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

• 作者: Antonio Ribeiro da Cunha Clovis Alberto Volpe Edgar Ricardo Sch?ffel
• 来源期刊: 水资源与保护(英文)
• 学科: 医学
• 关键词: EVAPOTRANSPIRATION Net RADIATION Solar RADIATION CLOUD COVER Empirical Models
• 年，卷（期）: 2014,（15）
• 页码范围: 1425-1436
• 页数: 12页
• 分类号: R73

摘要

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 (Rnmeasured and Rnestimated, 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 (Rnmeasured), five different models were used in order to obtain the Rnestimated. 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, Rnmeasured can be replaced by Rnestimated, 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 Rnestimated 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.