Partitioning Evapotranspiration into Soil Evaporation and Canopy Transpiration via a Two-Source Variational Data Assimilation System

作者:Xu, T (Xu, Tongren)[ 1,2 ] ; Bateni, SM (Bateni, Sayed M.)[ 3,4 ] ; Margulis, SA (Margulis, Steven A.)[ 5 ] ; Song, LS (Song, Lisheng)[ 1,2 ] ; Liu, SM (Liu, Shaomin)[ 1,2 ] ; Xu, ZW (Xu, Ziwei)[ 1,2 ]

JOURNAL OF HYDROMETEOROLOGY

卷: 17  期: 9  页: 2353-2370

DOI: 10.1175/JHM-D-15-0178.1

出版年: SEP 2016

摘要

The primary objective of this study is to assess the accuracy of the two-source variational data assimilation (TVDA) system for partitioning evapotranspiration (ET) into soil evaporation (ETS) and canopy transpiration (ETC). Its secondary aim is to compare performance of the TVDA system with the commonly used two-source surface energy balance (TSEB) method. A combination of eddy-covariance-based ET observations and stable-isotope-based measurements of the ratio of evaporation and transpiration to total evapotranspiration (ETS/ET and ETC/ET) over an irrigated cropland site (the so-called Daman site) in the middle reach of the Heihe River basin (northwestern China) was used to investigate these objectives. The results indicate that the TVDA method predicts ETS and ETC more accurately than TSEB. Root-mean-square errors (RMSEs) of midday (1300-1500 LT) averaged soil and canopy latent heat flux (LES and LEC) estimates from TVDA are 23.1 and 133.0 W m(-2), respectively. Corresponding RMSE values from TSEB are 41.9 and 156.0 W m(-2). Compared to TSEB, the TVDA method takes advantage of all of the information in land surface temperature observations in the estimation period by leveraging a dynamic model (the heat diffusion equation) and thus can generate more accurate LES and LEC estimates.

通讯作者地址: Xu, T (通讯作者)

Beijing Normal Univ, Res Ctr Remote Sensing & GIS, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.

通讯作者地址: Xu, T (通讯作者)

Beijing Normal Univ, Sch Geog, 19 Xinjiekouwai St, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Res Ctr Remote Sensing & GIS, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

[ 2 ] Beijing Normal Univ, Sch Geog, 19 Xinjiekouwai St, Beijing 100875, Peoples R China

[ 3 ] Univ Hawaii Manoa, Dept Civil & Environm Engn, Honolulu, HI 96822 USA

[ 4 ] Univ Hawaii Manoa, Water Resources Res Ctr, Honolulu, HI 96822 USA

[ 5 ] Univ Calif Los Angeles, Dept Civil & Environm Engn, Los Angeles, CA USA