2024 CSDMS meeting-095

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Strange Knickpoints: A Case Study in Using Landscape Evolution Models to Understand Site-Specific Landscape Complexities

Christopher Sheehan, University of Dayton Dayton Ohio, United States. csheehan1@udayton.edu

Magdelyn Stewart, University of Dayton Dayton Ohio, United States. stewartm20@udayton.edu

The modern Ohio River network is a Rubrik’s Cube for anyone interested in dynamic river reorganization. Throughout the Quaternary, the cyclic growth of North American ice sheets forced the Ohio drainage network to oscillate between a north-flowing (towards the Gulf of St. Laurence or Hudson Bay) and west / south-flowing (towards the Gulf of Mexico, i.e., the modern river) configuration. These cycles produced a network of overprinted paleo valleys that reflect multiple episodes of river reorganization (the so-called “Teays” paleo river network). The overprinted nature of these valleys makes it very difficult to assess the timing of specific stream capture events. In order to unravel this complex history of river reorganization, geomorphologists can begin by constraining the timing of individual stream capture events that do not overprint older episodes of drainage reversal.

One such event is likely present in Hocking Hills State Park in central Ohio, known for its hundreds of 30-50 m-tall waterfalls. These knickpoints were likely created when the upper reaches of the Salt Creek watershed were blocked by one of the ice sheets, forming a glacial lake that spilled over a drainage divide and rerouted the channel network from a west-flowing to a south-flowing configuration. The stream capture event would have also produced a local base level drop that created the knickpoints. This hypothesis implies that the knickpoints were all created at the same time; if true, we can constrain the timing of the capture event using catchment averaged erosion rates and knickpoint celerity models.

However, the hypothesis also implies that the waterfalls should be located at the same approximate χ value. This is not the case; rather, there is prominent, N-S trend in χ values. Without an explanation for this trend, any age constraints on the capture timing will be suspect.

We used Landlab-based landscape evolution models (LEMs) to explore several possible explanations for the trend in χ values. We found that following a single capture event, the trend can be explained by the specific combination of (a) the pre-capture channel topology; (b) the precise capture location; and (c) the spatial extent of different rock layers. We believe that this in an “Occam’s razer” scenario, because it allows the χ trend to be explained by a single, stream capture forcing. However, without the insights provided by our LEMs, we would have considered multiple forcings or stream capture events to be more likely. These simulations are a novel and interesting case study in how LEMs can be applied to understand unique complexities of specific field sites and also have important implications for using knickpoint celerity models to assess landscape evolution.