Fine Sediment Trapping in the Penobscot River Estuary

Thesis advisor: Gail KinekeThe Penobscot River Estuary is heavily contaminated with mercury; previous studies indicate maximum mercury concentrations of 4.6 ppm within the Frankfort Flats reach. The transport and trapping of this contaminant is linked to the transport and trapping of fine sediment within the estuary. Hydrographic and flow measurements, coupled with a spatial and temporal characterization of the bottom sediments, were performed during and following the freshet in 2010 to determine the mechanisms driving sediment transport and trapping within the estuary. The Penobscot River likely has a turbidity maximum associated with the landward extent of the salinity intrusion that is positioned over the Frankfort Flats reach during average discharge and tidal conditions. This turbidity maximum may be responsible for a patch of fine sediments in the Frankfort Flats reach in an otherwise coarse-grained bed. Additional transport and trapping of fine sediments within this reach is the result of secondary circulation driven by centripetal acceleration around meanders in the channel. Close proximity of meanders at Frankfort Flats, within ~5 km, creates opposite secondary circulation of magnitude ~0.2 m/s during flood and ebb conditions.Thesis (BS) — Boston College, 2011.Submitted to: Boston College. College of Arts and Sciences.Discipline: Geology & Geophysics Honors Program.Discipline: Earth and Environmental Sciences

Impact of SST and surface waves on Hurricane Florence (2018): a coupled modeling investigation

Author Posting. © American Meteorological Society , 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Zambon, J. B., He, R., Warner, J. C., & Hegermiller, C. A. Impact of SST and surface waves on Hurricane Florence (2018): a coupled modeling investigation. Weather and Forecasting, 36(5), (2021): 1713–1734, https://doi.org/10.1175/WAF-D-20-0171.1.Hurricane Florence (2018) devastated the coastal communities of the Carolinas through heavy rainfall that resulted in massive flooding. Florence was characterized by an abrupt reduction in intensity (Saffir–Simpson category 4 to category 1) just prior to landfall and synoptic-scale interactions that stalled the storm over the Carolinas for several days. We conducted a series of numerical modeling experiments in coupled and uncoupled configurations to examine the impact of sea surface temperature (SST) and ocean waves on storm characteristics. In addition to experiments using a fully coupled atmosphere–ocean–wave model, we introduced the capability of the atmospheric model to modulate wind stress and surface fluxes by ocean waves through data from an uncoupled wave model. We examined these experiments by comparing track, intensity, strength, SST, storm structure, wave height, surface roughness, heat fluxes, and precipitation in order to determine the impacts of resolving ocean conditions with varying degrees of coupling. We found differences in the storm’s intensity and strength, with the best correlation coefficient of intensity (r = 0.89) and strength (r = 0.95) coming from the fully coupled simulations. Further analysis into surface roughness parameterizations added to the atmospheric model revealed differences in the spatial distribution and magnitude of the largest roughness lengths. Adding ocean and wave features to the model further modified the fluxes due to more realistic cooling beneath the storm, which in turn modified the precipitation field. Our experiments highlight significant differences in how air–sea processes impact hurricane modeling. The storm characteristics of track, intensity, strength, and precipitation at landfall are crucial to predictability and forecasting of future landfalling hurricanes.This work has been supported by the U.S. Geological Survey Coastal/Marine Hazards and Resources Program, and by Congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act of 2019 (H.R. 2157). The authors also wish to acknowledge research support through NSF Grant OCE-1559178 and NOAA Grant NA16NOS0120028. We also wish to thank Chris Sherwood from the U.S. Geological Survey for his help in deriving wave length from WAVEWATCH III data

Modeling the morphodynamics of coastal responses to extreme events: what shape are we in?

This paper is not subject to U.S. copyright. The definitive version was published in Sherwood, C. R., van Dongeren, A., Doyle, J., Hegermiller, C. A., Hsu, T.-J., Kalra, T. S., Olabarrieta, M., Penko, A. M., Rafati, Y., Roelvink, D., van der Lugt, M., Veeramony, J., & Warner, J. C. Modeling the morphodynamics of coastal responses to extreme events: what shape are we in? Annual Review of Marine Science, 14, (2022): 457–492, https://doi.org/10.1146/annurev-marine-032221-090215.This review focuses on recent advances in process-based numerical models of the impact of extreme storms on sandy coasts. Driven by larger-scale models of meteorology and hydrodynamics, these models simulate morphodynamics across the Sallenger storm-impact scale, including swash,collision, overwash, and inundation. Models are becoming both wider (as more processes are added) and deeper (as detailed physics replaces earlier parameterizations). Algorithms for wave-induced flows and sediment transport under shoaling waves are among the recent developments. Community and open-source models have become the norm. Observations of initial conditions (topography, land cover, and sediment characteristics) have become more detailed, and improvements in tropical cyclone and wave models provide forcing (winds, waves, surge, and upland flow) that is better resolved and more accurate, yielding commensurate improvements in model skill. We foresee that future storm-impact models will increasingly resolve individual waves, apply data assimilation, and be used in ensemble modeling modes to predict uncertainties.All authors except D.R. were partially supported by the IFMSIP project, funded by US Office of Naval Research grant PE 0601153N under contracts N00014-17-1-2459 (Deltares), N00014-18-1-2785 (University of Delaware), N0001419WX00733 (US Naval Research Laboratory, Monterey), N0001418WX01447 (US Naval Research Laboratory, Stennis Space Center), and N0001418IP00016 (US Geological Survey). C.R.S., C.A.H., T.S.K., and J.C.W. were supported by the US Geological Survey Coastal/Marine Hazards and Resources Program. A.v.D. and M.v.d.L. were supported by the Deltares Strategic Research project Quantifying Flood Hazards and Impacts. M.O. acknowledges support from National Science Foundation project OCE-1554892

Ocean Surface Gravity Wave Evolution during Three Along-Shelf Propagating Tropical Cyclones: Model’s Performance of Wind-Sea and Swell

Despite recent advancements in ocean–wave observations, how a tropical cyclone’s (TC’s) track, intensity, and translation speed affect the directional wave spectra evolution is poorly understood. Given the scarcity of available wave spectral observations during TCs, there are few studies about the performance of spectral wave models, such as Simulating Waves Nearshore (SWAN), under various TC scenarios. We combined the National Data Buoy Center observations and numerical model hindcasts to determine the linkages between wave spectrum evolution and TC characteristics during hurricanes Matthew 2016, Dorian 2019, and Isaias 2020. Five phases were identified in the wave spectrogram based on the normalized distance to the TC, the sea–swell separation frequency, and the peak wave frequency, indicating how the wave evolution relates to TC characteristics. The wave spectral structure and SWAN model’s performance for wave energy distribution within different phases were identified. The TC intensity and its normalized distance to a buoy were the dominant factors in the energy levels and peak wave frequencies. The TC heading direction and translation speed were more likely to impact the durations of the phases. TC translation speeds also influenced the model’s performance on swell energy. The knowledge gained in this work paves the way for improving model’s performance during severe weather events

Wave-current interaction between Hurricane Matthew wave fields and the Gulf Stream

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 49(11), (2019): 2883-2900, doi: 10.1175/JPO-D-19-0124.1.Hurricanes interact with the Gulf Stream in the South Atlantic Bight (SAB) through a wide variety of processes, which are crucial to understand for prediction of open-ocean and coastal hazards during storms. However, it remains unclear how waves are modified by large-scale ocean currents under storm conditions, when waves are aligned with the storm-driven circulation and tightly coupled to the overlying wind field. Hurricane Matthew (2016) impacted the U.S. Southeast coast, causing extensive coastal change due to large waves and elevated water levels. The hurricane traveled on the continental shelf parallel to the SAB coastline, with the right side of the hurricane directly over the Gulf Stream. Using the Coupled Ocean–Atmosphere–Wave–Sediment Transport modeling system, we investigate wave–current interaction between Hurricane Matthew and the Gulf Stream. The model simulates ocean currents and waves over a grid encompassing the U.S. East Coast, with varied coupling of the hydrodynamic and wave components to isolate the effect of the currents on the waves, and the effect of the Gulf Stream relative to storm-driven circulation. The Gulf Stream modifies the direction of the storm-driven currents beneath the right side of the hurricane. Waves transitioned from following currents that result in wave lengthening, through negative current gradients that result in wave steepening and dissipation. Wave–current interaction over the Gulf Stream modified maximum coastal total water levels and changed incident wave directions at the coast by up to 20°, with strong implications for the morphodynamic response and stability of the coast to the hurricane.C.A. Hegermiller is grateful to the Woods Hole Oceanographic Institution (WHOI) Postdoctoral Scholarship program and the WHOI-U.S. Geological Survey (USGS) cooperative agreement for support. This project was supported by the USGS Coastal and Marine Hazards and Resources Program and by the Office of Naval Research, Increasing the Fidelity of Morphological Storm Impact Predictions Project. Thank you to the internal and external reviewers for improving the quality of this work, and to conversations within the Woods Hole community during the development of the experiment and analysis of the results. Model data can be found at http://geoport.whoi.edu/thredds/catalog/sand/usgs/users/chegermiller/projects/WCI_JPO_2019/catalog.html. Figure color maps are from Thyng et al. (2016).2020-05-0

Modeling of barrier breaching during hurricanes Sandy and Matthew

This paper is not subject to U.S. copyright. The definitive version was published in Hegermiller, C. A., Warner, J. C., Olabarrieta, M., Sherwood, C. R., & Kalra, T. S. Modeling of barrier breaching during hurricanes Sandy and Matthew. Journal of Geophysical Research: Earth Surface, 127(3), (2022): e2021JF006307, https://doi.org/10.1029/2021JF006307.Physical processes driving barrier island change during storms are important to understand to mitigate coastal hazards and to evaluate conceptual models for barrier evolution. Spatial variations in barrier island topography, landcover characteristics, and nearshore and back-barrier hydrodynamics can yield complex morphological change that requires models of increasing resolution and physical complexity to predict. Using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system, we investigated two barrier island breaches that occurred on Fire Island, NY during Hurricane Sandy (2012) and at Matanzas, FL during Hurricane Matthew (2016). The model employed a recently implemented infragravity (IG) wave driver to represent the important effects of IG waves on nearshore water levels and sediment transport. The model simulated breaching and other changes with good skill at both locations, resolving differences in the processes and evolution. The breach simulated at Fire Island was 250 m west of the observed breach, whereas the breach simulated at Matanzas was within 100 m of the observed breach. Implementation of the vegetation module of COAWST to allow three-dimensional drag over dune vegetation at Fire Island improved model skill by decreasing flows across the back-barrier, as opposed to varying bottom roughness that did not positively alter model response. Analysis of breach processes at Matanzas indicated that both far-field and local hydrodynamics influenced breach creation and evolution, including remotely generated waves and surge, but also surge propagation through back-barrier waterways. This work underscores the importance of resolving the complexity of nearshore and back-barrier systems when predicting barrier island change during extreme events.C. A. Hegermiller is grateful to the U.S. Geological Survey (USGS) Mendenhall Research Fellowship Program for support. This project was supported by the USGS Coastal and Marine Geology Program and the Office of Naval Research, Increasing the Fidelity of Morphological Storm Impact Predictions Project. M. Olabarrieta acknowledges support from the NSF project OCE-1554892.2022-07-2

Southern California deep-water to nearshore waves lookup tables

The lookup tables relate deep-water significant wave height, peak wave period, and peak wave direction with nearshore significant wave height, peak wave period, mean wave period, peak wave direction, and directional spreading at 4,802 stations spaced ~100 m apart along the 10 m bathymetric contour and 20 intermediate-water stations coincident with CDIP buoys within the Southern California Bight from Point Conception to the Mexican border. Deep-water significant wave height bin size = 0.25 m. Deep-water peak wave period bin size = 3 s. Deep-water peak wave direction bin size = 8 deg. Station file = socal.loc is a text file with 4822 lat,lon coordinates for stations starting from 1 to 4822. Lookup table files = LUT_STN*toSTN*.mat Each lookup table file is a mat file containing the lookup table for 600 stations, as noted in the filename. Structures of input/deep-water significant wave height, peak wave period, and peak wave direction and output/nearshore significant wave height, peak wave period, mean wave period, peak wave direction, and directional spreading allow for easy indexing of deep-water wave conditions to find nearshore conditions at a station of interest. These lookup tables were generated with Simulating WAves Nearshore model. Detailed modeling information can be found in: Hegermiller et al., submitted. Controls of multimodal wave conditions in a complex coastal setting. Please email [email protected] for more information