Archetypal River Corridor Terrain Models for Various Channel Types
Abstract
A channel type refers to a classification of river channel morphologies with common landform properties such as confinement, channel dimensions, slope, and substrate characteristics. As progress on spatially-explicit channel type prediction is made based on unprecedented availability of topographic surveys and aerial image data, there is an increasing demand for the development of channel archetypes, defined as idealized 3D site representations of the geo-hydro-eco setting, including detailed information of expected site attributes. Archetypes of distinct channel types present in any area of interest enable desktop-based, accurate prediction of hydraulic and ecosystem responses to climate, water management, or river engineering scenarios. Our study focuses on developing archetypal river corridor terrain models for 5 identified flashy-ephemeral channel types in the South Coast region of California: (1) Unconfined, uniform, sand-gravel, (2) Partly-confined, gravel-cobble, braided, (3) Confined, coble-boulder, cascade/step-pool, (4) Confined, gravel-cobble, uniform, and (5) Partly-confined, gravel-cobble, riffle-pool. We developed 1-m river corridor topography from dry-period LiDAR for 8-9 reaches per channel type and extracted relevant geomorphic variability functions from the sampled topographies. For channel variability functions such as longitudinal width and thalweg elevation undulations, each series was decomposed into multiple harmonic waves having different frequencies (Fourier transform), and their amplitudes were averaged by channel type. The averaged amplitudes were used to reconstruct a new series using Inverse Fourier transform or Fourier series representation. Geometric parameters such as slope, bankfull dimensions and sinuosity amplitude/phase were averaged by channel type. The result was a set of archetypal river models that can be used in lieu of surveyed terrains to quantify hydraulic and ecological responses which would significantly reduce surveying efforts.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H22Q1058L