Controls on Fluvial Bar Preservation in Braided Rivers
Abstract
In alluvial rivers, feedbacks between water and sediment transport control the scale, style, and pace of bedform and bar migration. Discrete fingerprints of this feedback can be preserved in ancient channel fills, providing a lens through which we can reconstruct the formative flow conditions of ancient river systems.
In meandering rivers, numerical modelling and detailed stratigraphic mapping techniques have been used to advance reconstructions of ancient channel morphodynamics and hydrodynamics from preserved bar deposits. Observations of braided channel belts from satellite imagery show channel thread movements (e.g. confluence splitting, translation and widening) that influence the morphology, kinematics, and preservation of braid bar deposits. These dynamics complicate our ability to directly apply similar theoretical and deterministic reconstructive techniques to ancient, braided channel fills, as their influence on bar preservation is less well-constrained. Here, we use a numerical model to simulate the evolution of a braided river under constant water- and sediment-supply conditions and investigate controls on bar preservation. Using the geometry and architecture of model deposits, we explore the fingerprint of channel morphodynamics on braided channel fills with an emphasis on observations that can be replicated in ancient outcrops. Our results demonstrate that 1) deposits of a single braided river exhibit a wide range of preservation, geometry, and architecture; 2) channel-thread lateral migration and channel-belt widening facilitate bar preservation; and 3) overall preservation of channel bar deposits may be slightly higher in multi-thread rivers than in deposit of comparably sized meandering rivers. This work establishes a statistical baseline for bar preservation in braided fluvial systems, providing important context for understanding how to reconstruct the processes reflected in ancient braided river deposits and which field measurements are most useful for interpreting signals of past landscape change.- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMEP52B0770A