The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ET_Ref) in different climates of Iran based on the Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990–2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R²), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO₅₆ Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8–15.1 mm month⁻¹ and 22–28.3 mm month⁻¹, respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01–11.2 mm month⁻¹ had higher accuracy than C and D climates with MAE = 13.44–14.48 mm month⁻¹. In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.

该研究旨在评估经验方程（Hargreaves-Samani； HS，Irmak； IR和Dalton； DT）和多元线性回归模型（MLR1-6）的准确性，以估计参考蒸散量（ET _Ref）。）的气候基于伊朗的科本方法，包括干旱沙漠（Bw），半干旱（Bs），冬季温和的湿润（C）和冬季严酷的湿润（D）。为此，使用了1990-2019年统计的30个统计年中的33个气象站的气候数据，并按月计算时间步长。根据各种气象数据（最低和最高温度，相对湿度，风速，太阳辐射，地外辐射和蒸气压赤字），除了使用6个多元线性回归模型和3个经验方程式作为MLR1，MLR2和HS （基于温度），MLR3和IR（基于辐射），MLR4，MLR5和DT（基于质量转移）以及MLR6（基于组合）也用于估算参考蒸散量。使用均方根误差（RMSE）比较了这些模型的结果，R ²）和纳什-苏特克利夫效率（NSE）统计标准，以FAO ₅₆ Penman-Monteith参考的蒸散结果为目标数据。所有MLR模型都比经验方程式给出更好的结果。结果表明，基于最小和最大温度数据的最简单回归模型（MLR1）比经验方程更为准确。与具有MSE6的MLR6模型和HS经验方程式相关的最低和最高准确度分别为10.8-15.1毫米月^-1和22-28.3毫米月^-1。此外，在所有评估方程中，基于辐射的模型，例如MAE = 8.01–11.2 mm month ^-1的Bw和Bs气候中的IR与MAE = 13.44–14.48 mm month ^-1的C和D气候相比，其准确性更高。总的来说，结果表明，基于SI <0.2，从Bw到D的所有气候条件下，回归模型的能力都非常出色。