DOI: 10.5281/zenodo.3552776
title: Global estimates of reach-level bankfull river width leveraging big-data geospatial analysis

description: <strong>1. Summary</strong> Global estimates of reach-level bankfull river width generated in the article by Peirong Lin, Ming Pan, George H. Allen, Renato Frasson, Zhenzhong Zeng, Dai Yamazaki, Eric F. Wood entitled "Global reach-level bankfull river width leveraging big-data geospatial analysis", <em>Geophysical Research Letters (accepted)</em>. <strong>2. File Description</strong> Shapefile storing machine learning-derived bankfull river width, and environmental covariates used to predict the width (~1.4GB). The polylines were vectorized by Lin <em>et al.</em> (2019) based on the Multi-Error Removed Improved-Terrain (MERIT) DEM and MERIT Hydro (Yamazaki <em>et al.</em>, 2017, 2019), under a channelization threshold of 25 km<sup>2</sup>. Only rivers wider than 30 m are shown here; these locations were determined by jointly using the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018) and the MERIT Hydro width estimates (Yamazaki <em>et al.</em>, 2019). <strong>3. Attribute Description</strong> <strong>COMID</strong>: identification number of the river reach, same as that used in global river modeling by Lin <em>et al.</em>, (2019); <strong>Order</strong>: Strahler-Horton stream order, with stream order 1 starting from those with an upstream drainage area of 25 km<sup>2</sup>; <strong>Area</strong>: Upstream drainage basin area in km<sup>2</sup>; <strong>Sin</strong>: Sinuosity of the river segment (unitless); <strong>Slp</strong>: mean slope of the river segment (unitless); <strong>Elev</strong>: mean elevation of the river segment; <strong>K</strong>: mean bedrock permeability of the unit catchment surrounding the river segment, with data extracted from Huscroft <em>et al. </em>(2018); <strong>P</strong>: mean bedrock porosity of the unit catchment surrounding the river segment, with data extracted from Huscroft <em>et al. </em>(2018); <strong>AI</strong>: mean aridity index of the unit catchment; data extracted from Trabucco & Zomer (2019); <strong>LAI</strong>: mean leaf area index of the unit catchment; data extracted from Zhu <em>et al. </em>(2013); <strong>SND</strong>: mean sand content (mass percentage, %) of the unit catchment; data extracted from Hengl <em>et al.</em> (2017); <strong>CLY</strong>: mean clay content (mass percentage, %) of the unit catchment; data extracted from Hengl <em>et al.</em> (2017); <strong>SLT</strong>: mean silt content (mass percentage, %) of the unit catchment; data extracted from Hengl <em>et al.</em> (2017); <strong>Urb</strong>: mean urban fraction of the unit catchment; data extracted from Liu <em>et al.</em> (2018); <strong>WTD</strong>: mean water table depth (m below surface) of the unit catchment; data extracted from Fan <em>et al.</em> (2013); <strong>HW</strong>: mean human water use (irrigational + industrial + domestic) of the unit catchment; data extracted from Wada <em>et al.</em> (2016) <strong>DOR</strong>: degree of dam regulation for the river segment; the definition of DOR and data were sourced from Grill <em>et al.</em> (2019) <strong>QMEAN</strong>: mean annual discharge (m<sup>3</sup>/s) for the river segment; the multi-year averaged were calculated from Lin <em>et al.</em> (2019); <strong>Q2</strong>: 2-year return period flood discharge (m<sup>3</sup>/s) for the river segment; the 35-year data used to calculate the field was sourced from Lin <em>et al.</em> (2019); <strong>Width_m</strong>: bankfull river width (m) estimated by using the optimized machine learning model of this study, applied to Q2 and other environmental covariates; <strong>Width_DHG</strong>: bankfull river width (m) estimated by using the Moody & Troutman (2002) equation applied to Q2 estimated in this study <strong>4. 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