Global Estimates of Reach-Level Bankfull River Width Leveraging Big Data Geospatial Analysis

Lin，Peirong1; Pan，Ming1; Allen，George H.2; de Frasson，Renato Prata3; Zeng，Zhenzhong1,4; Yamazaki，Dai5; Wood，Eric F.1

2020-04-16

10.1029/2019GL086405

GEOPHYSICAL RESEARCH LETTERS   影响因子和分区

0094-8276

1944-8007

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 (R = 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.

channel geometry global bankfull river width hydrodynamic modeling machine learning

SCI ; EI

英语

其他

NASA on Algorithm Development for SWOT River Discharge Retrievals[NNX16AH84G] ; NASA SWOT Science Team Project[NNX16AH82G] ; NASA THP[NNH17ZDA001N]

Geology

Geosciences, Multidisciplinary

WOS:000560367600036

AMER GEOPHYSICAL UNION

20201708509848

Big data ; Machine learning ; Stream flow ; Remote sensing ; Parameterization

Waterways:407.2 ; Fluid Flow, General:631.1 ; Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Mathematics:921

GEOSCIENCES

2-s2.0-85083511918

Scopus

1.Department of Civil and Environmental Engineering,Princeton University,Princeton,United States
2.Department of Geography,Texas A&M University,College Station,United States
3.Byrd Polar and Climate Research Center,The Ohio State University,Columbus,United States
4.Now at School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,China
5.Institute of Industrial Science,The University of Tokyo,Tokyo,Japan

Lin，Peirong,Pan，Ming,Allen，George H.,et al. Global Estimates of Reach-Level Bankfull River Width Leveraging Big Data Geospatial Analysis[J]. GEOPHYSICAL RESEARCH LETTERS,2020,47(7).

Lin，Peirong.,Pan，Ming.,Allen，George H..,de Frasson，Renato Prata.,Zeng，Zhenzhong.,...&Wood，Eric F..(2020).Global Estimates of Reach-Level Bankfull River Width Leveraging Big Data Geospatial Analysis.GEOPHYSICAL RESEARCH LETTERS,47(7).

Lin，Peirong,et al."Global Estimates of Reach-Level Bankfull River Width Leveraging Big Data Geospatial Analysis".GEOPHYSICAL RESEARCH LETTERS 47.7(2020).