气候变化领域集成服务门户(CCPortal): Global Estimates of Reach-Level Bankfull River Width Leveraging Big Data Geospatial Analysis

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DOI	10.1029/2019GL086405
	Global Estimates of Reach-Level Bankfull River Width Leveraging Big Data Geospatial Analysis
	Lin P.; Pan M.; Allen G.H.; de Frasson R.P.; Zeng Z.; Yamazaki D.; Wood E.F.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:7
英文摘要	Recent progress in remote sensing has snapshotted unprecedented numbers of river planform geometry, providing opportunity to revisit the oversimplified channel shape parameterizations in global hydrologic models. This study leveraged two recent Landsat-derived global river width databases and created a reach-level width dataset to measure the validity of model parameterizations at ~1.6 million kilometers of rivers in length. By showing state-of-the-art parameterization schemes only capture 30–40% of the width variance globally, we developed a machine learning (ML) approach surveying 16 environmental covariates, which considerably improved the predictive power (R2 = 0.81 and 0.77 for two testing cases). Beyond the commonly discussed upstream basin conditions, ML revealed that local physiographic factors and human interference are also important covariates for width variability. Finally, we applied the ML model to estimate bankfull river width, creating a new reach-level dataset for use in global hydrodynamic modeling. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	Big data; Parameterization; Remote sensing; Stream flow; Geo-spatial analysis; Human interference; Hydrodynamic model; Hydrologic models; Parameterization schemes; Predictive power; Recent progress; State of the art; Rivers; data set; estimation method; hydrodynamics; hydrological modeling; Landsat; machine learning; river bank; river system
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/170491
作者单位	Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Department of Geography, Texas A&M University, College Station, TX, United States; Byrd Polar and Climate Research Center, The Ohio State University, Columbus, OH, United States; Now at School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China; Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
推荐引用方式 GB/T 7714	Lin P.,Pan M.,Allen G.H.,et al. Global Estimates of Reach-Level Bankfull River Width Leveraging Big Data Geospatial Analysis[J],2020,47(7).
APA	Lin P..,Pan M..,Allen G.H..,de Frasson R.P..,Zeng Z..,...&Wood E.F..(2020).Global Estimates of Reach-Level Bankfull River Width Leveraging Big Data Geospatial Analysis.Geophysical Research Letters,47(7).
MLA	Lin P.,et al."Global Estimates of Reach-Level Bankfull River Width Leveraging Big Data Geospatial Analysis".Geophysical Research Letters 47.7(2020).