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Estimation of evapotranspiration of "soil-vegetation" system with a scheme combining a dual-source model and satellite data assimilation
被引:28
作者:
Cui, Yaokui
[1
,2
]
Jia, Li
[3
]
机构:
[1] Peking Univ, Sch Earth & Space Sci, Inst RS&GIS, Beijing 100871, Peoples R China
[2] Beijing Key Lab Spatial Informat Integrat & Its A, Beijing 100871, Peoples R China
[3] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
基金:
中国国家自然科学基金;
关键词:
Evapotranspiration;
Soil and vegetation;
Shuttleworth and Wallace;
Data assimilation;
Irrigated area;
GLOBAL TERRESTRIAL EVAPOTRANSPIRATION;
RIVER-BASIN;
ENERGY-BALANCE;
INTERCEPTION LOSS;
LAND;
EVAPORATION;
MOISTURE;
ALGORITHM;
MICROWAVE;
FLUXES;
D O I:
10.1016/j.jhydrol.2021.127145
中图分类号:
TU [建筑科学];
学科分类号:
0813 ;
摘要:
Evapotranspiration (ET) is a key variable linking the energy and water exchanges between the land surface and the atmosphere. The strong energy exchanges between the soil and vegetation in the "soil-vegetation" system and the lack of reliable soil moisture products at fine spatial scales, especially for root zone soil moisture, to directly model the effects of water stress on ET estimates, make it difficult to estimate actual ET and its components (soil evaporation Es, vegetation transpiration Tc and rainfall interception Ei). In this paper, we presented a "SoilVegetation" EvapoTranspiration framework (SVET) by combining the dual-source Shuttleworth and Wallace (SW) physical model and satellite soil moisture data assimilation procedure. In the SVET model, net radiation for the soil-vegetation system was first partitioned among soil evaporation, vegetation transpiration and rainfall interception; then, soil moisture was simulated for different soil layers based on the water balance, providing a first guess estimate of soil moisture; after that, simulated soil moisture was improved by assimilating remotely sensed surface soil moisture to reduce the uncertainty of the simulated soil moisture; finally, Es, Tc and Ei estimates were obtained. The ET estimates by the SVET were evaluated against in situ measurements at three stations in the Heihe River Basin with different vegetation cover types (crop, grassland and forest). The overall correlation coefficient (R), root mean square error (RMSE) and bias were 0.931, 0.67 mm d-1 and 0. 07 mm d-1, respectively. For the ET components, the bias of the estimated ratio of Tc/(Es + Tc) is -11.4%. In addition, the SVET model showed outstanding performance, especially over irrigated areas, compared with the results from the original SW model without a soil moisture data assimilation scheme. The SVET model is very promising for applications of water use monitoring of ecosystem, especially for irrigated areas.
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