Abstract This study was conducted to evaluate the performance of temperature-based models i.e. temperature-based Penman-Monteith FAO 56 fed with average wind speed (U) value (TPMU) and default U quantity (TPM2), Hargreaves-Samani (HS) and FAO Blaney-Criddle (FBC) against Penman-Monteith FAO 56 (PM) using data recorded in 1993–2015 at 146 sites over Iran. Two statistics i.e. normalized Root Mean Square (nRMSE) and relative Mean Bias Error (rMBE) were calculated to analyze the absolute error and bias magnitude of the temperature-based ET0 estimation, respectively. Except for the December-January-February (DJF), the models gave reliable seasonal estimates (i.e. nRMSE of 30 > %) for the majority of studied areas. At monthly scale, FBC gave poor estimates of ET0 in DJF for more than 60% of semi-arid and sub/humid-humid sites. ET0 in December and January was not also modeled reliably by TPM2 for 61 and 52% of the semi-arid and sub/humid-humid sites, respectively. Hence, application of FBC and TPM2 appears not to be recommendable in cold areas and months over Iran. Overall, TPMU and HS are better suited at all temporal scales under data limitation over the studied areas. In the case of data availability, calculation of TPM with local average U (instead of default quantity of 2 m s−1) is highly likely to improve the estimation accuracy. Seasonal and monthly ET0 were mostly underestimated over the hyper-arid/arid sites during the March-April-May (MAM) and June-July-August (JJA). However, TPM2 and HS overestimated ET0 for the majority of semi-arid and sub-humid/humid areas. The U anomalies were identified as the primary contributing factor to the error in temperature-based ET0 estimation for most cases. TPM2, HS and FBC provided more accurate estimates for the U range of 1.5–2.5 m s−1. These findings are of significant practical importance for agricultural, hydrological and climatic studies and applications under data sparse condition.

中文翻译：

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在伊朗缺乏可靠数据的情况下模拟参考作物蒸发量

摘要 本研究旨在评估基于温度的模型的性能，即基于温度的 Penman-Monteith FAO 56 以平均风速 (U) 值 (TPMU) 和默认 U 量 (TPM2)、Hargreaves-Samani (HS) 和粮农组织 Blaney-Criddle (FBC) 与 Penman-Monteith FAO 56 (PM) 使用 1993-2015 年在伊朗 146 个地点记录的数据。计算了两个统计量，即归一化均方根 (nRMSE) 和相对平均偏差误差 (rMBE)，以分别分析基于温度的 ET0 估计的绝对误差和偏差幅度。除了 12 月-1 月-2 月 (DJF)，这些模型对大多数研究区域给出了可靠的季节性估计（即 nRMSE 为 30 > %）。在月度尺度上，FBC 对 DJF 中 60% 以上的半干旱和亚/湿润湿地的 ET0 估计很差。12 月和 1 月的 ET0 也没有被 TPM2 可靠地模拟，分别用于 61% 和 52% 的半干旱和半湿润/半湿润地区。因此，FBC 和 TPM2 的应用似乎不推荐在寒冷地区和伊朗的几个月内使用。总体而言，在研究区域的数据限制下，TPMU 和 HS 更适合于所有时间尺度。在数据可用的情况下，用局部平均值 U（而不是默认的 2 m s-1 数量）计算 TPM 很有可能提高估计精度。在 3-4-5 月 (MAM) 和 6-7-8 月 (JJA) 期间，超干旱/干旱地区的季节性和每月 ET0 大多被低估。然而，TPM2 和 HS 高估了大多数半干旱和半湿润/湿润地区的 ET0。在大多数情况下，U 异常被确定为导致基于温度的 ET0 估计错误的主要因素。TPM2、HS 和 FBC 为 1.5-2.5 m s-1 的 U 范围提供了更准确的估计。这些发现对于数据稀疏条件下的农业、水文和气候研究和应用具有重要的实际意义。