Aeolian-Fluvial Landforms: the influence of supply and transport limiting factors in modeling river-sourced aeolian dunes
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In sedimentary environments, aeolian processes frequently interact with other geomorphic process (i.e., marine, fluvial, and lacustrine). Recent work emphasizes sediment connectivity among landforms, highlighting the interdependence of sediment transport pathways. For example, in the Colorado River within the Grand Canyon National Park (GRCA) aeolian dunes develop adjacent to river sandbars, with the river and sandbars providing sediment supply for transport to downwind dunes. The closure of Glen Canyon Dam decreased river sediment loads, reduced the large magnitude spring floods, and reduced the extreme low flows during periodic dry seasons and droughts. These changes reduced fluvial sediment availability for aeolian transport along the Colorado River in GRCA. Recent numerical models have made great progress at representing these properties in estimating coastal aeolian sediment transport and dune morphology. Given the many overlaps in relevant processes and types of landforms present in coastal-aeolian and fluvial-aeolian systems, this project aims to adapt and implement existing coastal numerical morphodynamic models (AeoLiS) to quantify behavior across the land-water interface throughout GRCA. The existing framework simulates spatiotemporal variations in bed surface properties and sediment availability and provides more capability to explore supply-limiting scenarios. Our objective is to develop and implement novel model workflows to assess linkages between bar state, river stage, and local vegetation on aeolian sediment transport rates and resultant dune landform evolution. We focus on individual sandbar-fed dune sites, many of which contain culturally significant archaeological material. This work will advance scientific understanding of aeolian-fluvial interactions, advance technical capabilities to quantify non-coastal aeolian transport dynamics, and provide actionable guidance to land managers in these systems. Still in year one of the project, we present preliminary results that highlights the site-specific prediction capabilities.