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Defining time-dependent hydraulic boundary conditions for the analysis of the climate variability of extremes of coastal flooding UFORIC

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UFORIC Understanding Flooding on Reef-lined Island Coasts Workshop

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Defining time-dependent hydraulic boundary conditions for the analysis of the climate variability of extremes of coastal flooding UFORIC

  1. 1. Fernando J. Mendez1, D. Anderson2 , P. Ruggiero2, A. Rueda1, J. A.A. Antolinez1, L. Cagigal1, C. Storlazzi3, P. Barnard3 Defining time-dependent hydraulic boundary conditions for the analysis of the climate variability of extremes of coastal flooding 1Universidad de Cantabria, Spain 2Oregon State University, Corvallis, OR, USA. 3USGS, USA
  2. 2. 1 2 3 4 Site 1 – Southern California area between Dana Point and Mexican Border (e.g., Naval Base San Diego – NBSD) Site 2 – Republic of Marshall Islands, Kwajalein, Roi-Namur (i.e. Kwajalein Missile Range – KMR) Site 3 – Guam, Apra Harbor area (i.e., Naval Base Guam) Site 4 – Hawaii, Island of Oahu, Kaneohe Bay (i.e., Marine Corps Base Hawaii –MCBH)
  3. 3. Four ingredients of Total Water Level TWL = MSL + ηA + ηNTR + R mean sea level astronomical tide non-tidal residual Run-up
  4. 4. - joint probabilities of compound events - tailor-made predictors at annual, monthly, intramonthly and daily scale - non-linear relationships between predictors and predictands - climate-based multivariate extreme value model - chronology of events - hybrid downscaling of thousands of synthetic events - climate change projections in a feasible way - influence of tropical cyclones Challenges - Multi-Decadal and Inter-annual Scale: Global SLR / ENSO - Annual and Intra-annual Scale: seasonality, Madden-Julian Oscillation, Kelvin waves - Daily Event Scale: tropical cyclones, distant swells and local wind seas Which processes affect Pacific Ocean dynamics? … and at what timescales?
  5. 5. Xm Xa Monthly Predictor XMJO ETd TCd DWT Hydraulic Boundary Cond. Annual Predictor Intraseasonal Pred. Hs0 Tp0 Dir0 Hs1 Tp1 Dir1 Hs2 Tp2 Dir2 ηNTR Hs0 Tp0 Dir0 ηNTR MMSL Regional Predictor Extratropical Cyclones Tropical Cyclones Daily Weather Types ηA Methodological Framework of TESLAFlood(*) (*) Time-varying Emulator for Short- and Long-term Analysis of coastal flooding Hybrid Downscaling Selection Dynamic Downs.(XBeach) Meta-model Maps / Statistics
  6. 6. longitude A S O N D Philosophy: a dynamic predictor, capturing changes in both time and space… Hovmoller Diagrams How to simulate large-scale ENSO variability? Annual Predictor Xa
  7. 7. Principle Component Analysis + Clustering = Select # of Representative “years” (X1,t a ,...,XnPCa ,t a ) AWTt Î{1,...,nAWT }
  8. 8. Xm Xa Monthly Predictor XMJO ETd TCd DWT Hydraulic Boundary Cond. Annual Predictor Intraseasonal Pred. Hs0 Tp0 Dir0 Hs1 Tp1 Dir1 Hs2 Tp2 Dir2 ηNTR Hs0 Tp0 Dir0 ηNTR MMSL Regional Predictor Extratropical Cyclones Tropical Cyclones Daily Weather Types ηA Regression Model for MMSL MMSL(t)= a0 +a1 X1 a (t)+a2 X2 a (t)+a3 X3 a (t)+ b0 +b1 X1 a (t)+b2 X2 a (t)+b3 X3 a (t)( )cos( 2pt 365 )+.. Hybrid Downscaling Selection Dynamic Downs.(XBeach) Meta-model Maps / Statistics
  9. 9. Y0 Multi-modal wave spectra Camus et al 2014 OD Perez et al 2014 OD Rueda et al 2017 JGR Hegermiller et al 2017 JPO HN,TN,DirNH HE,TE,DirE HSEA,TSEA,DirSEA Daily Predictor DWT
  10. 10. Daily Weather Types DWTi ,{i =1,...,nDWT } Based on Rueda et al 2017 JGR Prob
  11. 11. ETd Hs0 Tp0 Dir0 Hs1 Tp1 Dir1 Hs2 Tp2 Dir2 ηNTR Joint Distribution Hs-Tp-Dir Rueda et al 2017 JGR - Multivariate climate based extreme value model
  12. 12. Monte Carlo Simulation of daily multimodal spectra 1000 YEARS OF DAILY SIMULATION Rueda et al 2017 JGR HN,TN,DirNH HE,TE,DirE HSEA,TSEA,DirSEA
  13. 13. Xm Xa Monthly Predictor XMJO ETd TCd DWT Hydraulic Boundary Cond. Annual Predictor Intraseasonal Pred. Hs0 Tp0 Dir0 Hs1 Tp1 Dir1 Hs2 Tp2 Dir2 ηNTR Hs0 Tp0 Dir0 ηNTR MMSL Regional Predictor Extratropical Cyclones Tropical Cyclones Daily Weather Types ηA Hybrid Downscaling Selection Dynamic Downs.(XBeach) Meta-model Maps / Statistics Chronology Model ENSO, seasonality and MJO together!
  14. 14. Kwajalein is affected by ExtraTropical (ET) and Tropical Cyclones (TC)
  15. 15. Pr(DWTt = i DWTt-1 ,...,DWTt-e ,Xt a ,Xt m ,Xt MJO )= = exp(ai +bi Xt + g ij DWTt- j d j=1 e å ) exp(ak + bk Xt + g kj DWTt- j d j=1 e å )k=1 nDWT å ;"i =1,...,nDWT Xt =(X1,t a ,X2,t a ,X3,t a ,cos 2pt Ta ,sin 2pt Ta ,X1,t MJO ,X2,t MJO ) Chronology Model: Climate-based Autoregressive Logistic Model Guanche et al (2013) ClimDyn Antolinez et al (2016) JGR Annual Cycle MJO ENSO ET3 ET3 ET6 ET6 ET6 Cat2 ET1 ET13 ET2 Cat4 ET7 ET7… Categorical time series of DWTs for Extratropical Cyclones (ET) and Tropical Cyclones affecting Kwajalein (Cati) Cat0 Cat1 Cat2 Cat3 Cat4 Cat5 100 simula ons ET1 ET2 ET36 ETt d Î{1,...,nET } TCt d Î{C0 ,...,C5 } DWTt =(ETt d ÈTCt d )Î{1,...,nDWT }
  16. 16. Dealing with Tropical Cyclones Historical TC tracks (IBTracs) Proposed model r=4º Parameters that define a TC - pmin, Minimum Pressure - V, forward velocity - δ, azimut - γ, angle of entrance Historical TC tracks with the proposed model
  17. 17. Dealing with Tropical Cyclones Historical TC tracks + Synthetic Tracks (Nakajo et al 2014) Synthetic Tracks + MDA Selection Algorithm (Camus et al,2011) N=100 N=300Historical Synthetic (N=10959 from 1 Million Worldwide)
  18. 18. Intra-daily simulations: hydrograph approach ET2 ET2 ET6 ET6 ET6 Response function: 𝑇𝑊𝐿 = 𝛼𝐻𝑠 0.5 𝑇𝑝 + 𝑆𝑆 ET6 ET8 ET8 ET8 TWL Modelling triangles: 𝑡 𝐴 Hs Tp ss θ TWL 𝑡 𝐵 ∆𝑡 𝑇𝑊𝐿 𝑚𝑎𝑥2 𝑇𝑊𝐿 𝑚𝑎𝑥2 𝑇𝑊𝐿 𝑚𝑎𝑥1 𝑇𝑊𝐿 𝑚𝑎𝑥3 𝜇1 𝜇2 𝜇3 𝐷𝑊𝑇 = 𝐻𝑠 (𝑘) , 𝑇𝑝 (𝑘) , 𝜃 (𝑘) , 𝑆𝑆, 𝜏, 𝜇 ET2 ET2 ET6 ET6 ET6 ET6 ET8 ET8 ET8 𝜏1 𝜏2 𝜏3 Timing until TWLmax Average TWL during hydrograph
  19. 19. “Dynamic Annual Weather Type” informed by ocean processes Operating in the “continuum” Daily Weather Type depends on covariates at multiple timescales Tropical Cyclone Probabilities depend on all processes Hs for SEA, NH and SH (days) Sea State Type, I (days) Daily Weather Type, DWT (days) Annual Weather Type, AWT (years) … … Ci-1 Ci Ci+1 Realization of a “climate, C” (N years) Astronomical tide, AT (1 year) Astronomical Tide Type, ATT (days) SS (days) Monthly Mean Sea Level, MMSL (months) Simulated Tropical Cyclones Framework is built… Next steps are to actually simulate TESLAFlood Time-varying Emulator for Short- and Long-term Analysis of coastal flooding
  20. 20. Fernando J. Mendez1, Dylan Anderson2 , Peter Ruggiero2, Ana Rueda1, Jose A.A. Antolinez1, Laura Cagigal1, Curt Storlazzi3, Patrick Barnard3 Defining time-dependent hydraulic boundary conditions for the analysis of the climate variability of extremes of coastal flooding 1Universidad de Cantabria, Spain 2Oregon State University, Corvallis, OR, USA. 3USGS, USA Acknowledgments. US Department of Defense, Project SERDP Number RC-2644 (Advancing Best Practices for the Analysis of the Vulnerability of Military Installations in the Pacific Basin to Coastal Flooding Under a Changing Climate) PI: John J. Marra, NOAA NESDIS NCEI. GOW2 data base has been kindly provided by IHCantabria

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