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

Area of Science:

• Hydrology
• Geospatial Science
• Environmental Science

Background:

• High-precision global river datasets are essential for hydrological and environmental research.
• Existing datasets often exhibit significant spatial inaccuracies, limiting their utility.
• Limitations in current datasets necessitate improved methods for global river mapping.

Purpose of the Study:

• To develop a novel multi-source vector river data fusion framework for generating a high spatial accuracy global river dataset with topological information.
• To address the spatial accuracy limitations of existing global river datasets.
• To create a publicly available, scalable, and cost-effective global river dataset.

Main Methods:

• Integration of OpenStreetMap (OSM) waterways (high-resolution, incomplete topology) with HydroRIVERS.
• Supplementation of missing river segments using the Global River Topology (GRIT) dataset.
• Fusion framework designed to preserve complete river topology while enhancing spatial accuracy.

Main Results:

• The resulting dataset, GSriver, demonstrates significantly improved spatial accuracy compared to MERIT, GRIT, and HydroRIVERS.
• GSriver shows accuracy improvements of 36.3% over MERIT, 40.7% over GRIT, and 56.7% over HydroRIVERS.
• Over 40% of GSriver nodes exhibit deviations of less than 10 meters from the high-precision NHDPlus dataset.

Conclusions:

• The proposed fusion framework effectively leverages crowdsourced data (OSM) to overcome the spatial accuracy limitations of traditional DEM-derived river datasets.
• GSriver offers a scalable and cost-effective solution for constructing large-scale, high-accuracy river datasets.
• The publicly available GSriver dataset provides a valuable resource for advancing hydrological and environmental research.