Motivation for understanding extreme coastal water levels
S. Dukes
Gryphon Aerial Services
Southern California Flooding
Willapa Bay Washington Flooding
Northern Oregon Coast Flooding
Peter Ruggiero and Fernando Mendez
Dylan Anderson, Laura Cagigal, Ana Rueda, Jose Antolinez, Mark Merrifield, John Marra, Ian Robertson, Ayesha Genz, Meredith Leung, Patrick Barnard, Li Erikson, Andy O’Neill, Tyler Anderson, Zhenqiang Wang
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Combining a stochastic climate emulator with surrogate models of dynamic coastal simulators to drive probabilistic coastal flood impacts research
San Diego, CA Observational Record (Relatively Short)
Serafin et al., 2014: short records preclude accurate and robust estimates of extreme return level events.
Objective: Develop a probabilistic approach to efficiently evaluate coastal flooding in both today’s climate and a range of possible future climates.
Approach: Hybrid statistical dynamical downscaling techniques taking advantage of a broad range of machine learning and data science advances.
Anderson et al., 2019, Parker et al., 2019, Cagigal et al., 2020, Anderson et al., 2021, Marra et al., 2022
Develop a Coastal Flooding ‘Digital Twin’ via:
Linking a stochastic climate emulator with surrogate models (emulators) of high-fidelity simulators
1.
2.
Stochastic Climate Emulator: Time-varying emulator for short- and long-term analysis of coastal flooding (TESLA; Anderson et al., 2019; JGR: Oceans)
Weather Typing: via identifying dominant modes of spatial variability through EOFs and K-means clustering with respect to Principal Components
Anderson et al., 2019
Chronology Model: Climate-based Autoregressive Logistic Model
Includes ENSO, seasonality, MJO together, and previous two days of synoptic weather together
Guanche et al. (2014), Antolinez et al. (2016)
Emulation of local metocean conditions: Sample from Gaussian Copulas for waves and storm surge
Individual waves, winds, surges, etc.
TWL Hydrographs
TESLA Generated Monte Carlo Simulations of Multivariate Forcing
TESLA Generated Monte Carlo Simulations of Multivariate Forcing
TESLA Generated Monte Carlo Simulations of Multivariate Forcing
TESLA Generated Monte Carlo Simulations of Multivariate Forcing
Not all extreme total water level events are created equal!
There are an infinite combination of drivers (Hs, Tp, Dir, SS, tide, MMSL, and rainfall/river discharge) that can combine to generate the 100-year return period event
100s of 500-year synthetic time series simulated with TESLA
Extreme Value Application: Return Period Analysis of a Proxy of Flooding
Marra et al., 2022
El Ninos
La Ninas
Extreme Value Application: Assessing the influence of climate variability on extreme TWLs
Anderson et al., 2019
Resilience Metric Application: Compute the influence of seasonality on extreme TWL exceedances and impacts
Slope = 0.039
Toe elev. = 3.53 m
Crest elev. 6.14 m
(a)
(b)
(c)
Leung et al., in revision
Resilience Metric Application: Compute the influence of sea level rise on extreme TWL exceedances and impacts
Slope = 0.039
Toe elev. = 3.53 m
Crest elev. 6.14 m
(a)
(b)
(c)
Leung et al., in revision
Tesla derived input conditions (21 Dimensions)
Hs, Tp, θ,
SLP, U, V
tides
Numerical Model
Hazard Assessment
Teach a statistical model to “behave” like the numerical model
Surrogate models (Emulators) of high-fidelity simulators:
CoSMoS- D3D and XBeach
… machine learning approaches: Gaussian Process Regression;
Neural Networks;
Radial Basis Functions;
Random Forests; etc.
Jia & Taflanidis (2013); Jia et al. (2016); Parker et al. (2019); Goldstein et al. (2019); Anderson et al., 2021
Freeboard
Naval Base Coronado Bayside Application: Surrogate model-derived stochastic future pier use threshold exceedances
Mostly functional
Partially functional
NOT functional
Anderson et al., 2021
Beach width
Naval Base Coronado Oceanside Application: Surrogate model-derived daylight hours available for beach training
Anderson et al., 2021
Dune erosion
Naval Base Coronado Oceanside Application: Surrogate model-derived frequency and style of berm/dune impacts
Anderson et al., 2021
Barrier inundation
Naval Base Coronado Oceanside Application: Surrogate model-derived flood depths during El Niño event and 1.0 m of SLR at 2100
Marra et al., 2022
Naval Base Coronado Oceanside Application: Surrogate model-derived flood depths during El Niño event and 1.5 m of SLR at 2100
Marra et al., 2022
Barrier inundation
Computational reduction via machine learning derived hydrodynamic emulators
Final Thoughts:
Hybrid statistical dynamical modeling, utilizing a range of big data and machine learning approaches, is a powerful approach for producing stochastic, spatially varying extreme total water levels for probabilistic coastal flood risk analysis
Coupled with surrogate models of high-fidelity numerical simulators these approaches can provide a fully stochastic exploration of coastal flooding risk (including compound flooding)
https://github.com/teslakit/teslakit/
Wide range of coastal flood risk applications