Development of the Coastal Storm Modeling System (CoSMoS) for Predicting the Impact of Storms on High-energy, Active-margin Coasts

Patrick L. Barnard, Maarten van Ormondt, Li H. Erikson, Jodi Eshleman, Cheryl Hapke, Peter Ruggiero, Peter N. Adams, Amy C. Foxgrover

Research output: Contribution to journalArticlepeer-review

Abstract

The Coastal Storm Modeling System (CoSMoS) applies a predominantly deterministic framework to make detailed predictions (meter scale) of storm-induced coastal flooding, erosion, and cliff failures over large geographic scales (100s of kilometers). CoSMoS was developed for hindcast studies, operational applications (i.e., nowcasts and multiday forecasts), and future climate scenarios (i.e., sea-level rise + storms) to provide emergency responders and coastal planners with critical storm hazards information that may be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. The prototype system, developed for the California coast, uses the global WAVEWATCH III wave model, the TOPEX/Poseidon satellite altimetry-based global tide model, and atmospheric-forcing data from either the US National Weather Service (operational mode) or Global Climate Models (future climate mode), to determine regional wave and water-level boundary conditions. These physical processes are dynamically downscaled using a series of nested Delft3D-WAVE (SWAN) and Delft3D-FLOW (FLOW) models and linked at the coast to tightly spaced XBeach (eXtreme Beach) cross-shore profile models and a Bayesian probabilistic cliff failure model. Hindcast testing demonstrates that, despite uncertainties in preexisting beach morphology over the ~500 km alongshore extent of the pilot study area, CoSMoS effectively identifies discrete sections of the coast (100s of meters) that are vulnerable to coastal hazards under a range of current and future oceanographic forcing conditions, and is therefore an effective tool for operational and future climate scenario planning.

Original languageAmerican English
JournalNatural Hazards
Volume74
DOIs
StatePublished - Jan 1 2014
Externally publishedYes

Disciplines

  • Life Sciences

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