Time-lag Effects Between Meteorological Factors and Transpiration of

XIA Yinhua; ZHANG Xinping; DAI Junjie; WANG Rui; LUO Zidong

doi:10.13870/j.cnki.stbcxb.2021.05.027

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Time-lag Effects Between Meteorological Factors and Transpiration of

更新时间：2025-03-12

- Time-lag Effects Between Meteorological Factors and Transpiration of
- Vol. 35, Issue 5, Pages: 194-203(2021)
- 作者机构：
  
  1. 湖南师范大学地理科学学院,长沙,410081
  2. 湖南师范大学地理空间大数据挖掘与应用湖南省重点实验室,长沙,410081
- 作者简介：
- 基金信息：
- DOI：10.13870/j.cnki.stbcxb.2021.05.027
  CLC： S175.4
- Published：2021
- 稿件说明：
移动端阅览
XIA Yinhua, ZHANG Xinping, DAI Junjie, et al. Time-lag Effects Between Meteorological Factors and Transpiration of[J]. 2021, 35(5): 194-203.
DOI：

XIA Yinhua, ZHANG Xinping, DAI Junjie, et al. Time-lag Effects Between Meteorological Factors and Transpiration of[J]. 2021, 35(5): 194-203. DOI： 10.13870/j.cnki.stbcxb.2021.05.027.

摘要

以亚热带地区常绿阔叶树种樟树（Cinnamomum camphora）为研究对象，利用Grainer热扩散式探针技术，于2019年6月至2020年10月测定了樟树小时尺度的蒸腾速率，并同步监测了气象因子和土壤含水量，分析了樟树蒸腾在日间不同时段与主要气象因子的关系、时滞的变化特征以及时滞校正对蒸腾预测模型的影响。结果表明：蒸腾在生长季（6—10月）与太阳辐射的时滞回环呈逆时针，与饱和水汽压差和气温的时滞回环呈顺时针。利用Gauss方程拟合发现，2019年与2020年生长季蒸腾平均滞后于太阳辐射0.94 h，提前于饱和水汽压差和气温2.60，2.61 h。这种时滞效应是蒸腾在日内不同阶段对气象因子的响应不同导致的，蒸腾在上升阶段（7：00—11：00）对太阳辐射变化的响应较下降阶段（17：00—21：00）敏感，而在上升阶段蒸腾对饱和水汽压差和气温变化的响应不如下降阶段敏感。蒸腾与太阳辐射、饱和水汽压差和气温的时滞效应存在明显的季节变化，2019年与2020年的归一化时滞回环面积存在一定差异，但均在10月最大，8月或9月最小。小时尺度上，土壤水分充足时，时滞随主要日均气象因子的增大而增大，土壤水分胁迫时，随主要日均气象因子的增大，时滞变化不明显。时滞校正后，太阳辐射和饱和水汽压差与蒸腾的回归方程的决定系数分别提高了4%和9%，运用主成分分析构造的综合气象因子对蒸腾模拟的决定系数提高了7%，时滞校正可以提高蒸腾预测模型的精度。旨在加深对植物生理过程与环境变化过程关系的认识，并为提高蒸腾预测模型的精度提供依据。

Abstract

In this study

the hourly transpiration rate of Cinnamomum camphora was measured from June 2019 to October 2020 using the Grainer-type thermal dissipation probes. Meteorological factors and soil water content were simultaneously monitored during the study period. To deepen the understanding of the relationship between plant physiological changes and environmental changes

and to provide a basis for improving the accuracy of transpiration predicting model

this study analyzed the relationship between the transpiration of C. camphora and the main meteorological factors at different periods during the day. The variation characteristics of time lag and the change of hysteresis correction on the predicting model of transpiration were also investigated. The results showed that the time lag between transpiration and solar radiation is counterclockwise

and that between transpiration and saturated water vapor pressure or temperature is clockwise during the growing seasons (from June to October). Based on Gauss equations

it was found that on average transpiration in the growing seasons of 2019 and 2020 lagged behind solar radiation by 0.94 hours

and advanced by 2.60 and 2.61 hours on saturated vapor pressure difference and temperature. This time-lag effect was caused by the different responses of transpiration to meteorological factors at different stages of the day. In the rising stage (7:00-11:00)

transpiration was more sensitive to changes of solar radiation than that in the descending stage (17:00-21:00). On the contrary

in the rising stage

transpiration was less sensitive to variation in saturated vapor pressure and temperature than that in the descending stage. The time-lag effects between transpiration and solar radiation or saturated vapor pressure or temperature had obvious seasonal variation. There were certain differences in the hysteresis loop area between the growing seasons of 2019 and 2020

but the maximum area appeared in October and the minimum area appeared in August or September. On the hourly scale

when soil moisture was sufficient

the hysteresis added with the increase of the main daily mean meteorological factors

but under soil moisture stress

the time lag did not change significantly with the increase of the major daily mean meteorological factors. After correcting time lag

the determinants of the regression equations of transpiration and solar radiation increased by 4% and 9%

and the coefficient of determination of the regression equations of transpiration and integrated meteorological factors constructed by principal component analysis increased by 7%. Therefore

eliminating time lag could improve the accuracy of transpiration predicting model.

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