Biological Systems Engineering
Date of this Version
2010
Citation
Transactions of the ASABE 53(4):1059-1076
Abstract
We compared daily net radiation (Rn) estimates from 19 methods with the ASCE‐EWRI Rn estimates in two climates: Clay Center, Nebraska (sub‐humid) and Davis, California (semi‐arid) for the calendar year. The performances of all 20 methods, including the ASCE‐EWRI Rn method, were then evaluated against Rn data measured over a non‐stressed maize canopy during two growing seasons in 2005 and 2006 at Clay Center. Methods differ in terms of inputs, structure, and equation intricacy. Most methods differ in estimating the cloudiness factor, emissivity (α), and calculating net longwave radiation (Rnl). All methods use albedo (α) of 0.23 for a reference grass/alfalfa surface. When comparing the performance of all 20 Rn methods with measured Rn, we hypothesized that the α values for grass/alfalfa and non‐stressed maize canopy were similar enough to only cause minor differences in Rn and grass‐ and alfalfa‐reference evapotranspiration (ETo and ETr) estimates. The measured seasonal average α for the maize canopy was 0.19 in both years. Using α = 0.19 instead of α = 0.23 resulted in 6% overestimation of Rn. Using α = 0.19 instead of α = 0.23 for ETo and ETr estimations, the 6% difference in Rn translated to only 4% and 3% differences in ETo and ETr, respectively, supporting the validity of our hypothesis. Most methods had good correlations with the ASCE‐EWRI Rn (r2 > 0.95). The root mean square difference (RMSD) was less than 2 MJ m‐2 d‐1 between 12 methods and the ASCE‐EWRI Rn at Clay Center and between 14 methods and the ASCE‐EWRI Rn at Davis. The performance of some methods showed variations between the two climates. In general, r2 values were higher for the semi‐arid climate than for the sub‐humid climate. Methods that use dynamic α as a function of mean air temperature performed better in both climates than those that calculate α using actual vapor pressure. The ASCE‐EWRI‐estimated Rn values had one of the best agreements with the measured Rn (r2 = 0.93, RMSD = 1.44 MJ m‐2 d‐1), and estimates were within 7% of the measured Rn. The Rn estimates from six methods, including the ASCE‐EWRI, were not significantly different from measured Rn. Most methods underestimated measured Rn by 6% to 23%. Some of the differences between measured and estimated Rn were attributed to the poor estimation of Rnl. We conducted sensitivity analyses to evaluate the effect of Rnl on Rn, ETo, and ETr. The Rnl effect on Rn was linear and strong, but its effect on ETo and ETr was subsidiary. Results suggest that the Rn data measured over green vegetation (e.g., irrigated maize canopy) can be an alternative Rn data source for ET estimations when measured Rn data over the reference surface are not available. In the absence of measured Rn, another alternative would be using one of the Rn models that we analyzed when all the input variables are not available to solve the ASCE‐EWRI Rn equation. Our results can be used to provide practical information on which method to select based on data availability for reliable estimates of daily Rn in climates similar to Clay Center and Davis.
Comments
Copyright (c) 2010 American Society of Agricultural and Biological Engineers. Used by permission.