干旱区生态环境知识资源中心(Arid): Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models

Arid

DOI	10.3390/atmos10040188
	Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models
	Sun, Huaiwei 1; Yang, Yong 1; Wu, Ruiying 1; Gui, Dongwei 2; Xue, Jie 2; Liu, Yi 2; Yan, Dong 1
通讯作者	Sun, Huaiwei ; Yan, Dong
来源期刊	ATMOSPHERE
EISSN	2073-4433
出版年	2019
卷号	10 期号:4
英文摘要	Evapotranspiration (ET) is one of the key components of the global hydrological cycle. Many models have been established to obtain an accurate estimation of ET, but the uncertainty of each model has not been satisfactorily addressed, and the weight determination in multi-model simulation methods remains unclear. In this study, the Bayesian model averaging (BMA) method was adopted to tackle this issue. We explored the combination of four surface energy balance (SEB) models (SEBAL, SSEB, S-SEBI and SEBS) with the BMA method by using Landsat 8 images over two study areas in China, the Huailai flux station (semiarid region) and the Sidaoqiao flux station (arid/semiarid region), and the data from two stations were used as validation for this method. The performances of SEB models and different BMA methods is revealed by three statistical parameters (i.e., the coefficient of determination (R-2), root mean squared error (RMSE), and the Nash-Sutcliffe efficiency coefficient (NSE)). We found the best performing SEB model was SEBAL, with an R-2 of 0.609 (0.672), RMSE of 1.345 (0.876) mm/day, and NSE of 0.407 (0.563) at Huailai (Sidaoqiao) station. Compared with the four individual SEB models, each of the BMA methods (fixed, posterior inclusion probability, or random) can provide a more accurate and reliable simulation result. Similarly, in Huailai (Sidaoqiao) station, the best performing BMA random model provided an R-2 of 0.750 (0.796), RMSE of 0.902 (0.602) mm/day, and NSE of 0.746 (0.793). We conclude that the BMA method outperformed the four SEB models alone and obtained a more accurate prediction of ET in two cropland areas, which provides important guidance for water resource allocation and management in arid and semiarid regions.
英文关键词	evapotranspiration model average Bayesian model averaging (BMA) remote sensing Landsat 8
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000467313400027
WOS关键词	ATMOSPHERE WATER FLUX ; TERRESTRIAL EVAPOTRANSPIRATION ; MAPPING EVAPOTRANSPIRATION ; RIVER-BASIN ; UNCERTAINTY ; VALIDATION ; ALGORITHM ; CLOSURE ; SEBS
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
来源机构	中国科学院新疆生态与地理研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/214444
作者单位	1.Huazhong Univ Sci & Technol, Coll Hydropower & Informat Engn, Wuhan 430074, Hubei, Peoples R China; 2.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Cele Natl Stn Observat & Res Desert Grassland Eco, Urumqi 830011, Peoples R China
推荐引用方式 GB/T 7714	Sun, Huaiwei,Yang, Yong,Wu, Ruiying,et al. Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models[J]. 中国科学院新疆生态与地理研究所,2019,10(4).
APA	Sun, Huaiwei.,Yang, Yong.,Wu, Ruiying.,Gui, Dongwei.,Xue, Jie.,...&Yan, Dong.(2019).Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models.ATMOSPHERE,10(4).
MLA	Sun, Huaiwei,et al."Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models".ATMOSPHERE 10.4(2019).