Abstract. The abundance of global, remotely sensed surface water observations has accelerated efforts 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 flows through channel networks and characterize network structure, 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 morphologic 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. Across all nine channel networks, the recipe-predicted flow
directions agreed with expert judgement for 97 % of all tested links, and
most disagreements were attributed to unusual channel network topologies
that can easily be accounted for by pre-seeding critical links with known
flow directions. Our results highlight the (non)universality of
process–form relationships across deltas and braided rivers.
Geometry and Topology of Estuary and Braided River Channel Networks Automatically Extracted From Topographic Data
