QQ客服 官方微博 反馈留言

	Assimilating multi-source data into land surface model to simultaneously improve estimations of soil moisture, soil temperature, and surface turbulent fluxes in irrigated fields
	Huang, Chunlin1,5; Chen, Weijing2; Li, Yan1,3; Shen, Huanfeng2; Li, Xin1,4
刊名	AGRICULTURAL AND FOREST METEOROLOGY
	2016-12-15
卷号	230页码:142-156
关键词	Data assimilation Ensemble Kalman Smoother Soil moisture Soil temperature Surface turbulent fluxes Common Land Model
ISSN号	0168-1923
DOI	10.1016/j.agrformet.2016.03.013
通讯作者	Huang, Chunlin(huangcl@lzb.ac.cn)
英文摘要	The optimal estimation of soil moisture, soil temperature, and surface turbulent fluxes in irrigation fields is restricted by a lack of accurate irrigation information. To resolve the input uncertainty from imprecise irrigation quantity, an improved data assimilation scheme that is EnKS (Ensemble Kalman Smoother) implemented with inflation and localization (referred to as ESIL) is proposed to estimate soil moisture, soil temperature, and surface turbulent fluxes for irrigated fields by assimilating multi-source observations. The Daman station, which is located at an irrigated maize farmland in the middle reaches of the Heihe River Basin (HRB), is selected in this study to investigate the performance of the proposed assimilation scheme. The measured land surface temperature (LST) and surface soil moisture (SSM) in the first soil layer are taken as observations to conduct a series of data assimilation experiments to analyze the influence of a lack of irrigation information and combinations of multi-source observations on estimations of soil moisture, soil temperature, and surface turbulent fluxes. This study demonstrates the feasibility of ESIL in improving the estimation of hydrothermal conditions under unknown irrigation. The coefficient correlation (R) with the ESIL method increases from 0.342 and 0.703 to 0.877 and 0.830 for the soil moisture and soil temperature in the first layer, respectively. Meanwhile, the surface turbulent fluxes are significantly improved and the RMSE decreases from 173 W/m(2) and 186 W/m(2) to 97 W/m(2) and 111 W/m(2) for the sensible and latent heat fluxes, respectively. (C) 2016 Elsevier B.V. All rights reserved.
收录类别	SCI
WOS关键词	ENSEMBLE KALMAN SMOOTHER ; HEIHE RIVER-BASIN ; FILTER ; ALGORITHM ; CHINA
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
WOS类目	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000389731800013
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2557513
专题	寒区旱区环境与工程研究所
通讯作者	Huang, Chunlin
作者单位	1.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Heihe Remote Sensing Expt Res Stn, Key Lab Remote Sensing Gansu Prov, Lanzhou, Gansu, Peoples R China 2.Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Huang, Chunlin,Chen, Weijing,Li, Yan,et al. Assimilating multi-source data into land surface model to simultaneously improve estimations of soil moisture, soil temperature, and surface turbulent fluxes in irrigated fields[J]. AGRICULTURAL AND FOREST METEOROLOGY,2016,230:142-156.
APA	Huang, Chunlin,Chen, Weijing,Li, Yan,Shen, Huanfeng,&Li, Xin.(2016).Assimilating multi-source data into land surface model to simultaneously improve estimations of soil moisture, soil temperature, and surface turbulent fluxes in irrigated fields.AGRICULTURAL AND FOREST METEOROLOGY,230,142-156.
MLA	Huang, Chunlin,et al."Assimilating multi-source data into land surface model to simultaneously improve estimations of soil moisture, soil temperature, and surface turbulent fluxes in irrigated fields".AGRICULTURAL AND FOREST METEOROLOGY 230(2016):142-156.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。