Novel Method for Determining Flow Directions in River Channel Networks
The abundance of global, remotely-sensed surface water observations has paved the way toward modeling how water moves across the Earth’s surface. In order to model flows through channel networks, the direction of flow for each link within the network must be known, yet this is difficult to determine from imagery alone. In this work, we propose a rapid, automatic, and objective method to identify flow directions for all links of a channel network using only remotely-sensed imagery and knowledge of the network’s inlet and outlet locations.
Channel networks are important conduits for water, nutrients, sediment, heat, contaminants, and other waterborne constituents. Accurate modeling of the transport of these fluxes requires knowledge of flow directions to be able to route water, solutes, and solids through these complex networks. This method provides a fast, new, and simple means by which to assign flow directions that will greatly advance our ability to model river-ocean connections and changes to the fluxes of waterborne constituents along complicated transport pathways.
The abundance of global, remotely-sensed surface water observations has paved the way toward characterizing and modeling how water moves across the Earth’s surface through complex channel networks. In particular, deltas and braided river channel networks may contain thousands of links that route water, sediment, and nutrients across landscapes. In order to model transport through channel networks, the direction of flow for each link within the network must be known. In this work, we propose a rapid, automatic, and objective method to identify flow directions for all links of a channel network using only remotely-sensed imagery and knowledge of the network’s inlet and outlet locations. We designed a suite of direction-predicting algorithms (DPAs), each of which exploits a particular characteristic of the channel network to provide a prediction of a link’s flow direction. DPAs were chained together to create “recipes”, or algorithms that set all the flow directions of a channel network. Separate recipes were built for deltas and braided rivers and applied to seven delta and two braided river channel networks. This method agreed with expert judgment for 97% of all tested channel links, showing the strength of the algorithms for predicting flow directions for a range of channel network structures.