Abstract
Channel sinuosity is ubiquitous along river networks, producing complex patterns that encapsulate and influence morphodynamic processes and ecosystem services. Accurately characterizing these patterns is challenging with traditional curvature-based algorithms. Here, we present WigglyRivers, a Python package that builds on existing wavelet-based methods to create an unsupervised meander identification and characterization tool. The package uses planimetric information the user provides or from the USGS's High-Resolution National Hydrography Dataset to characterize individual reaches or entire river networks. WigglyRivers also includes a supervised river identification tool for manually selecting individual meandering features. Here, we provide examples of idealized river transects and show the capabilities of WigglyRivers. We also use the supervised identification tool to validate the unsupervised identification on river transects across the continental US. WigglyRivers is a tool to understand better the multiscale characteristics of river networks and the link between river geomorphology and river corridor connectivity.
| Original language | English |
|---|---|
| Article number | 106423 |
| Journal | Environmental Modelling and Software |
| Volume | 188 |
| DOIs | |
| State | Published - Apr 2025 |
Funding
This research was funded by the U.S. Department of Energy as part of the Watershed Dynamics and Evolution (WaDE) Science Focus Area at Oak Ridge National Laboratory, the River Corridor Scientific Focus Area project at Pacific Northwest National Laboratory, the IDEAS watersheds project, and by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy.
Keywords
- Continuous wavelet transform
- Meanders
- Python
- River networks
- Sinuosity
- WigglyRivers