Reference evapotranspiration estimate with missing climatic data and multiple linear regression models

Abstract

The reference evapotranspiration (ETo) is considered one of the primary variables for water resource management, irrigation practices, agricultural and hydro-meteorological studies, and modeling different hydrological processes. Therefore, an accurate prediction of ETo is essential. A large number of empirical methods have been developed by numerous scientists and specialists worldwide to estimate ETo from different climatic variables. The FAO56 Penman-Monteith (PM) is the most accepted and accurate model to estimate ETo in various environments and climatic conditions. However, the FAO56-PM method requires radiation, air temperature, air humidity, and wind speed data. In this study in Adana Plain, which has a Mediterranean climate for the summer growing season, using 22-year daily climatic data, the performance of the FAO56-PM method was evaluated with different combinations of climatic variables when climatic data were missing. Additionally, the performances of Hargreaves-Samani (HS) and HS (A&G) equations were assessed, and multiple linear regression models (MLR) were developed using different combinations of climatic variables. The FAO56-PM method could accurately estimate daily ETo when wind speed (U) and relative humidity (RH) data were unavailable, using the procedures suggested by FAO56 Paper (RMSEs were smaller than 0.4 mm d−1, and percent relative errors (REs) were smaller than 9%). Hargreaves-Samani (A&G) and HS equations could not estimate daily ETo accurately according to the statistical indices (RMSEs = 0.772-0.957 mm d−1; REs (%) = 18.2–22.6; R2 = 0.604–0.686, respectively). On the other hand, MLR models’ performance varied according to a combination of different climatic variables. According to t-stat and p values of independent variables for MLR models, solar radiation (Rs) and sunshine hours (n) variables had more effect on estimating ETo than other variables. Therefore, the models that used Rs and n data estimated daily ETo more accurately than the others. RMSE values of the models that used Rs were between 0.288 to 0.529 mm d−1; RE(%) values were between 6.2%–11.5% in the validation process. RMSE values of the models that used n were between 0.457 to 0.750 mm d−1; RE(%) values were between 9.9%–16.3% in the validation process. The models based only on air temperature had the worst performance (RMSE = 1.117 mm d−1; RE(%) = 24.2; R2 = 0.423).

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

“…Studies have shown that the HS equation can accurately estimate ETo values when compared with the FAO Penman-Monteith (PM) method, which is considered the most accurate method for ETo estimation (Rattayová et al, 2023) (Al-Asadi et al, 2023. The HS equation performs well under different climatic conditions and environments, including regions with high altitudes (Koç & Can, 2023). In some cases, the HS equation outperforms the FAO56-PM method, particularly when meteorological data are limited or unavailable (Elagib & Musa, 2023).…”

Application of Machine Learning to Forecast Drought Index for the Mekong Delta

“…Studies have shown that the HS equation can accurately estimate ETo values when compared with the FAO Penman-Monteith (PM) method, which is considered the most accurate method for ETo estimation [51,52]. The HS equation performs well under different climatic conditions and environments, including regions with high altitudes [53]. In some cases, the HS equation outperforms the FAO56-PM method, particularly when meteorological data are limited or unavailable [54].…”