Artículos de revistas
Effect of height and time lag on the estimation of sensible heat flux over a drip-irrigated vineyard using the surface renewal (SR) method across distinct phenological stages
Registro en:
AGRICULTURAL WATER MANAGEMENT 141 : 74-83
1873-2283
Autor
Poblete-Echeverria, C.
Sepulveda-Reyes, D.
Ortega-Farias, S.
Institución
Resumen
Univ Talca, Res & Extens Ctr Irrigat & Agroclimatol CITRA, Talca, Chile. Poblete-Echeverria, C (Poblete-Echeverria, Carlos); Sepulveda-Reyes, D (Sepulveda-Reyes, Daniel); Ortega-Farias, S (Ortega-Farias, Samuel) For drip-irrigated vineyards, sensible heat flux (H) is a key parameter to estimate water requirements, when actual evapotranspiration (ETa) is computed as a residual from the surface energy balance. In this regard, a field experiment was carried out to study the effect of measurement height (z) and time lag (r) on the estimation of H over a drip-irrigated vineyard using classical formulation of surface renewal (SR) method. For vineyards, previous studies have indicated that the calibration factor (alpha) and the accuracy of the SR method depend on z, however the combined effect of z and r on the estimation of H has not been studied in detail for key phenological stages. In this study 12 combinations of 4 time lags (r(1) = 0.2s, r(2) =0.5 s, r(3) = 0.7 s and r(4) = 1.0 s) and 3 measurement heights (z(1)= 0.5, z(2)= 1.0 and z(3) =1.5 m above canopy) of high-frequency air temperature were evaluated to estimate H using the SR method (H-SR) across distinct phenological stages. A three-dimensional sonic anemometer (CSAT3) was used to measure sensible heat (H-EC over the vineyard. Results indicated that the regression analysis between H-SR and H-EC was highly significant with determination coefficients (r(2)) between 0.70 and 0.93. Also, alpha values registered in this study varied from 0.67 to 1.01 for the different combinations and phenological periods. Calibrated H-SR computed using z(1) and r3 gave the best estimates of H-EC in the validation period, with a root mean square error (RMSE) of 52.2 Wm(-2) and mean absolute error (MAE) of 35.2 Wm(-2) for all dataset analysis. (C) 2014 Published by Elsevier B.V.