Estimation of Evapotranspiration by Various Net Radiation Estimation Formulae for Non-Irrigated Grass in Brazil

Abstract

The objective of this study was to assess the accuracy of estimating evapotranspiration (ET) using the FAO-56 Penman-Monteith (FAO-56-PM) model, with measured and estimated net radiation (Rn measured and Rn estimated , respectively), the latter obtained via five different models. We used meteorological data collected between August 2005 and June 2008, on a daily basis and on a seasonal basis (wet vs. dry seasons). The following data were collected: temperature; relative humidity; global global solar radiation (Rs); wind speed and soil heat flux. The atmospheric pressure was determined by aneroid barograph, and sunshine duration was quantified with a Campbell-Stokes recorder. In addition to the sensor readings (Rn measured ), five different models were used in order to obtain the Rn estimated . Four of those models consider the effects of cloud cover: the original Brunt model; the FAO-24 model for wet climates; the FAO-24 model for dry climates, and the FAO-56 model. The fifth was a linear regression model based on Rs. In estimating the daily ET0 with the FAO-56-PM model, Rn measured can be replaced by Rn estimated , in accordance with the FAO-24 model for dry climates, with a relative error of 2.9%, or with the FAO-56 model, with an error of 4.9%, when Rs is measured, regardless of the season. The Rn estimated obtained with the fifth model has a relatively high error. The original Brunt model and FAO-24 model for wet climates performed more poorly than did the other models in estimating the Rn and ET0. In overcast conditions, the original Brunt model, the FAO-24 model for wet climates, the FAO-24 model for dry climates, the FAO-56 model and the model of linear regression with Rs as the predictor variable tended to overestimate Rn and ET, those estimates becoming progressively more accurate as the cloud cover diminished.