Abstract
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.
Highlights
River channel networks (CNs) sustain communities and ecosystems across the globe by delivering and distributing fluxes of water, sediment, and nutrients
We found 97.0 % and 98.2 % agreement between expert judgement and links set according to the delta recipe (DR) and braided rivers (BR), respectively
This work presents a framework for building algorithmic recipes to automatically and objectively set the steady-state flow directions in all links of a channel network (CN) graph using only a binary mask of the channel network
Summary
River channel networks (CNs) sustain communities and ecosystems across the globe by delivering and distributing fluxes of water, sediment, and nutrients. The flow direction of river discharge may not be steady through time or may be in multiple directions simultaneously Such bidirectional flows may result from large, irreversible perturbations to the channel network (e.g., Shugar et al, 2017), fluid density differences within the channel (e.g., Garcia et al, 2006), or most commonly tidal influence (Fagherazzi et al, 2004). Even when a high-resolution DEM is available, the presence of shoals and bifurcations in multi-threaded CNs can result in flows that travel upslope, requiring sophisticated techniques to resolve flow directions (van Dijk et al, 2019; Kleinhans et al, 2017) These challenges render popular DEM-based hydrologic processing algorithms (Schwanghart and Scherler, 2014; Tarboton, 1997) and related products (Lehner et al, 2008; Yamazaki et al, 2019) ineffective. Improvements to reduce errors in setting link directionalities are discussed
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