Geometry and Topology of Estuary and Braided River Channel Networks Automatically Extracted From Topographic Data

Automatic extraction of channel networks from topography in systems with multiple interconnected channels, like braided rivers and estuaries, remains a major challenge in hydrology and geomorphology. Representing channelized systems as networks provides a mathematical framework for analyzing transport and geomorphology. In this paper, we introduce a mathematically rigorous methodology and software for extracting channel network topology and geometry from digital elevation models (DEMs) and analyze such channel networks in estuaries and braided rivers. Channels are represented as network links, while channel confluences and bifurcations are represented as network nodes. We analyze and compare DEMs from the field and those generated by numerical modeling. We use a metric called the volume parameter that characterizes the volume of deposited material separating channels to quantify the volume of reworkable sediment deposited between links, which is a measure for the spatial scale associated with each network link. Scale asymmetry is observed in most links downstream of bifurcations, indicating geometric asymmetry and bifurcation stability. The length of links relative to system size scales with volume parameter value to the power of 0.24–0.35, while the number of links decreases and does not exhibit power law behavior. Link depth distributions indicate that the estuaries studied tend to organize around a deep main channel that exists at the largest scale while braided rivers have channel depths that are more evenly distributed across scales. The methods and results presented establish a benchmark for quantifying the topology and geometry of multichannel networks from DEMs with a new automatic extraction tool. Plain Language Summary Channels are features of the Earth's surface that carry water and other material across the continents toward the coasts. We have long recognized that knowing the shapes, sizes, and connections of channels in rivers, estuaries, and deltas is vital for understanding and predicting future change. However, automatically identifying channel networks from surface elevation is challenging because channels display a wide range of different shapes, sizes, and patterns, including shallow and deep areas, and often have many intersections with other channels. We have developed a method for identifying channel networks from elevation surveys. We first find the “lowest path” in a channel network, meaning the channel that is at generally lower e