Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model
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, an...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | Earth's Future |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2021EF002285 |
| _version_ | 1818492526750859264 |
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| author | D. L. Anderson P. Ruggiero F. J. Mendez P. L. Barnard L. H. Erikson A. C. O’Neill M. Merrifield A. Rueda L. Cagigal J. Marra |
| author_facet | D. L. Anderson P. Ruggiero F. J. Mendez P. L. Barnard L. H. Erikson A. C. O’Neill M. Merrifield A. Rueda L. Cagigal J. Marra |
| author_sort | D. L. Anderson |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T17:44:27Z |
| format | Article |
| id | doaj.art-1708b293963049b3abf8ffa255a734de |
| institution | Directory Open Access Journal |
| issn | 2328-4277 |
| language | English |
| last_indexed | 2024-12-10T17:44:27Z |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Earth's Future |
| spelling | doaj.art-1708b293963049b3abf8ffa255a734de2022-12-22T01:39:17ZengWileyEarth's Future2328-42772021-12-01912n/an/a10.1029/2021EF002285Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical ModelD. L. Anderson0P. Ruggiero1F. J. Mendez2P. L. Barnard3L. H. Erikson4A. C. O’Neill5M. Merrifield6A. Rueda7L. Cagigal8J. Marra9College of Engineering North Carolina State University Raleigh NC USACollege of Earth, Ocean, and Atmospheric Sciences Oregon State University Corvallis OR USADpto Ciencias y Tecnicas del Agua y del Medio Ambiente Universidad de Cantabria Santander SpainPacific Coastal and Marine Science Center United States Geological Survey Santa Cruz CA USAPacific Coastal and Marine Science Center United States Geological Survey Santa Cruz CA USAPacific Coastal and Marine Science Center United States Geological Survey Santa Cruz CA USAScripps Institution of Oceanography University of California San Diego La Jolla CA USADpto Ciencias y Tecnicas del Agua y del Medio Ambiente Universidad de Cantabria Santander SpainDpto Ciencias y Tecnicas del Agua y del Medio Ambiente Universidad de Cantabria Santander SpainNational Oceanic and Atmospheric Administration Honolulu HI USAAbstract 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.https://doi.org/10.1029/2021EF002285surrogate modelingcoastal floodingstochastic predictionsclimate variabilitycompound extremesfuture sea levels |
| spellingShingle | D. L. Anderson P. Ruggiero F. J. Mendez P. L. Barnard L. H. Erikson A. C. O’Neill M. Merrifield A. Rueda L. Cagigal J. Marra Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model Earth's Future surrogate modeling coastal flooding stochastic predictions climate variability compound extremes future sea levels |
| title | Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model |
| title_full | Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model |
| title_fullStr | Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model |
| title_full_unstemmed | Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model |
| title_short | Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model |
| title_sort | projecting climate dependent coastal flood risk with a hybrid statistical dynamical model |
| topic | surrogate modeling coastal flooding stochastic predictions climate variability compound extremes future sea levels |
| url | https://doi.org/10.1029/2021EF002285 |
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