River Network Routing and Discharge Partitioning on a Multichannel River Network

This is the authors' abstract. We don't add key points for this paper.

The advent of global river network data products and river routing models (RRMs) has resulted in substantial improvements to our understanding of the quantity and movement of water in Earth's rivers. However, the majority of vector‐based global river networks are derived from digital elevation models (DEMs) that do not allow representation of river network complexities such as bifurcations and divergences. Resulting discharge estimates may therefore not accurately reflect the movement of water. Satellite remote sensing has enabled the development of river networks that represent increased river complexities—albeit with limited topological accuracy—a necessary ingredient for river routing. Here, we describe an algorithm that generates globally consistent topology for a river network with optically‐derived centerlines—producing new and updated attributes—and adapt an existing vector‐based RRM to handle the added complexity in connectivity (i.e., divergences) via discharge partitioning. We apply the adapted RRM to runoff from an ensemble of land surface models for two case study Pfafstetter level‐2 basins (the Amazon and Mackenzie Rivers) to estimate 3‐hourly discharge between 2015 and 2024. We show that the adapted RRM is able to partition discharge at divergences, which results in a mean increase of 0.75 in Kling‐Gupta Efficiency and 0.31 in correlation coefficient at 6 gauges located on a divergence when compared to routing on a traditional DEM‐derived river network (i.e., contains no divergences). By comparing the resulting hydrographs from the optically‐ and DEM‐derived river networks, we find that flow waves travel faster through portions of river networks where divergences are accurately represented.