植物生态学报 ›› 2022, Vol. 46 ›› Issue (3): 300-310.DOI: 10.17521/cjpe.2021.0292

所属专题: 碳水能量通量

• 研究论文 • 上一篇    下一篇

蒸散发广义互补原理中关键参数αe的时空变化特征及计算方法分析

黄樱, 陈挚, 石喆, 熊博文, 鄢春华*(), 邱国玉*()   

  1. 北京大学深圳研究生院环境与能源学院, 广东深圳 518055
  • 收稿日期:2021-08-12 接受日期:2021-10-30 出版日期:2022-03-20 发布日期:2022-01-05
  • 通讯作者: 鄢春华,邱国玉
  • 作者简介:Qiu GY, qiugy@pkusz.edu.cn)
    * (Yan CH, yanch@pku.edu.cn;
  • 基金资助:
    四川省科技计划(2021YFH0082);国家自然科学基金(42001022)

Temporal and spatial variation characteristics and different calculation methods for the key parameter αe in the generalized complementary principle of evapotranspiration

HUANG Ying, CHEN Zhi, SHI Zhe, XIONG Bo-Wen, YAN Chun-Hua*(), QIU Guo-Yu*()   

  1. School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
  • Received:2021-08-12 Accepted:2021-10-30 Online:2022-03-20 Published:2022-01-05
  • Contact: YAN Chun-Hua,QIU Guo-Yu
  • Supported by:
    Sichuan Science and Technology Program(2021YFH0082);National Natural Science Foundation of China(42001022)

摘要:

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αe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αe年值及月值, 探究αe的时空变异性并对比使用不同时间尺度的αe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αe, 进一步探究两个基于干旱系数(AI)的αe年值统计模型(下称Liu法和Brutsaert法)在站点尺度的适用性, 明确αe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αe, 不同通量站αe月值变化规律有所差异; 在空间变化上, 湿润站点αe年值总体大于干旱站点。Liu法和Brutsaert法计算的αe接近年校准值。2)在应用广义互补原理模型时, 使用校准αe年值能取得较好的模拟精度, 使用各月份αe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αe时, 基于AI计算αe具有较大的潜力。3)使用校准αe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。

关键词: 实际蒸散发, 干旱系数, 互补原理, 涡度相关, 参数计算方法

Abstract:

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 αe is critical. The temporal and spatial variation of αe and the applicability of different methods for calculating αe were investigated at eight flux stations under different climatic conditions and ecosystem types in China.
Methods Firstly, the annual and monthly values of αe were calibrated based on the measured data. The spatiotemporal variability of αe was investigated and the influence of αe with different temporal scales on the calculation accuracy of the generalized complementarity principle model were compared. Considering that αe can not be calibrated without measured evapotranspiration data, the applicability of two statistical models of annual αe values based on aridity index (AI)(Liu method and Brutsaert method) were evaluated to determine whether αe can be determined using AI. Finally, the error sources of each calculation method were analyzed.
Important findings αe value varies with season, and the monthly variations of αe differ among different flux stations. In terms of spatial variation, the annual values of αe at humid sites were larger than those at arid sites. The αe 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 αe, and the accuracy can be further improved by using the monthly αe. Two AI-based methods also achieved accurate simulation results, which further confirmed the potential of predicting αe 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 αe, 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.

Key words: actual evapotranspiration, aridity index, complementary principle, eddy covariance, parameter calculation method