非参数化蒸散发估算方法在华北灌溉农田的适用性评价

Applicability evaluation of the nonparametric approach for estimating evapotranspiration on irrigated farmland in the North China Plain

  • 摘要: 蒸散发是水循环和地表能量平衡系统重要的组成部分之一, 是农业、水资源管理和气候变化研究中的基础信息。非参数化蒸散发估算方法避免了复杂的参数化过程, 降低了计算过程的不确定性, 具有广阔的应用前景。本文基于华北灌溉农田中国科学院栾城农业生态系统试验站、中国科学院禹城综合试验站和北京师范大学馆陶试验站3个通量站点的观测数据, 利用非参数化方法估算3个站点30 min和日尺度蒸散发, 利用能量残差闭合修正方法修正后的通量数据为验证参考值, 评价非参数化蒸散发估算方法在华北平原灌溉农田不同季节和不同时间尺度的适用性。结果显示: 1) 非参数化方法在华北灌溉农田不同作物类型、不同时间尺度具有可靠和稳健的表现, 估算结果可以较好地反映季节及日内变化特征, 但总体上低估蒸散发; 对比估算值与参考值, 在日尺度上, 平均偏差为−16.18~−12.88 W∙m−2, 决定系数为0.80~0.83, 均方根误差为22.45~31.06 W∙m−2, Nash-Sutcliffe效率系数为0.66~0.75; 在30 min尺度上, 平均偏差为−13.30~−7.68 W∙m−2, 决定系数均为0.88, 均方根误差为39.22~42.15 W∙m−2, Nash-Sutcliffe效率系数为0.86~0.87。2)非参数化估算方法在水分供应较充足或作物生长茂盛时较严重低估潜热通量, 而在较干燥或作物稀疏时轻度低估或不低估潜热通量。3)该方法对灌溉活动的响应考虑不足, 可能需要在模型结构上进一步改进以提高灌溉农田模拟精度。4)非参数化估算方法在华北灌溉农田中参数敏感性从高到低依次为地表空气温度、地表温度、地表净辐射和土壤热通量, 其中可忽略土壤热通量的影响。该研究不仅为非参数化蒸散发估算方法改进提供参考, 而且有助于加深对蒸散发理论的认识。

     

    Abstract: Evapotranspiration (ET), generated by the evaporation of water from a natural surface into the atmosphere, is an important component of the water cycle and surface energy balance system, which is a fundamental information for agriculture, water resource management, and climate change research. In recent decades, the estimation of ET or latent energy (LE, which is the amount of heat required for ET) has remained one of the most challenging problems for researchers. A nonparametric approach for estimating ET may avoid the complex parameterization process and reduce the calculation uncertainties; therefore, it has broad application prospects. However, a more detailed applicability evaluation of the nonparametric approach in different regions or ecosystems is needed, as most of the current studies on the application of this approach focus on arid basins, with few applicability analysis reports focusing on irrigated farmland in sub-humid areas. In this study, the eddy covariance data modified by the energy residual closed correction method in three irrigated farmland stations (Luancheng Agroecosystem Experimental Station, Chinese Academy of Sciences; Yucheng Comprehensive Experimental Station, Chinese Academy of Sciences; and Guantao Experimental Station, Beijing Normal University) in the North China Plain were used as references, and the applicability of the nonparametric approach for estimating LE on irrigated farmland at different time scales (daily scale and 30 min scale) and seasons in the three stations was evaluated. The results showed that the nonparametric approach had reliable and robust performance for different crop types and time scales on irrigated farmland in the North China Plain. The estimated LE could ideally reflect seasonal and intraday variations, but these values were generally underestimated. Furthermore, the bias, coefficient of determination, root mean square error, and Nash-Sutcliffe coefficient at the daily (and 30 min) scale were −16.18 to −12.88 W∙m−2 (−13.30 to −7.68 W∙m−2), 0.80 to 0.83 (0.88), 22.45 to 31.06 W∙m−2 (39.22 to 42.15 W∙m−2), and 0.66 to 0.75 (0.86 to 0.87), respectively. The nonparametric approach significantly underestimated the latent heat flux when the water supply was sufficient or when crops were growing vigorously; moreover, this approach slightly or not underestimated the latent heat flux when the water supply was insufficient or when crops were sparse. In addition, the response of the nonparametric approach to irrigation activities was not considered sufficiently, and further improvement to the model structure may be required to improve the simulation accuracy of irrigated farmland. Finally, the parameter sensitivity of the nonparametric approach in irrigated farmland in the North China Plain, from high to low, was air temperature, surface temperature, net radiation, and soil heat flux, but the influence of soil heat flux can be ignored. Ultimately, this study not only provides a reference for the improvement of the nonparametric ET estimation approach but also helps further the understanding of ET fundamental theory.

     

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