Surface Renewal Estimation of Pasture Evapotranspiration

Snyder, Richard L.; Spano, Donatella; Duce, Pierpaolo; U, K. T. Paw; Rivera, Melissa C.

doi:10.1061/(asce)0733-9437(2008)134:6(716)

Cited by 29 publications

(16 citation statements)

References 7 publications

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“…Results indicated good estimation for the whole cropping season [48]. Drexler et al performed experiments for the evaluation of the SR method over rangeland grass using the SR and EC methods.…”

Section: Sr Versus Eddymentioning

confidence: 99%

Surface Renewal Application for Estimating Evapotranspiration: A Review

Buttar

Tanny

et al. 2018

Advances in Meteorology

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The estimation of evapotranspiration (ET) is essential for meteorological modeling of surface exchange processes, as well as for the agricultural practice of irrigation management. Hitherto, a number of methods for estimation of ET at different temporal scales and climatic conditions are constantly under investigation and improvement. One of these methods is surface renewal (SR). Therefore, the premise of this review is to present recent developments and applications of SR for ET measurements. The SR method is based on estimating the turbulent exchange of sensible heat flux between plant canopy and atmosphere caused by the instantaneous replacement of air parcels in contact with the surface. Additional measurements of net radiation and soil heat flux facilitate extracting ET using the shortened energy balance equation. The challenge, however, is the calibration of SR results against direct sensible heat flux measurements. For the classical SR method, only air temperature measured at high frequency is required. In addition, a new model suggests that the SR method could be exempted from calibration by measuring additional micrometeorological variables. However, further improvement of the SR method is required to provide improved results in the future.

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Section: Sr Versus Eddymentioning

confidence: 99%

Surface Renewal Application for Estimating Evapotranspiration: A Review

Buttar

Tanny

et al. 2018

Advances in Meteorology

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“…The weighting factor α generally depends on the measurement height, lag time, canopy structure and thermocouple size (Snyder et al, 1996;Spano et al, 1997aSpano et al, ,b, 2000Duce et al, 1998). Once determined, α is fairly stable, almost invariant and does not change from site to site regardless of weather conditions unless the canopy structure changes (Paw U et al, 1995;Snyder et al, 1996Snyder et al, , 2008Spano et al, 2000). A consistent α value of 0.5, using air temperature measurements conducted at canopy height, for coniferous forests, orchards, and maize canopies (Paw U et al, 1995); and 1.0 for short turf grass using measurements conducted at several heights above the canopy (Snyder et al, 1996) were reported to give best estimates of H compared to EC.…”

Section: Surface Renewal (Sr) Methodsmentioning

confidence: 99%

“…The SR method has been successfully applied over a range of surfaces (e.g., Paw U et al, 1995;Snyder et al, 1996Snyder et al, , 2008Duce et al, 1997;Spano et al, 1997aSpano et al, ,b, 2000Zapta and Martínez-Cob, 2001;Savage et al, 2004;Mengistu, 2008;Abraha, 2010;Nile, 2010) including open water (Mengistu and Savage, 2010). The method has been in constant development and refinement since its introduction, and several versions of the SR method for estimation of H exist.…”

Section: Surface Renewal (Sr) Methodsmentioning

confidence: 99%

Energy and Mass Exchange Over Incomplete Vegetation Cover

Abraha

Savage

2012

Critical Reviews in Plant Sciences

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“…This required a rapid computational method that worked over a range of different time averaging periods, and which could implement the various calibration procedures used in the SR method. Initial attempts to calculate flux followed methods as described by Paw U et al (2005) and Snyder et al (2008). However, implementing these methods as documented was hampered by slow computation time, which constrained the many iterations required to determine the minimum flux averaging period.…”

Section: Introductionmentioning

confidence: 99%

Computational efficiency for the surface renewal method

Kelley

Higgins

2018

Atmos. Meas. Tech.

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Abstract. Measuring surface fluxes using the surface renewal (SR) method requires programmatic algorithms for tabulation, algebraic calculation, and data quality control. A number of different methods have been published describing automated calibration of SR parameters. Because the SR method utilizes high-frequency (10 Hz+) measurements, some steps in the flux calculation are computationally expensive, especially when automating SR to perform many iterations of these calculations. Several new algorithms were written that perform the required calculations more efficiently and rapidly, and that tested for sensitivity to length of flux averaging period, ability to measure over a large range of lag timescales, and overall computational efficiency. These algorithms utilize signal processing techniques and algebraic simplifications that demonstrate simple modifications that dramatically improve computational efficiency. The results here complement efforts by other authors to standardize a robust and accurate computational SR method. Increased speed of computation time grants flexibility to implementing the SR method, opening new avenues for SR to be used in research, for applied monitoring, and in novel field deployments.

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Surface Renewal Estimation of Pasture Evapotranspiration

Cited by 29 publications

References 7 publications

Surface Renewal Application for Estimating Evapotranspiration: A Review

Surface Renewal Application for Estimating Evapotranspiration: A Review

Energy and Mass Exchange Over Incomplete Vegetation Cover

Computational efficiency for the surface renewal method

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