USGS Science⌕ Search

Geology topics

Christopher J. Amante

Publications and source records attributed to Christopher J. Amante.

2 recordsLinked to original sources

Inundation processes, barrier island breaching, and structure impacts during Hurricane Michael (2018)

We demonstrate the increased ability to forecast hurricane impacts with a coupled numerical modeling system by simulating ocean waves, water levels, currents, sediment transport, and structural damage to predict inundation, coastal morphological change, and residential building impacts. The Coupled-Ocean-Atmosphere-Waves-Sediment-Transport (COAWST) modeling system is applied to simulate Hurricane Michael (category 5, 2018) that made landfall near Tyndall Air Force Base, FL, in the northern Gulf of America, causing severe devastation and flooding. Atmospheric forcings from the Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) are used to drive the ocean and wave models on a series of nested grids. Results identify that coastal inundation at Mexico Beach is due to surge from winds and waves, supplemented by pulses of infragravity wave motions that propagate landward into the inundation region. Seed lines observed on interior building walls also demonstrate variable changes in water level. In addition, a machine learning model was applied to hindcast structure damages, caused mostly by waves and winds, with a 72% accuracy estimate of substantial damage in proximity of landfall. The storm also created a breach across Cape San Blas, the adjacent barrier spit, due to large surge and low dune elevations. Dune locations with vegetated land cover are shown to reduce wave-energy dissipation and reduce erosion, whereas locations without land cover had increased breaching potential. The breach occurred during the maximum ocean-side water level, and the delayed high water on the bay side allowed a pressure gradient to drive flow seaward and promote breach development.

Florida↗

Assessing the vertical accuracy of digital elevation models by quality level and land cover

The vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area’s exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of lidar-derived digital elevation models (DEMs) on a per-project basis to gauge whether the datasets meet quality and accuracy standards. Here, we collated over 5200 contractor elevation checkpoints along the Atlantic and Gulf of Mexico coasts of the United States that were collected for project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey’s Three-Dimensional Elevation Program. We used land cover data to quantify non-vegetated vertical accuracy and vegetated vertical accuracy statistics (overall and by point spacing bins) and assessed elevation error by land cover class. We found the non-vegetated vertical accuracy had an overall root mean square error of 6.9 cm and vegetated areas had a 95th percentile vertical error of 22.3 cm. Point spacing was generally positively correlated to elevation accuracy, but sample size limited the ability to interpret results from accuracy by land cover, particularly in wetlands. Based on the specific questions a researcher may be asking, use of literature or fieldwork could assist with enhancing error statistics in underrepresented classes.

Remote Sensing Letters↗