DOI: 10.6084/m9.figshare.30119851.v3
title: GSriver: An improved global river vector dataset based on multi-source river data fusion(HydroRIVERS, OSM, and GRIT)

description: An Improved Global River vector Dataset based on Multi-Source River Data FusionContact: Yesen Liu (liuys@iwhr.com) and Yaohuan Huang (Huang(huangyh@igsnrr.ac.cn) for questionsDownload Instructions:- The data format is a shapefile file, compressed into zip format, and divided by continent (due to the limitation of shapefile files, Russia will be saved separately as a file)- GSriver-AF-shp.zip(911M): Rivers in Africa, including 166335 rivers- GSriver-ASandEU-shp.zip(1.35G): Rivers in the Asia Europe region (excluding Russia), including 209528 rivers- GSriver-NA-shp.zip(921M): Rivers in North America, including 134242 rivers- GSriver-OA-shp.zip(246M): Rivers in Oceania, including 45738 rivers- GSriver-RUS-shp.zip(735M): Rivers in Russia, including 95122 rivers- GSriver-SA-shp.zip(666M): Rivers in South America, including 97664 riversUnderlying sources:- OSM waterways: https://www.openstreetmap.org- HydroRIVERS: https://www.hydrosheds.org/products/hydrorivers- Global River Topology(GRIT): https://zenodo.org/records/11219313/filesCoordinate system-WGS84(World Geodetic System 1984)Spatial and Temporal Coverage:The dataset covers global river networks (divided by continents) and specified the temporal baseline of the input sources (HydroRIVERS v10, OSM Waterways 2022, and GRIT).Reference:- Liu Y., Wang J.H, Liu C.J, etc. Allen (in review): An Improved Global River vector Dataset based on Multi-Source River Data FusionVariables and Units(Name ,Type ,Description):- OBJECT ,integer ,ID Field in MDB Vector Layer Files- shape ,Binary ,Stores geometric information of river lines- HydroID ,LONG ,Upstream HydroRIVERS reach ID(from HYDRO_ID field), serving as the unique identifier for the river- Nextdown ,LONG ,Code of the receiving river(0 for level-1 rivers indicating sea/lake termination or inland rivers)- MAINRIVID ,LONG ,Watershed ID(from MAINRIV_ID field in HydroRIVERS)- Hydrocount ,LONG ,Number of original HydroRIVERS reaches merged into this river- Rclass ,INTEGER ,River classification level- RclassDISP ,INTEGER ,Display classification level(using 11 major rivers as level-1 reference)- Rlenth ,DOUBLE ,River length(km)- Hydrolenth ,DOUBLE ,Original HydroRIVERS reach length before fusion(km)- Rcatch ,DOUBLE ,Drainage area of this river(excluding tributary areas)(km²)- TCatch ,DOUBLE ,Total drainage area of all tributaries(km²)- OSMratio ,DOUBLE ,Percentage of vertices from OSM waterways(2 decimal places)- GRITratio ,DOUBLE ,Percentage of vertices from GRIT(2 decimal places)- Hydroratio ,DOUBLE ,Percentage of vertices from HydroRIVERS(2 decimal places)- Rname ,STRING ,River name(Some names have been modified based on other datasets)- NameList ,STRING ,All associated OSM waterway names- OrignCNT ,STRING ,Country of river originMethods of creation:We devised a multi-resolution vector data fusion framework by integrating high-precision coordinate information into existing river networks to produce SkyRivers. In this study, SkyRivers is generated by fusing HydroRIVERS with OSM waterways, while utilizing GRIT as a supplementary data source in regions where OSM waterways coverage is incomplete. To these three vector datasets, there are two fundamental technical challenges for data fusion:(1) establishing accurate correspondences between HydroRIVERS and their counterparts in OSM waterways or GRIT, and(2) effectively integrating the high-resolution coordinates from OSM waterways or GRIT into the HydroRIVERS river network topology. To overcome these challenges, our approach implemented several key solutions including HydroRIVERS reaches integration, OSM Waterways identification, multi-data fusion, GRIT supplement, and topology repair.Potential applications:- Flood forecasting- Development of Water Information Platform- Water resources mapping- Overlay analysis with high-resolution land use dataUncertainty Estimates:The accuracy of OSM data varies widely depending on contributor activity, which can introduce inconsistencies in sparsely mapped areas. For instance, the United States contains over 6.6 million of the 27 million total OSM river reaches, substantially exceeding other countries’ coverage. In GSriver, approximately 23.6% of river reaches remain unmodified and retain their original lower spatial accuracy. Additionally, while GRIT helps supplement sparsely mapped areas, it still inherits the limitations of DEM-based approaches in flatlands and deltaic systems. Validation also revealed a few residual topological inconsistencies in plain regions such as the Huai River Basin in China. To address these limitations, each river in GSriver includes metadata fields for source and fusion ratio to aid transparency and user interpretation. Users can assess the quality of individual river segments using the OSMratio, GRITpercent, and Hydropercent fields in the attribute table. Rivers with higher OSMratio generally exhibit higher spatial precision, while those with high Hydropercent retain the uncertainty inherent in DEM-derived networks.<br>