Projecting climate dependent coastal flood risk with a hybrid statistical dynamical model

Earth's Future
By: , and 

Links

Abstract

Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.

Study Area

Publication type Article
Publication Subtype Journal Article
Title Projecting climate dependent coastal flood risk with a hybrid statistical dynamical model
Series title Earth's Future
DOI 10.1029/2021EF002285
Volume 9
Issue 12
Year Published 2021
Language English
Publisher American Geophysical Union
Contributing office(s) Pacific Coastal and Marine Science Center
Description e2021EF002285, 24 p.
Country United States
State California
City San Diego
Other Geospatial Naval Base Coronado
Google Analytic Metrics Metrics page
Additional publication details