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Abstract High‐resolution raster hydrography maps are a fundamental data source for many geoscience applications. Here we introduce MERIT Hydro, a new global flow direction map at 3‐arc sec resolution (~90 m at the equator) derived from the latest elevation data (MERIT DEM) and water body data sets (G1WBM, Global Surface Water Occurrence, and OpenStreetMap). We developed a new algorithm to extract river networks near automatically by separating actual inland basins from dummy depressions caused by the errors in input elevation data. After a minimum amount of hand editing, the constructed hydrography map shows good agreement with existing quality‐controlled river network data sets in terms of flow accumulation area and river basin shape. The location of river streamlines was realistically aligned with existing satellite‐based global river channel data. Relative error in the drainage area was <0.05 for 90% of Global Runoff Data Center (GRDC) gauges, confirming the accuracy of the delineated global river networks. Discrepancies in flow accumulation area were found mostly in arid river basins containing depressions that are occasionally connected at high water levels and thus resulting in uncertain watershed boundaries. MERIT Hydro improves on existing global hydrography data sets in terms of spatial coverage (between N90 and S60) and representation of small streams, mainly due to increased availability of high‐quality baseline geospatial data sets. The new flow direction and flow accumulation maps, along with accompanying supplementary layers on hydrologically adjusted elevation and channel width, will advance geoscience studies related to river hydrology at both global and local scales.
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Dai Yamazaki
Tokyo University of Science
Daiki Ikeshima
Tokyo Institute of Technology
Jeison Sosa
At Bristol
SHILAP Revista de lepidopterología
Water Resources Research
University of North Carolina at Chapel Hill
The University of Tokyo
University of Bristol
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Yamazaki et al. (Tue,) studied this question.
synapsesocial.com/papers/69d738d98e958094d1b8a4e3 — DOI: https://doi.org/10.1029/2019wr024873
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