Coastal Hazards System: Interpretation and Application

Transcription

1 Lessons Learned and Best Practices: Resilience of Coastal Infrastructure Hato Rey, PR March 8-9, 2017 Coastal Hazards System: Interpretation and Application Victor M. Gonzalez, P.E. Team: PI: Jeffrey A. Melby, PhD Co-PI: Norberto C. Nadal-Caraballo, PhD Fatima Diop Debbie Green Coastal and Hydraulics Laboratory US Army Engineer R&D Center Vicksburg, MS

2 Coastal Hazards System Background Main features of Probabilistic Coastal Hazard Assessment (PCHA) Full quantification of the hazard in a probabilistic context Coverage of practical forcing-parameter and probability space Computation of joint probability of storm forcing and responses, maintaining the relationship between both and across responses High fidelity in modeling and statistics Regional Quantification of uncertainty (how accurate?) PCHA allows comprehensive and accurate understanding of the storm hazard for use in risk context

3 Coastal Hazards System Background PCHA study outputs Climatology: Historical storms, synthetic-storm suite with associated relative probabilities, extratropical storm reanalysis data, measurements. Meteorological modeling simulation results (e.g. wind speed) Hydrodynamic modeling: grids, simulation results (e.g. currents, peaks and time series for tide, surge, total water level, and wave height, with corresponding wave period and direction). Statistical analysis results: JPM, hazard curves, and uncertainty. Regional, high fidelity, and scenarios (e.g. SLC, tide, etc.) Very Large Datasets 3

4 Coastal Hazards System Maximize data relevance and usability Coastal storm hazards data repository and mining system Delivery framework for wide range of coastal hazards tools and capabilities, not just a database Leverage federal regional wave/water level modeling studies (National coastal storm hazard data resource) Visualize and download through user friendly web tool 4

5 Coastal Hazards System Goals Long-term storage and public access > well-vetted measured and high-fidelity modeled coastal storm data. Easily accessible data; search, browse, visualize Contextual data products and tools that support federal decision making Complete statistical description Support risk management/ assessment/ communication Support project design and evaluation Support expedient coastal storm response prediction, emergency management, operations 5

6 North Pacific Mainland Coastal Hazards System Regions Southern North Hawaii Pacific South Pacific Gulf of Mexico 6

7 CHS Data Available For each Project HURDAT Central Pressure, Rmax, Forward Speed ADCIRC Water Elevation, Wind, Current, Pressure NLR Astronomical Tide Bias & Uncertainty Global Sea Level Change Bias & Uncertainty NOAA 6min, Hourly, Monthly Water Level Storm Conditions Model Results Statistics Observations TROP Central Pressure, Rmax, Forward Speed STWAVE SWAN WAM Wave Height, Direction, Period SRR Storm relative Probabilities Storm Rate, Low & High intensity storm rate AEP Expected Value Average Recurrence Interval Confidence Limit for each AEP NDBC Hourly wave 7

8 Coastal Hazards System NACCS Example Data Approximately 19,000 output locations Peaks and time series files for all storms in HDF5 format Thirteen (13) Annual Exceedance Probability (AEP) values corresponding to:1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, years ARI Confidence Limits :2%, 5%, 10%, 16%, 84%, 90%, 95%, 98% (2,16, 84, 98% plots) Storm recurrence rates, storm relative probabilities Measurements for storms and associated GPD AEP Grids, model inputs, reports, Matlab codes. 8

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10 Coastal Hazards System Map Interface Improvements Transitioning CHS map interface to ESRI Eliminate browser compatibility issues Provide enhanced geospatial capabilities USACE, South Atlantic Division Mobile District team Irven Ingram Technical Lead Code modifications underway ESRI ArcGIS API for JavaScript SceneView 10

11 Select CHS Web Tool CHS NACCS Example Select Project Select Sub-Project / Simulation Conditions NEW: Statistics Results Annual Exceedance Probability (AEP) Non-Linear Residual (NLR) 11

12 CHS NACCS Example - AEP Zoom in to view icons Clicking on an icon displays icons for each type of model result and statistic result selected in Navigation Options pane Clicking the AEP icon displays point information and a plot image 12

13 Unique features of CHS Entire hazard Joint statistics Updatable Self-describing files Summary info and detailed modeling scenarios Storm probabilities for sampling Downscaled storm set recompute probabilities 13

14 CHS Applications Making use of CHS Consume end product e.g. hazard curves Apply the statistical relationships between forcing and responses Stochastic design Stochastic risk assessment Monte Carlo Simulation (MCS) - Storm relative probabilities and storm recurrence rates at specific locations can be used to sample storms. Coastal Structure Reliability Joint evaluation of WL and waves (H m0, T p, & θ) with MCS Storms 14

15 CHS Applications: Risk and Reliability Stochastic design: reliability Stochastic assessment: MCS, risk Lajes, Azores AFB breakwater damage/repair StormSim Monte Carlo Simulation (MCS) Tropical cyclones Storm rates for tropical cyclones Storm relative probabilities Extratropical cyclones Gaussian Copula StormSim Coastal Structure Reliability CHS epistemic uncertainty Structure section/material properties Structure model epistemic uncertainty Level III reliability analysis Limit state exceedance (damage, overtopping, transmission, etc.) Probability of failure or conversely, reliability 15

16 CHS Applications Coastal Hazard Rapid Prediction System (CHRPS) High-fidelity Surrogate Models for Hurricane Response Training of CHS forcing and response using GPM. Rapid prediction of response: inundation (surge+tide), wave height, wave period, wave direction, currents, wind speed, wind direction Forecasts at multiple days prior to landfall NOAA and Coastal Hazards System data linkage for scenario predictions Uncertainty estimates Risk Estimates CHS or Stand-alone 16

17 Overview CHS Applications - CHRPS Validate metamodel CHS regional storm modeling Response data: correct dry nodes, etc. Train metamodel Neural Networks Gaussian Processes NOAA cyclone forecast: lat, lon, DP, v, q, R max Predict highfidelity response 17

18 CHRPS Katrina Validation

19 Contact Information Victor M. Gonzalez, P.E. U.S. Army Engineer R&D Center Coastal and Hydraulics Laboratory Victor M. Questions? 19