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基于Shuttleworth-Wallace模型的水稻蒸散组分模拟及其特征分析
引用本文:王宇,周莉,贾庆宇,王磊,徐军亮.基于Shuttleworth-Wallace模型的水稻蒸散组分模拟及其特征分析[J].中国农业气象,2017(11):709-719.
作者姓名:王宇  周莉  贾庆宇  王磊  徐军亮
作者单位:1. 河南科技大学林学院,洛阳,471023;2. 中国气象科学研究院,北京,100081;3. 中国气象局沈阳大气环境研究所,沈阳,110016
基金项目:国家自然科学基金项目(41205117),公益性行业(气象)科研专项(GYHY201506019),国家自然科学基金项目(41401063),河南科技大学博士科研启动基金项目(13480043)
摘    要:农田蒸散(ET)及其组分的模拟是精准灌溉及准确估算生产力的基础。基于2013-2015年的涡度相关通量观测及辅助观测资料,利用Shuttleworth-Wallace模型(S-W模型)对盘锦水稻的蒸散及其组分进行模拟,并利用结构方程模型分析土壤蒸发占蒸散比例(ES/ET)的控制机制。结果表明:(1)S-W模型模拟蒸散值在生长季前期偏低,在生长旺季总体偏高;而在生长季后期与观测蒸散基本吻合。(2)就季节变化过程而言,水稻蒸散模拟值呈现明显的日间波动(0.5~10.4mm·d-1),但季节总体变化趋势不明显;蒸腾(TR)则先增大后降低,变化范围为0.1~8.4mm·d-1;土壤蒸发(ES)呈U型曲线,变化范围为0.1~4.7mm·d-1。(3)模拟水稻蒸散3a均值为892mm。在年尺度上,TR与ES各占ET的50%;但在生长季,TR是ET的主要消耗方式:在移栽分蘖期,水稻的植物蒸腾与土壤蒸发较接近,而在其它各生育期及全生育期,水稻的植物蒸腾均达土壤蒸发的2倍以上。(4)结构方程模型分析结果表明,气温是ES/ET最重要的影响因子,ES/ET随气温上升而下降(总影响系数为-0.82)。气温不仅对ES/ET有显著的直接影响(直接影响系数为-0.50),还通过叶面积指数(LAI)对ES/ET产生显著的间接影响(间接影响系数为-0.32)。除气温外,LAI和风速也是ES/ET的重要影响因子,ES/ET随LAI增大而下降(总影响系数为-0.39),随风速增大而增大(总影响系数为0.38)。

关 键 词:结构方程模型  涡度相关  土壤蒸发  植物蒸腾

Characteristics of Evapotranspiration and Its Components Simulated Using Shuttleworth-Wallace Model in Rice Paddy Field
WANG Yu,ZHOU Li,JIA Qing-yu,WANG Lei,XU Jun-liang.Characteristics of Evapotranspiration and Its Components Simulated Using Shuttleworth-Wallace Model in Rice Paddy Field[J].Chinese Journal of Agrometeorology,2017(11):709-719.
Authors:WANG Yu  ZHOU Li  JIA Qing-yu  WANG Lei  XU Jun-liang
Abstract:The simulation of evapotranspiration (ET) and its components in croplands is critical for the precise irrigation and accurate estimation of ecosystem productivity. Based on the eddy-covariance flux measurement and ancillary data during 2013-2015, evapotranspiration and its components were simulated using the Shuttleworth-Wallace model (S-W model) in a rice paddy field in Panjin. The controlling mechanism of the ratio of soil evaporation to evapotranspiration (ES/ET) was analyzed with the structural equation modeling (SEM) method. The results showed that: (1) the simulated ET was close to the observed ET in the late growing season, however, it was lower than the observed ET in the early growing season and higher in the peak growing season. (2) As for the seasonal variation, the simulated ET showed a drastic day-to-day fluctuation (0.5-10.4mm·d-1) but no clear seasonal pattern; the plant transpiration (TR) was higher in the peak growing season and lower at the start and the end of the growing season, with the range of 0.1-8.4mm·d-1; ES showed a U-type curve, with the range of 0.1-4.7mm·d-1. (3) The simulated mean annual ET was 892mm during 2013-2015. TR was equal to ES at the annual scale. As for the growing season scale, TR was the main consumer of the ET: TR was close to ES in the transplanting-tillering stage, while in the other growth stages and the whole growing season, TR was more than twice as ES. (4) The SEM results indicated that air temperature (Ta) was the primary controlling factor of the ES/ET (total effect=-0.82). Ta was shown to influence ES/ET both directly (direct effect=-0.50) and indirectly through its regulation on leaf area index (LAI, indirect effect=-0.32). In addition, the LAI and wind speed (WS) were also shown to have significant effects on ES/ET. ES/ET decreased with LAI (total effect=-0.39) and increased with WS (total effect=0.38).
Keywords:Structural equation modeling  Eddy covariance  Soil evaporation  Plant transpiration
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