Evaluation of FAO-56 Penman-Monteith Model with Limited Data and the Valiantzas Models for Estimating Grass-Reference Evapotranspiration in Sahelian Conditions uri icon

abstract

  • Accurate determination of grass-reference evapotranspiration (ETo) is very important for agricultural, hydrological, and environmental studies, especially under increasing water scarcity and climate mitigation conditions. The FAO-56 Penman-Monteith (FAO-PM) model is renowned for being the most accurate model; however, it requires a full climate data set that is not always available at all weather stations, especially in most of the developing countries. Thus, using ETo models that require a reduced set of climate data continues to be an important alternative in such cases. The objectives of this study were to (1) evaluate the FAO-56 Penman-Monteith equation to estimate ETo with limited climate data, and (2) evaluate two of the latest ETo equations developed by Valiantzas. Climatic variables, including maximum and minimum air temperature, maximum and minimum relative humidity, solar radiation, and wind speed were collected from eight weather stations across Burkina Faso. The results showed that when solar radiation data were missing, solar radiation estimation from maximum and minimum daily air temperatures generated accurate estimates of ETo, with regression slope from 0.98 to 1.04, root mean squared error (RMSE) less than 0.60 mm/day0.60 mm/day, and low mean biased error (MBE) within the range of −0.18–0.02 mm/day−0.18–0.02 mm/day. In the case of lacking relative humidity data, the RMSE and MBE varied from 0.07 to 0.47 mm/day0.47 mm/day and from −0.45−0.45 to 0.22 mm/day0.22 mm/day, respectively, and the regression slope varied from 0.90 to 1.14, and the coefficient of determination (R2R2) ranged from 0.77 to 0.92. In the case of missing wind speed, the method that used long-term average wind speed (ETo-um) resulted in the best estimates, especially for lower ETo rates (ETo
  • Accurate determination of grass-reference evapotranspiration (ETo) is very important for agricultural, hydrological, and environmental studies, especially under increasing water scarcity and climate mitigation conditions. The FAO-56 Penman-Monteith (FAO-PM) model is renowned for being the most accurate model; however, it requires a full climate data set that is not always available at all weather stations, especially in most of the developing countries. Thus, using ETo models that require a reduced set of climate data continues to be an important alternative in such cases. The objectives of this study were to (1)evaluate the FAO-56 Penman-Monteith equation to estimate ETo with limited climate data, and (2)evaluate two of the latest ETo equations developed by Valiantzas. Climatic variables, including maximum and minimum air temperature, maximum and minimum relative humidity, solar radiation, and wind speed were collected from eight weather stations across Burkina Faso. The results showed that when solar radiation data were missing, solar radiation estimation from maximum and minimum daily air temperatures generated accurate estimates of ETo, with regression slope from 0.98 to 1.04, root mean squared error (RMSE) less than 0.60mm/day, and low mean biased error (MBE) within the range of -0.18-0.02mm/day. In the case of lacking relative humidity data, the RMSE and MBE varied from 0.07 to 0.47mm/day and from -0.45 to 0.22mm/day, respectively, and the regression slope varied from 0.90 to 1.14, and the coefficient of determination (R2) ranged from 0.77 to 0.92. In the case of missing wind speed, the method that used long-term average wind speed (ETo-um) resulted in the best estimates, especially for lower ETo rates (ETo<6mm/day) as compared to the method (ETo-w2) that adopted the default wind speed of 2m/s. Indeed the ETo-w2 method overestimated ETo from 4 to 35%. With the ETo-um method, RMSE varied from 0.43 to 0.57mm/day and the MBE varied from -0.05 to 0.04mm/day whereas the (ETo-w2) method had the RMSE ranging from 0.59 to 2.11mm/day and the MBE ranging from 0.26 to 1.90mm/day. The FAO-PM equation had the least accurate performance when only temperature data is available. The Valiantzas 1 equation that uses only air temperature and relative humidity data was not suitable and is not recommended for use in Burkina Faso climatic conditions. The Valiantzas 2 equation with full climatic data resulted in good ETo estimates relative to the FAO-PM ETo with full data under the Burkina Faso climate conditions; however, the FAO-PM equation is recommended because of limitations of the Valiantzas 2 equation under limited data conditions. (C) 2016 American Society of Civil Engineers.

publication date

  • 2016
  • 2016
  • 2016