Usta Selçuk
Van Vocational School Department of Construction Technology, Yüzüncü Yil (Centennial) University, Van, Turkey.
PeerJ. 2024 Nov 27;12:e18549. doi: 10.7717/peerj.18549. eCollection 2024.
Accurately measured or estimated reference evapotranspiration (ET) data are needed to properly manage water resources and prioritise their future uses. ET can be most accurately measured using lysimeter systems. However, high installation and operating costs, as well as difficult and time-consuming measurement processes limit the use of these systems. Therefore, the approach of estimating ET by empirical models is more preferred and widely used. However, since those models are well in accordance with the climatic and environmental traits of the region in which they were developed, their reliability must be examined if they are utilised in distinctive regions. This study aims to test the usability of mass transfer-based Dalton, Rohwer, Penman, Romanenko, WMO and Mahringer models in Van Lake microclimate conditions and to calibrate them in compatible with local conditions.
Firstly, the original equations of these models were tested using 9 years of daily climate data measured between 2012 and 2020. Then, the models were calibrated using the same data and their modified equations were created. The original and modified equations of the models were also tested with the 2021 and 2022 current climate data. Modified equations have been created using the Microsoft Excel program solver add-on, which is based on linear regression. The daily average ET values estimated using the six mass transfer-based models were compared with the daily average ET values calculated using the standard FAO-56 PM equation. The statistical approaches of the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Nash-Sutcliffe Efficiency (NSE), and determination coefficient (R) were used as comparison criterion.
The best and worst performing models in the original equations were Mahringer (MAE = 0.70 mm day, MAPE = 15.86%, RMSE = 0.87 mm day, NSE = 0.81, R = 0.94) and Penman (MAE = 1.84 mm day, MAPE = 33.68%, RMSE = 2.39 mm day, NSE = -0.49, R = 0.91), respectively, whereas in the modified equations Dalton (MAE = 0.29 mm day, MAPE = 7.51%, RMSE = 0.33 mm day, NSE = 0.97, R = 0.97) and WMO (MAE = 0.36 mm day, MAPE = 8.89%, RMSE = 0.43 mm day, NSE = 0.95, R = 0.97). The RMSE errors of the daily average ET values estimated using the modified equations were generally below the acceptable error limit (RMSE < 0.50 mm day). It has been concluded that the modified equations of the six mass transfer-based models can be used as alternatives to the FAO-56 PM equation under the Van Lake microclimate conditions (NSE > 0.75), while the original equations-except for those of Mahringer (NSE = 0.81), WMO (NSE = 0.79), and Romanenko (NSE = 0.76)-cannot be used.
准确测量或估算的参考蒸发散(ET)数据对于合理管理水资源及其未来利用的优先级至关重要。使用蒸渗仪系统可以最准确地测量ET。然而,高昂的安装和运行成本以及困难且耗时的测量过程限制了这些系统的使用。因此,通过经验模型估算ET的方法更受青睐且被广泛应用。然而,由于这些模型与它们所开发地区的气候和环境特征高度契合,如果在不同地区使用,必须检验其可靠性。本研究旨在测试基于质量传递的道尔顿、罗韦尔、彭曼、罗曼年科、世界气象组织(WMO)和马林格模型在凡湖小气候条件下的可用性,并根据当地条件对其进行校准。
首先,使用2012年至2020年期间测量的9年每日气候数据对这些模型的原始方程进行测试。然后,使用相同数据对模型进行校准并创建其修正方程。模型的原始方程和修正方程也使用2021年和2022年的当前气候数据进行测试。修正方程是使用基于线性回归的Microsoft Excel程序求解器加载项创建的。将使用六种基于质量传递的模型估算的每日平均ET值与使用标准粮农组织-56 Penman-Monteith(PM)方程计算的每日平均ET值进行比较。使用平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、均方根误差(RMSE)、纳什-萨特克利夫效率(NSE)和决定系数(R)的统计方法作为比较标准。
原始方程中表现最佳和最差的模型分别是马林格模型(MAE = 0.70毫米/天,MAPE = 15.86%,RMSE = 0.87毫米/天,NSE = 0.81,R = 0.94)和彭曼模型(MAE = 1.84毫米/天,MAPE = 33.68%,RMSE = 2.39毫米/天,NSE = -0.49,R = 0.91),而在修正方程中是道尔顿模型(MAE = 0.29毫米/天,MAPE = 7.51%,RMSE = 0.33毫米/天,NSE = 0.97,R = 0.97)和WMO模型(MAE = 0.36毫米/天,MAPE = 8.89%,RMSE = 0.43毫米/天,NSE = 0.95,R = 0.97)。使用修正方程估算的每日平均ET值的RMSE误差通常低于可接受误差限(RMSE < 0.50毫米/天)。得出的结论是,在凡湖小气候条件下(NSE > 0.75),六种基于质量传递的模型的修正方程可以用作粮农组织-56 PM方程的替代方案,而原始方程(除了马林格模型(NSE = 0.81)、WMO模型(NSE = 0.79)和罗曼年科模型(NSE = 0.76)的方程外)不能使用。