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

Name: Global estimates of reach-level bankfull river width leveraging big-data geospatial analysis
Creator: Zenodo
Published: 2019-11-25T17:26:36+08:00

Zenodo2024-12-03 更新2026-05-25 收录

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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/rs5020927

1. 摘要本数据集为Peirong Lin、Ming Pan、George H. Allen、Renato Frasson、Zhenzhong Zeng、Dai Yamazaki、Eric F. Wood发表于《地球物理研究快报》（Geophysical Research Letters）（已接收）的论文《借助大数据地理空间分析的全球河段行洪河道宽度》中生成的全球河段行洪河道宽度（bankfull river width）估算结果。 2. 文件说明本数据集为存储机器学习衍生的行洪河道宽度（bankfull river width）及用于预测该宽度的环境协变量的Shapefile格式文件（约1.4GB）。这些线状矢量要素由Lin et al.（2019）基于多误差修正改进地形（Multi-Error Removed Improved-Terrain, MERIT）数字高程模型（Digital Elevation Model, DEM）与MERIT Hydro数据集（Yamazaki et al.，2017、2019），以25 km²为河道汇流阈值矢量化得到。本数据集仅展示宽度大于30 m的河道；这些河道点位通过联合使用陆地卫星全球河道宽度（Global River Widths from Landsat, GRWL）数据库（Allen & Pavelsky, 2018）与MERIT Hydro宽度估算结果（Yamazaki et al.，2019）确定。 3. 属性说明 COMID：河段标识号，与Lin et al.（2019）全球河道建模中使用的编号一致； Order：斯特拉勒-霍顿河流级（Strahler-Horton stream order），其中1级河流源自上游汇流面积为25 km²的河道； Area：河段上游汇流区面积（单位：km²）； Sin：河段蜿蜒度（无量纲）； Slp：河段平均坡度（无量纲）； Elev：河段平均高程； K：河段周边单元流域的平均基岩渗透率，数据源自Huscroft et al.（2018）； P：河段周边单元流域的平均基岩孔隙度，数据源自Huscroft et al.（2018）； AI：单元流域平均干旱指数，数据源自Trabucco & Zomer（2019）； LAI：单元流域平均叶面积指数（Leaf Area Index, LAI），数据源自Zhu et al.（2013）； SND：单元流域平均砂粒含量（质量百分比，%），数据源自Hengl et al.（2017）； CLY：单元流域平均黏粒含量（质量百分比，%），数据源自Hengl et al.（2017）； SLT：单元流域平均粉粒含量（质量百分比，%），数据源自Hengl et al.（2017）； Urb：单元流域平均城市用地占比，数据源自Liu et al.（2018）； WTD：单元流域平均地下水位埋深（m，地表以下），数据源自Fan et al.（2013）； HW：单元流域平均人类用水量（灌溉+工业+生活用水），数据源自Wada et al.（2016）； DOR：河段大坝调控程度，DOR的定义与数据源自Grill et al.（2019）； QMEAN：河段年平均径流量（单位：m³/s），多年平均值由Lin et al.（2019）计算得到； Q2：河段2年一遇洪水流量（单位：m³/s），用于计算该字段的35年数据集源自Lin et al.（2019）； Width_m：本研究通过优化机器学习模型，结合Q2与其他环境协变量估算得到的行洪河道宽度（单位：m）； Width_DHG：基于本研究估算的Q2，套用Moody & Troutman（2002）公式得到的行洪河道宽度（单位：m）。 4. 参考文献 1. Allen, G. H., & Pavelsky, T. M. (2018). 全球河道与溪流分布范围. 《科学》（Science）, 361(6402), 585–588. https://doi.org/10.1126/science.aat0636 2. Fan, Y., Li, H., & Miguez-Macho, G. (2013). 全球地下水位埋深格局. 《科学》（Science）, 339(6122), 940–943. https://doi.org/10.1126/science.1229881 3. Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., et al. (2019). 全球自由流动河流制图. 《自然》（Nature）, 569(7755), 215. https://doi.org/10.1038/s41586-019-1111-9 4. Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., et al. (2017). SoilGrids250m：基于机器学习的全球格网土壤信息. 《公共科学图书馆·综合》（PLOS ONE）, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748 5. Huscroft, J., Gleeson, T., Hartmann, J., & Börker, J. (2018). 全球松散与固结基岩渗透率汇编与制图：GLobal HYdrogeology MaPS 2.0（GLHYMPS 2.0）. 《地球物理研究快报》（Geophysical Research Letters）, 45(4), 1897–1904. https://doi.org/10.1002/2017GL075860 6. Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). 294万个河段的天然河道流量全球重建. 《水资源研究》（Water Resources Research）, 0(0). https://doi.org/10.1029/2019WR025287 7. Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., et al. (2018). 基于Google Earth Engine平台的陆地卫星影像高分辨率全球时序城市用地制图. 《环境遥感》（Remote Sensing of Environment）, 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055 8. Trabucco, A., & Zomer, R. (2019, January 18). 全球干旱指数与潜在蒸散量（ET0）气候数据库v2. https://doi.org/10.6084/m9.figshare.7504448.v3 9. Wada, Y., Graaf, I. E. M. de, & Beek, L. P. H. van. (2016). 人类活动与气候变化对全球水资源影响的高分辨率模拟. 《地球系统建模进展杂志》（Journal of Advances in Modeling Earth Systems）, 8(2), 735–763. https://doi.org/10.1002/2015MS000618 10. Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., et al. (2017). 高精度全球地形高程图. 《地球物理研究快报》（Geophysical Research Letters）, 44(11), 5844–5853. https://doi.org/10.1002/2017GL072874 11. Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro：基于最新地形数据集的高分辨率全球水文制图. 《水资源研究》（Water Resources Research）. https://doi.org/10.1029/2019WR024873 12. Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., et al. (2013). 1981-2011年基于全球库存建模与制图研究归一化植被指数（GIMMS NDVI3g）的全球叶面积指数（LAI3g）与光合有效辐射吸收比例（FPAR3g）数据集. 《遥感》（Remote Sensing）, 5(2), 927–948. https://doi.org/10.3390/rs5020927

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2019-11-25

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

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