气候变化领域集成服务门户(CCPortal): Estimating particulate organic carbon flux in a highly dynamic estuary using satellite data and numerical modeling

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DOI	10.1016/j.rse.2020.112116
	Estimating particulate organic carbon flux in a highly dynamic estuary using satellite data and numerical modeling
	Wang Z.; Bai Y.; He X.; Tao B.; Li T.; Chen X.; Wang T.; Gong F.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	Quantitative estimates of the flux, transportation, and burial of riverine particulate organic carbon (POC) in estuaries and shelf areas are crucial for understanding the carbon cycle in marginal seas. The complexity of the hydrodynamic environment and estuarine processes in the Changjiang River estuary (CRE) results in a POC flux with high spatiotemporal variability. In this study, we proposed a new method for time series monitoring of riverine POC flux by combining satellite ocean color and numerical modeling data. We used 500-m resolution geostationary satellite data from the Geostationary Ocean Color Imager (GOCI) to retrieve surface POC concentrations in the CRE and used the numerical Finite Volume Community Ocean Model (FVCOM) to simulate three-dimensional (3D) current and sediment distribution. Diurnal, seasonal, and annual variations in POC flux through the lower reaches (Datong Hydrological Station), upper estuary (Xuliujing Hydrological Station), and outlets of the CRE, as well as transportation and deposition outside the CRE, were examined from July 2011 to June 2018. The annual mean POC fluxes through the Datong, Xuliujing, and outlet sections were 1.16 ± 0.16 Tg C/yr, 1.29 ± 0.11 Tg C/yr, and 1.17 ± 0.11 Tg C/yr, respectively. After passing through the estuary, 35.2% of POC was deposited in the sandbar outside the outlets, 52.9% was transported to the south, and a small amount entered the eastern shelf. Thus, these results indicated that POC flux through the river and estuary sections differed from the effective riverine POC flux into the sea. The proposed method combining high spatiotemporal-resolution satellite data and numerical modeling not only makes full use of their advantages, but also reduces the uncertainty of their individual estimates. This approach also supports long-term monitoring of riverine fluxes to the sea and helps clarify the effects of terrestrial inputs on marginal seas under multiple stresses from human activities and climate change. © 2020
英文关键词	Changjiang River estuary (CRE); Geostationary Ocean Color Imager (GOCI); Numerical model; Particulate organic carbon flux; Total suspended materials
语种	英语
scopus关键词	Climate change; Estuaries; Geostationary satellites; Numerical methods; Organic carbon; Uncertainty analysis; Changjiang River Estuary; Particulate organic carbon; Particulate organic carbon fluxes; Quantitative estimates; Satellite Ocean Color; Spatio-temporal resolution; Spatiotemporal variability; Threedimensional (3-d); Numerical models; carbon cycle; carbon flux; estimation method; flux measurement; geostationary satellite; GOCI; numerical model; ocean color; organic carbon; particulate organic carbon; satellite data; China; Datong; Shanxi; Yangtze Estuary
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179080
作者单位	State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China; School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200030, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 510000, China; Ocean College, Zhejiang University, Zhoushan, 316000, China; National Earth System Science Data Center, Beijing, 100101, China
推荐引用方式 GB/T 7714	Wang Z.,Bai Y.,He X.,et al. Estimating particulate organic carbon flux in a highly dynamic estuary using satellite data and numerical modeling[J],2021,252.
APA	Wang Z..,Bai Y..,He X..,Tao B..,Li T..,...&Gong F..(2021).Estimating particulate organic carbon flux in a highly dynamic estuary using satellite data and numerical modeling.Remote Sensing of Environment,252.
MLA	Wang Z.,et al."Estimating particulate organic carbon flux in a highly dynamic estuary using satellite data and numerical modeling".Remote Sensing of Environment 252(2021).