Agricultural Structures and Irrigation/Agriculture Faculty, Çukurova University, Adana, Turkey.
PeerJ. 2023 Apr 27;11:e15252. doi: 10.7717/peerj.15252. eCollection 2023.
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, 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; REs (%) = 18.2-22.6; R = 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; 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; 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; RE(%) = 24.2; R = 0.423).
参考蒸散量（ETo）被认为是水资源管理、灌溉实践、农业和水文气象研究以及模拟不同水文过程的主要变量之一。因此，准确预测 ETo 至关重要。全球许多科学家和专家已经开发了大量的经验方法，以便从不同的气候变量来估算 ETo。粮农组织 56 佩恩曼-蒙特斯（PM）公式是在各种环境和气候条件下估算 ETo 最被接受和准确的模型。然而，FAO56-PM 方法需要辐射、空气温度、空气湿度和风速数据。在本研究中，在夏季生长季节具有地中海气候的阿达纳平原，使用 22 年的每日气候数据，当气候数据缺失时，评估了 FAO56-PM 方法在不同气候变量组合下的性能。此外，评估了哈格雷夫斯-萨曼尼（HS）和 HS（A&G）方程的性能，并使用不同的气候变量组合开发了多元线性回归模型（MLR）。当风速（U）和相对湿度（RH）数据不可用时，FAO56 论文（RMSE 小于 0.4 毫米 d，相对误差（RE）小于 9%）建议的程序可以准确估算每日 ETo。Hargreaves-Samani（A&G）和 HS 方程不能根据统计指标准确估算每日 ETo（RMSEs = 0.772-0.957 毫米 d；REs（%）= 18.2-22.6；R = 0.604-0.686，分别）。另一方面，MLR 模型的性能取决于不同气候变量的组合。根据 MLR 模型的独立变量的 t 统计量和 p 值，太阳辐射（Rs）和日照时数（n）变量对估算 ETo 的影响大于其他变量。因此，使用 Rs 和 n 数据的模型更准确地估计了每日 ETo。使用 Rs 的模型的 RMSE 值在 0.288 到 0.529 毫米 d 之间；在验证过程中，RE（%）值在 6.2%-11.5%之间。使用 n 的模型的 RMSE 值在 0.457 到 0.750 毫米 d 之间；在验证过程中，RE（%）值在 9.9%-16.3%之间。仅基于空气温度的模型表现最差（RMSE = 1.117 毫米 d；RE（%）= 24.2；R = 0.423）。
