Global estimates of reach-level bankfull river width leveraging big-data geospatial analysis

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). SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748 Huscroft, J., Gleeson, T., Hartmann, J., & Börker, J. (2018). Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0). Geophysical Research Letters, 45(4), 1897–1904. https://doi.org/10.1002/2017GL075860 Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 0(0). https://doi.org/10.1029/2019WR025287 Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., et al. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055 Trabucco, A., & Zomer, R. (2019, January 18). Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. https://doi.org/10.6084/m9.figshare.7504448.v3 Wada, Y., Graaf, I. E. M. de, & Beek, L. P. H. van. (2016). High-resolution modeling of human and climate impacts on global water resources. Journal of Advances in Modeling Earth Systems, 8(2), 735–763. https://doi.org/10.1002/2015MS000618 Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., et al. (2017). A high-accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844–5853. https://doi.org/10.1002/2017GL072874 Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset. Water Resources Research. https://doi.org/10.1029/2019WR024873 Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., et al. (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sensing, 5(2), 927–948. https://doi.org/10.3390/rs502092