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Mapping flow-obstructing structures on global rivers
  • +15
  • Xiao Yang,
  • Tamlin M Pavelsky,
  • Matthew Richard Voss Ross,
  • Stephanie R Januchowski-Hartley,
  • Wayana Dolan,
  • Elizabeth Humphries Altenau,
  • Michael Belanger,
  • Danesha Byron,
  • Michael Durand,
  • Ian Van Dusen,
  • Hailey Galit,
  • Michiel Jorissen,
  • Theodore Langhorst,
  • Eric Lawton,
  • Riley Lynch,
  • Katie Ann Mcquillan,
  • Sayali K Pawar,
  • Aaron Whittemore
Xiao Yang
University of North Carolina at Chapel Hill

Corresponding Author:[email protected]

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Tamlin M Pavelsky
University of North Carolina at Chapel Hill
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Matthew Richard Voss Ross
Colorado State University
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Stephanie R Januchowski-Hartley
Swansea University
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Wayana Dolan
University of North Carolina at Chapel Hill
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Elizabeth Humphries Altenau
University of North Carolina at Chapel Hill
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Michael Belanger
University of North Carolina at Chapel Hill
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Danesha Byron
University of North Carolina at Chapel Hill
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Michael Durand
Ohio State University
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Ian Van Dusen
Ohio State University
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Hailey Galit
University of North Carolina at Chapel Hill
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Michiel Jorissen
Independent Research Aide
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Theodore Langhorst
University of North Carolina at Chapel Hill
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Eric Lawton
Independent Research Aide
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Riley Lynch
Colorado State University
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Katie Ann Mcquillan
North Carolina State University
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Sayali K Pawar
University of Dundee
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Aaron Whittemore
Washington State University
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Abstract

To help store water, facilitate navigation, generate energy, mitigate floods, and support industrial and agricultural production, people have built and continue to build obstructions to natural flow in rivers. However, due to the long and complex history of constructing and removing such obstructions, we lack a globally consistent record of their locations and types. Here, we used a consistent method to visually locate and classify obstructions on 2.1 million km of large rivers (width ≥ 30m) globally. We based our mapping on Google Earth Engine’s high resolution images from 2018–2020, which for many places have meter-scale resolution. The resulting dataset, the Global River Obstruction Database (GROD), consists of 29,877 unique obstructions, covering six different obstruction types: dam, lock, low head dam, channel dam, and two types of partial dams. By classifying a subset of the obstructions multiple times, we are able to show high classification consistency (87% mean balanced accuracy) for the three types of obstructions that fully intersect rivers: dams, low head dams, and locks. The classification of the three types of partial obstructions are somewhat less consistent (61% mean balanced accuracy). Overall, by comparing GROD to similar datasets, we estimate GROD likely captured 90% of the obstructions on large rivers. We anticipate that GROD will be of wide interest to the hydrological modeling, aquatic ecology, geomorphology, and water resource management communities.
Jan 2022Published in Water Resources Research volume 58 issue 1. 10.1029/2021WR030386