estimates were generated. Surface flux conditions ranged widely and include episodes of latent heat fluxes exceeding net radiation. Flux estimates from surface renewal and flux variance were similar with respect to simultaneous eddy covariance observations on most days. At midday the flux variance had better peformance than surface renewal estimates, although the reverse was true for early morning and late afternoon transition times. During advection neither approach was accurate, although the surface renewal fluxes did a better job estimating the correct sign of sensible heat fluxes . Both techniques were found to offer flux estimates comparable to EC data with the additional potential benefit of reduced fetch requirements. These outcomes indicate that flux data with accuracies approaching EC capabilities is feasible with a potential for reduced deployment complexity and cost.

Last Modified: 8/3/2015
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