An improved global river vector dataset based on multi-source river data fusion

High-precision global river datasets are crucial for hydrology and environmental research. Although several global river datasets have been developed and widely adopted, they often suffer from significant deviations from actual river distributions in many areas. In this study, we propose a multi-source vector river data fusion framework to generate a high spatial accuracy global river dataset with topological information, named GSriver. By integrating high-spatial-resolution but topologically incomplete OpenStreetMap (OSM) waterways with HydroRIVERS and supplementing missing segments with the Global River Topology (GRIT) dataset, GSriver preserves complete river topology while significantly enhancing spatial accuracy. Validation against the high-precision NHDPlus dataset of the United States reveals that GSriver improves spatial accuracy over MERIT, GRIT, and HydroRIVERS by 36.3%, 40.7%, and 56.7%, respectively. More than 40% of nodes in GSriver deviate less than 10 meters from NHDPlus. This approach addresses the spatial accuracy limitations of conventional DEM-derived river datasets by leveraging the advantages of crowdsourced data, offering a scalable and cost-effective solution for constructing large-scale river datasets. GSriver is publicly available at https://doi.org/10.6084/m9.figshare.30119851.v3 ¹.