小流域水文循环过程中蒸散发量模拟方法评价

蒸散发(ET)在农业灌溉和水资源管理中起着重要作用。ET可通过FAO-Penman-Monteith方法(ET_(FPM))进行准确估算,ET_(FPM)方法是ET估算的标准参考方法,此方法需要提供更为详实的气象数据。对于ET的估算,需要寻找使用较少的输入数据,而不会影响预测准确性的替代方法。研究运用5个基于辐射的模型,包括Makkink(ET_(MAK))、Priestley和Taylor(ET_(PT))、Abtew(ET_(ABT))、Jensen-Haise(ET_(JH))、McGuinness和Bordne(ET_(MB)),3个基于温度的模型,包括Hargreaves and Samani(ET_(HS))、Hamon(ET_(HAM))和Linacre(ET_(LIN)),以及1个基于空气动力学的模型Penman(ET_(PEN)),通过使用韩仓河流域周边6个气象水文站的长期数据,将选取的模型与ET_(FPM)模型在月尺度和生长季节尺度上进行比较评价。结果表明,ET_(JH)、ET_(HAM)分别是67%,33%研究区域每月ET的最佳预测方法。在研究区域中,基于辐射的方法优于基于温度的方法。植被生长季节ET累积值表明,Jensen-Haise和Hamon方法在暖季和秋冬季生长期表现最佳,而春季生长期最佳预测方法仅包括Jensen-Haise方法。最佳替代方法和ET_(FPM)方法之间的差异表明,最佳替代方法在某些地区的估算可信度不高,因此在使用之前应考虑ET模型可预测性能的时空变化。

Evaluation of Evapotranspiration Simulation Methods in Hydrological Cycle in Small Basin

Evapotranspiration (ET) plays an important role in agricultural irrigation and water resources management. ET can be accurately estimated by the FAO-Penman-Monteith method (ET_FPM). The ET_FPM method is the standard reference method for ET estimation. This method needs to provide more detailed meteorological data. For the estimation of ET, it is necessary to find an alternative method that uses less input data without affecting the accuracy of the prediction. This study used 5 radiation-based models, including Makkink (ET_MAK), Priestley and Taylor (ET_PT), Abtew (ET_ABT), Jensen-Haise (ET_JH), McGuinness and Bordne (ET_MB), and 3 temperature-based models, including Hargreaves and Samani (ET_HS), Hamon (ET_HAM) and Linacre (ET_LIN), and a model Penman (ET_PEN) based on aerodynamics. Using the long-term data from 6 meteorological and hydrological stations in the Hancang River Basin, the selected model were evaluated by comparing them with ET_FPM on a monthly and growing season scale. The statistical analysis revealed that ET_JH and ET_HAM are the best forecasting methods for monthly ET in 67% and 33% of the study area respectively. In the study area, the radiation-based methods were better than temperature-based methods. The cumulative values of ET during the vegetation growth periods showed that the Jensen-Haise method and Hamon method perform best in the warm season and autumn and winter growing seasons, while the best prediction method for the spring growing season only included the Jensen-Haise method. However, divergence between estimations of the best alternative methods and the reference method showed that the best ET alternative methods might be unreliable in some regions. Accordingly, the spatiotemporal variability in predictability performance of ET models should be taken into account prior to use.