Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model. Issue 12 (3rd December 2021)

• Record Type: Journal Article
• Authors: Anderson, D. L.; Ruggiero, P.; Mendez, F. J.; Barnard, P. L.; Erikson, L. H.; O'Neill, A. C.; Merrifield, M.; Rueda, A.; Cagigal, L.; Marra, J.
• Is Part Of: Earth's future. Volume 9:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-03
• Subjects: surrogate modeling -- coastal flooding -- stochastic predictions -- climate variability -- compound extremes -- future sea levels; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2021EF002285
• Languages: English
• ISSNs: 2328-4277

Abstract

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. Plain Language Summary: Coastal flooding is caused by the combination of winds, waves, tides, and regional changes in water levels due to large‐scale climate patterns such as the El Niño Southern Oscillation. Although high fidelity numerical models are our best representation of nearshore and estuarine physical processes, the random nature of climate, weather/storms, and their timing interfering with tides results in too many hypothetical futures to simulate with computationally expensive models. Machine learning techniques are used in this study to efficiently derive coastal water levels throughout the 21st century based on a subset of numerical model simulations. The future simulations provide hourly information allowing for coastal managers to explore resilience metrics that consider the event characteristics (i.e., durations and environmental forcing) of both extreme flood events resulting from large storms and sunny‐day flooding due to sea level rise. Key Points: A surrogate model was coupled with a stochastic climate model to forecast hypothetical future coastal impacts Future changes of characteristics driving flooding and dune impact events are projected contingent on sea level rise Relevant time scales of climate variability are incorporated into resilience metrics at Naval Base Coronado, San Diego