04 Apr 2024
 | 04 Apr 2024
Status: this preprint is open for discussion.

Examination of Analytical Shear Stress Predictions for Coastal Dune Evolution

Orie Cecil, Nicholas Cohn, Matthew Farthing, Sourav Dutta, and Andrew Trautz

Abstract. Existing process-based models for simulating coastal foredune evolution largely use the same analytical approach for estimating wind induced surface shear stress distributions over spatially variable topography. Originally developed for smooth, low-sloping hills, these analytical models face significant limitations when the topography of interest exhibits large height-to-length ratios and/or steep, localized features. In this work, we utilize computational fluid dynamics (CFD) to examine the error trends of a commonly used analytical shear stress model for a series of idealized two-dimensional dune profiles. It is observed that the prediction error of the analytical model increases as compared to the CFD simulations for increasing height-to-length ratio and localized slope values. Furthermore, we explore two data-driven methodologies for generating alternative shear stress prediction models, namely, symbolic regression and linear, projection-based, non-intrusive reduced order modeling. These alternative modeling strategies demonstrate reduced overall error, but still suffer in their generalizability to broader sets of dune profiles outside of the training data. Finally, the impact of these improvements to aeolian sediment transport fluxes is examined to demonstrate that even modest improvements to the shear stress prediction can have significant impacts to dune evolution simulations over engineering-relevant timescales.

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Orie Cecil, Nicholas Cohn, Matthew Farthing, Sourav Dutta, and Andrew Trautz

Status: open (extended)

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  • RC1: 'Comment on egusphere-2024-855', Anonymous Referee #1, 09 May 2024 reply
Orie Cecil, Nicholas Cohn, Matthew Farthing, Sourav Dutta, and Andrew Trautz
Orie Cecil, Nicholas Cohn, Matthew Farthing, Sourav Dutta, and Andrew Trautz


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Short summary
Using computational fluid dynamics, we analyze the error trends of an analytical shear stress distribution model used to drive aeolian transport for coastal dunes which are an important line of defense against storm related flooding hazards. We find that compared to numerical simulations, the analytical model results in a net overprediction of the landward migration rate. Additionally, two data-driven approaches are proposed for reducing the error while maintaining computational efficiency.