Award Abstract # 2052443
Collaborative Research: Morphodynamic simulations of coastal storms and overwash to characterize back-barrier lake stratigraphies

NSF Org: EAR
Division Of Earth Sciences
Recipient: NORTHEASTERN UNIVERSITY
Initial Amendment Date: April 27, 2021
Latest Amendment Date: April 27, 2021
Award Number: 2052443
Award Instrument: Standard Grant
Program Manager: Justin Lawrence
jlawrenc@nsf.gov
 (703)292-2425
EAR
 Division Of Earth Sciences
GEO
 Directorate for Geosciences
Start Date: May 1, 2021
End Date: October 31, 2025 (Estimated)
Total Intended Award Amount: $353,666.00
Total Awarded Amount to Date: $353,666.00
Funds Obligated to Date: FY 2021 = $353,666.00
History of Investigator:
  • Samuel Munoz (Principal Investigator)
    s.munoz@northeastern.edu
  • Qin Chen (Co-Principal Investigator)
Recipient Sponsored Research Office: Northeastern University
360 HUNTINGTON AVE
BOSTON
MA  US  02115-5005
(617)373-5600
Sponsor Congressional District: 07
Primary Place of Performance: Northeastern University
360 Huntington Ave
Boston
MA  US  02115-5005
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): HLTMVS2JZBS6
Parent UEI:
NSF Program(s): Geomorphology & Land-use Dynam
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 745800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.050

ABSTRACT

Hurricanes constitute major hazards to coastal communities of the eastern United States, generating strong winds and waves, coastal flooding, and erosion. These storms often induce overwash, a process where water and sediments flow over coastal barriers, erode the beach, and deposit sediments atop and behind the barrier. Geoscientists often use deposits of overwash in coastal ponds to infer when hurricanes occurred in the past, prior to written and satellite records of hurricanes. These sedimentary records of past hurricane activity are useful for constraining the probability of a hurricane making landfall on a stretch of coastline, or for understanding changes in hurricane frequency in response to past changes in climate, and are commonly integrated into hazard assessments. However, these sedimentary records are limited to reconstructing the frequency of past storms, while inferences into the magnitude or track of these events remain preliminary. In this project, the investigators will harness recent advances in computer simulations of coastal sediment transport to develop a new approach for reconstructing storm strength and track from overwash deposits.

This project will integrate field-based observations of overwash from sediment cores with state-of-the-art simulations of observed and simulated hurricanes to: (i) evaluate the influences of storm properties on overwash deposition in back-barrier settings, (ii) diagnose the sensitivity of depositional patterns to geomorphic change, and (iii) relate storm strength and track to the properties of overwash deposits. This work will deliver quantitative reconstructions of hurricane magnitude for southern New England ? a heavily-populated region that is vulnerable to tropical cyclones ? extending the historical record of hurricane activity in this region back centuries to provide insight into strengths and tracks of prehistoric hurricanes that made landfall in this region. The investigators anticipate that the integrative and scalable approach developed here will be applicable to other coastlines in the United States and abroad that are vulnerable to tropical and extratropical cyclones, and will facilitate the critical challenge of predicting and mitigating against coastal erosion and flooding.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Wang, Nan and Chen, Qin and Zhu, Ling "Data-driven modeling of Bay-Ocean wave spectra at bridge-tunnel crossing of Chesapeake Bay, USA" Applied Ocean Research , v.135 , 2023 https://doi.org/10.1016/j.apor.2023.103537 Citation Details
Salatin, Reza and Chen, Qin and Raubenheimer, Britt and Elgar, Steve and Gorrell, Levi and Li, Xin "A new framework for quantifying alongshore variability of swash motion using fully convolutional networks" Coastal Engineering , v.192 , 2024 https://doi.org/10.1016/j.coastaleng.2024.104542 Citation Details

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