1. Summary 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", Geophysical Research Letters (accepted). 2. File Description Shapefile storing machine learning-derived bankfull river width, and environmental covariates used to predict the width (~1.4GB). The polylines were vectorized by Lin et al. (2019) based on the Multi-Error Removed Improved-Terrain (MERIT) DEM and MERIT Hydro (Yamazaki et al., 2017, 2019), under a channelization threshold of 25 km2. 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 et al., 2019). 3. Attribute Description COMID: identification number of the river reach, same as that used in global river modeling by Lin et al., (2019); Order: Strahler-Horton stream order, with stream order 1 starting from those with an upstream drainage area of 25 km2; Area: Upstream drainage basin area in km2; Sin: Sinuosity of the river segment (unitless); Slp: mean slope of the river segment (unitless); Elev: mean elevation of the river segment; K: mean bedrock permeability of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018); P: mean bedrock porosity of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018); AI: mean aridity index of the unit catchment; data extracted from Trabucco & Zomer (2019); LAI: mean leaf area index of the unit catchment; data extracted from Zhu et al. (2013); SND: mean sand content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017); CLY: mean clay content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017); SLT: mean silt content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017); Urb: mean urban fraction of the unit catchment; data extracted from Liu et al. (2018); WTD: mean water table depth (m below surface) of the unit catchment; data extracted from Fan et al. (2013); HW: mean human water use (irrigational + industrial + domestic) of the unit catchment; data extracted from Wada et al. (2016) DOR: degree of dam regulation for the river segment; the definition of DOR and data were sourced from Grill et al. (2019) QMEAN: mean annual discharge (m3/s) for the river segment; the multi-year averaged were calculated from Lin et al. (2019); Q2: 2-year return period flood discharge (m3/s) for the river segment; the 35-year data used to calculate the field was sourced from Lin et al. (2019); Width_m: bankfull river width (m) estimated by using the optimized machine learning model of this study, applied to Q2 and other environmental covariates; Width_DHG: bankfull river width (m) estimated by using the Moody & Troutman (2002) equation applied to Q2 estimated in this study 4. References Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585–588. https://doi.org/10.1126/science.aat0636 Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940–943. https://doi.org/10.1126/science.1229881 Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., et al. (2019). Mapping the world’s free-flowing rivers. Nature, 569(7755), 215. https://doi.org/10.1038/s41586-019-1111-9 Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., et al. (2017). 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