蒸散发广义互补原理中关键参数α_(e)的时空变化特征及计算方法分析

Temporal and spatial variation characteristics and different calculation methods for the key parameter α_(e) in the generalized complementary principle of evapotranspiration

蒸散发广义互补原理是实测数据稀少条件下估算蒸散发的重要方法,其中准确估算参数α_(e)是应用该方法的关键。该研究利用中国不同气候和生态类型的8个通量站数据,首先基于实测数据校准得到α_(e)年值及月值,探究α_(e)的时空变异性并对比使用不同时间尺度的α_(e)对广义互补原理模型计算精度的影响。考虑到实际情况下蒸散发实测数据缺乏而无法校准得到α_(e),进一步探究两个基于干旱系数(AI)的α_(e)年值统计模型(下称Liu法和Brutsaert法)在站点尺度的适用性,明确α_(e)是否可以利用AI确定,最后探讨各计算方法的误差来源。主要结论如下:1)季节变化影响α_(e),不同通量站α_(e)月值变化规律有所差异;在空间变化上,湿润站点α_(e)年值总体大于干旱站点。Liu法和Brutsaert法计算的α_(e)接近年校准值。2)在应用广义互补原理模型时,使用校准α_(e)年值能取得较好的模拟精度,使用各月份α_(e)时精度进一步提升。两种基于AI的免校准方法取得较好的模拟效果,当缺少实测数据而无法校准α_(e)时,基于AI计算α_(e)具有较大的潜力。3)使用校准α_(e)年值时广义互补原理模型能模拟出蒸散发的年内变化趋势,但在部分月份估算值出现偏差。Liu法和Brutsaert法计算的蒸散发在干旱站点的夏季月份呈现低估现象,原因可能在于高估了降雨集中的夏季月份的AI。结果也进一步验证了广义互补原理在估算广泛不同的自然环境下的蒸散发的潜力。

Aims The generalized complementary principle of evapotranspiration is one of the important methods to estimate evapotranspiration when the observed data are scarce. In implementing this method, an accurate estimation of parameter α_(e)is critical. The temporal and spatial variation of α_(e)and the applicability of different methods for calculating α_(e)were investigated at eight flux stations under different climatic conditions and ecosystem types in China. Methods Firstly, the annual and monthly values of α_(e)were calibrated based on the measured data. The spatiotemporal variability of α_(e)was investigated and the influence of α_(e)with different temporal scales on the calculation accuracy of the generalized complementarity principle model were compared. Considering that α_(e)can not be calibrated without measured evapotranspiration data, the applicability of two statistical models of annual α_(e)values based on aridity index(AI)(Liu method and Brutsaert method) were evaluated to determine whether α_(e)can be determined using AI. Finally, the error sources of each calculation method were analyzed. Important findings α_(e)value varies with season, and the monthly variations of α_(e)differ among different flux stations. In terms of spatial variation, the annual values of α_(e)at humid sites were larger than those at arid sites. The α_(e)calculated by Liu method and Brutsaert method were close to the calibrated values. In applying the generalized complementary principle model, high simulation accuracy can be achieved by using the calibrated annual α_(e), and the accuracy can be further improved by using the monthly α_(e). Two AI-based methods also achieved accurate simulation results, which further confirmed the potential of predicting α_(e)based on AI in the absence of observed data. The generalized complementary principle model can simulate the annual variation trend of evapotranspiration when using annual α_(e), but the estimated value were biased in some months. The evapotranspiration calculated by Liu method and Brutsaert method were underestimated in summer months of the drought sites, which may be caused by the fact that the AI was overestimated in summer months when rainfall was concentrated. The results further demonstrate the potential of the generalized complementary principle in estimating evapotranspiration in a wide range of natural environments.