Abstract
We present Orinoco, an open-source Python toolkit that applies the fast-marching method to derive a river delta channel network from a water mask and ocean delineation. We are able to estimate flow direction, along-channel distance, channel width, and network-related metrics for deltaic analyses including the steady-state fluxes. To demonstrate the capabilities of the toolkit, we apply our software to the Wax Lake and Atchafalaya River Deltas using water masks derived from Open Street Map (OSM) and Google Maps. We validate our width estimates using the Global River Width from Landsat (GRWL) database over the Mackenzie Delta as well as in situ width measurements from the National Water Information System (NWIS) in the southeastern United States. We also compare the stream flow direction estimates using products from RivGraph, a related Python package with similar functionality. With the exciting opportunities afforded with forthcoming surface water and topography (SWOT) data, we envision Orinoco as a tool to support the characterization of the complex structure of river deltas worldwide and to make such analyses easily accessible within a Python remote sensing workflow. To support that end, all the data, analyses, and figures in this paper can be found within Jupyter notebooks at Orinoco’s GitHub repository.
Highlights
Deltas are complex coastal landforms that constitute a vital link between the terrestrial and marine environment [1]
We introduced Orinoco, an open-source Python library for extracting a deltaic channel network from a water mask and ocean delineation using the fast-marching method
We demonstrated the application of our software to large areas including an entire Global River Width from Landsat (GRWL) tile (Section 4) as well as a high resolution water mask over the Wax Lake and Atchafalaya using Google Map tiles
Summary
Deltas are complex coastal landforms that constitute a vital link between the terrestrial and marine environment [1]. An open source tool to facilitate the computation of along-channel distance between sparse measurements is required to estimate water surface slope within complex and often narrow deltaic channels, which may be left out of a SWOT water mask. We introduce a Python toolkit Orinoco that extracts a channel network [35] as a graph determined by the channel geometry to support the analysis and processing of SWOT data over the world’s deltas. We extract this network by efficiently leveraging the fast-marching method to segment the delta’s channels. While the channel networks obtained by Orinoco and RivGraph are similar, the methodologies for doing so are very different: where we use the fast-marching method and a channel segmentation, RivGraph uses the skeletonization procedure from [45]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
