TY - JOUR AU - Ganji, Homayoon AU - Kajisa, Takamitsu PY - 2019/09/10 Y2 - 2024/03/28 TI - Error propagation approach for estimating root mean square error of the reference evapotranspiration when estimated with alternative data JF - Journal of Agricultural Engineering JA - J Agric Eng VL - 50 IS - 3 SE - Original Articles DO - 10.4081/jae.2019.909 UR - https://www.agroengineering.org/jae/article/view/909 SP - 120-126 AB - <p>Estimation of reference evapotranspiration (<em>ET</em><sub>0</sub>) with the Food and Agricultural Organisation (FAO) Penman-Monteith model requires temperature, relative humidity, solar radiation, and wind speed data. The lack of availability of the complete data set at some meteorological stations is a severe restriction for the application of this model. To overcome this problem, <em>ET</em><sub>0</sub> can be calculated using alternative data, which can be obtained via procedures proposed in FAO paper No.56. To confirm the validity of reference evapotranspiration calculated using alternative data (<em>ET</em><sub>0(Alt)</sub>), the root mean square error (<em>RMSE</em>) needs to be estimated; lower values of RMSE indicate better validity. However, RMSE does not explain the mechanism of error formation in a model equation; explaining the mechanism of error formation is useful for future model improvement. Furthermore, for calculating <em>RMSE</em>, <em>ET</em><sub>0</sub> calculations based on both complete and alternative data are necessary. An error propagation approach was introduced in this study both for estimating <em>RMSE</em> and for explaining the mechanism of error formation by using data from a 30-year period from 48 different locations in Japan. From the results, <em>RMSE</em> was confirmed to be proportional to the value produced by the error propagation approach (Δ<em>ET</em><sub>0</sub>). Therefore, the error propagation approach is applicable to estimating the RMSE of <em>ET</em><sub>0(Alt)</sub> in the range of 12%. Furthermore, the error of <em>ET</em><sub>0(Alt)</sub> is not only related to the variables’ uncertainty but also to the combination of the variables in the equation.</p> ER -