arXiv subjects

Cary D. Troy

Publications and source records attributed to Cary D. Troy.

2 recordsLinked to original sources

arXiv2025

Shoreline Responses to Rapid Water Level Increases in Lake Michigan

High-resolution multispectral satellite imagery was utilized to quantify shoreline recession at eleven beaches around Lake Michigan during a record-setting water level increase between 2013 and 2020. Shoreline changes during this period ranged from 20 m to 62 m, corresponding to 52-95% of the initial beach widths. Average estimated shoreline erosion across all beaches varied from 1% to 75% of the observed changes, with the remainder attributed to inundation. Significant correlations were found between shoreline erosion and wave-related factors, including offshore wave power, offshore bathymetric slope, storm energy, and potential alongshore sediment transport divergence. In contrast, parameters related to cross-shore transport, such as dimensionless fall velocity, exhibited weak correlations. Additionally, the results underscore the importance of distinguishing between immediately reversible changes (inundation) and morphological changes that could be reversible over longer timescales, when assessing the impact of rising water levels., The findings also suggest that in addition to waves playing a key role in regulating shoreline changes, alongshore sediment transport processes may play a more crucial role in beach erosion during significant water level increases than cross-shore processes, challenging traditional models of beach adjustment to rising waters.

physics.ao-ph↗

arXiv2023

A Machine Learning Framework for Extending Wave Height Time Series Using Historical Wind Records

This study presents a novel machine learning-based (ML) framework that utilizes the ConvLSTM-1D model to hindcast or forecast wave heights at coastal locations using a nonuniform array of wind observations. This approach was applied to Lake Michigan to perform a 70-year ice-free hindcast of waves near Chicago, IL (USA). The Wave Information System model (WIS) served as the training, validation, and testing dataset for the ML model. Ensemble learning-optimized ML models forced by different numbers of observation stations were tested, showing that a single wind station alone as an input feature produced a reasonably accurate wave height model. However, the wave height model accuracy increased as more wind input data was included from around the lake, largely plateauing beyond the inclusion of four stations that spanned Lake Michigan's southern basin. The optimized model lookback period was found to be 10 hours for all models, suggestive of a fetch-limited temporal coupling between the wind observations and nearshore waves. The ML framework offers a promising avenue for utilizing historical wind records worldwide to extend wave height time series for nearshore locations, particularly in enclosed and semi-enclosed basins where waves are strongly linked to local winds.

physics.ao-ph↗