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Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model.

D L Anderson1, P Ruggiero2, F J Mendez3

  • 1College of Engineering North Carolina State University Raleigh NC USA.

Earth'S Future
|July 22, 2022
PubMed
Summary
This summary is machine-generated.

A new hybrid framework efficiently predicts coastal flood risk by coupling surrogate models with climate emulators. This approach assesses present and future flood events, crucial for coastal resilience planning.

Keywords:
climate variabilitycoastal floodingcompound extremesfuture sea levelsstochastic predictionssurrogate modeling

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Area of Science:

  • Coastal geomorphology
  • Climate change impacts
  • Hydrodynamic modeling

Background:

  • Traditional numerical models for coastal flooding are computationally intensive, limiting the simulation of diverse future climate scenarios.
  • Accurate prediction of spatially varying flood surfaces is essential for understanding coastal hazards.

Purpose of the Study:

  • To develop an efficient surrogate modeling framework for predicting nearshore and estuarine water levels under various offshore forcing conditions.
  • To couple this framework with a climate emulator for downscaling hypothetical climate iterations.
  • To assess present-day and future coastal flood risk, including event chronology and changes over time.

Main Methods:

  • Development of a hybrid statistical-dynamical framework combining surrogate models (Gaussian process regression) with a climate emulator.
  • Utilizing the Coastal Storm Modeling System (CoSMoS), including Delft3D and XBeach, as the dynamic simulator.
  • Validation using in-situ tide gauge data and nearshore pressure sensor arrays.

Main Results:

  • The framework efficiently predicts spatially varying nearshore and estuarine water levels for any combination of offshore forcing conditions.
  • It assesses coastal flood risk, including the timing of flood and dune overtopping events.
  • Reveals the influence of climate variability on flood resilience and the stochastic nature of future water levels.

Conclusions:

  • The hybrid framework provides an efficient method for assessing current and future coastal flood risk.
  • It aids in understanding the impact of climate variability on coastal resilience metrics.
  • Enables detailed analysis of flood event characteristics and their evolution.