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/rs5020927

1. 概述
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》（已接收）的论文《基于大数据地理空间分析的全球河段级满岸河宽估算》中生成的全球河段级满岸河宽估算结果。

2. 文件说明
本数据集为存储机器学习推演得到的满岸河宽及用于预测河宽的环境协变量的形状文件（Shapefile），数据量约1.4GB。该数据集的多段线矢量成果由Lin等人（2019）基于多误差修正改进地形数字高程模型（Multi-Error Removed Improved-Terrain, MERIT DEM）与MERIT水文数据集（MERIT Hydro，Yamazaki等，2017、2019）生成，河道提取阈值设为25 km²。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基于Landsat的全球河宽数据库（Global River Widths from Landsat, GRWL，Allen & Pavelsky，2018）与MERIT水文数据集的河宽估算结果（Yamazaki等，2019）确定。

3. 属性说明
COMID：河段标识码，与Lin等人（2019）全球河道模拟中使用的标识码一致；
Order：Strahler-Horton河序，河序1级始于上游流域面积为25 km²的河道；
Area：上游流域面积，单位：km²；
Sin：河道弯曲度，无单位；
Slp：河道平均坡度，无单位；
Elev：河道平均海拔；
K：河道周边单元流域的基岩渗透率，数据源自Huscroft等（2018）；
P：河道周边单元流域的基岩孔隙度，数据源自Huscroft等（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，数据源自Zhu等（2013）；
SND：单元流域平均砂粒含量（质量百分比，%），数据源自Hengl等（2017）；
CLY：单元流域平均粘粒含量（质量百分比，%），数据源自Hengl等（2017）；
SLT：单元流域平均粉粒含量（质量百分比，%），数据源自Hengl等（2017）；
Urb：单元流域平均城市用地占比，数据源自Liu等（2018）；
WTD：单元流域平均地下水位埋深（m，地表以下），数据源自Fan等（2013）；
HW：单元流域平均人类用水量（灌溉用水+工业用水+生活用水），数据源自Wada等（2016）；
DOR：河段大坝调控程度，其定义与数据源自Grill等（2019）；
QMEAN：河段年平均径流量（单位：m³/s），多年平均值由Lin等（2019）计算得到；
Q2：河段2年一遇洪水径流量（单位：m³/s），用于计算该字段的35年数据源自Lin等（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满岸河宽，单位：m；
Width_DHG：基于本研究估算的Q2，通过Moody & Troutman（2002）公式推演得到的满岸河宽，单位：m。

4. 参考文献
1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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