Exploring Coastal Hazards in Virginia and North Carolina via Reanalysis of 2011 Hurricane Irene with Future Sea Level Rise Andrew Molthan Earth Science Branch, NASA Marshall Space Flight Center, Huntsville, Alabama in collaboration with: D. Loftis1, H. Wang1, D. Forrest1, J.R. Bell2, J. Srikishen3, D. Sun4, D. Bekaert5, S. Cohen6, T. Allen7, B. Hamlington7, W. Moore8 , L. Rogers9, D. Borges9, W. Ball9, J. Murray9, D. Green10 1Virginia Institute of Marine Science, College of William and Mary, Glouchester Pt., VA 2Earth System Science Center, University of Alabama Huntsville, Huntsville, AL 3Universities Space Research Association / NASA Marshall, Huntsville, AL 4George Mason University, Fairfax, VA 5Jet Propulsion Laboratory / California Institute of Technology, Pasadena, CA 6University of Alabama, Tuscaloosa, AL 7Old Dominion University, Norfolk, VA 8Hampton University, Hampton, VA 9NASA Langley Research Center, Langley, VA 1 10Program Manager, NASA Applied Sciences: Disasters Program, Washington, D.C. Background • In FY17, NASA’s Applied Sciences: Disasters Program began a small pilot project titled “Mid-Atlantic Resiliency Demonstration Study, Communities at Intensive Risk” • Strategic Goals and Objectives: • Demonstrate how coupling diverse models, data and predictions enable us to develop and extend our collective knowledge of compounding risk • Collaborate with partners to understand their data-driven needs for decision making in coastal communities with intensive risk • Demonstrate how various tools, ranging from modeling to remote sensing, can help to identify current and future areas of risk. • Engage with key external partners to understand their goals for improving disaster resilience and participate as a key collaborator. • Focus: • Holistic, interdisciplinary research and integrated application outcomes focused on improving resilience for coastal communities. Areas of Interest: Virginia • Preliminary focus in Virginia based upon previously established partnerships in these areas and significant impacts from past events. Portsmouth, VA Norfolk, VA Sea Level Rise Scenarios • Craney Island Naval Supply Center • Naval Station Norfolk • Norfolk Naval Shipyard • NATO Strategic Command HQ https://coast.noaa.gov/floodexposure Areas of Interest: N. Carolina • Preliminary focus in N. Carolina focused on partnerships as well as unique access to high-resolution data for analysis. Sea Level Rise Scenarios Pamlico County, NC https://coast.noaa.gov/floodexposure Technical Components Large-scale model Bldg. Footprints WX Forcing Informs Sub-grid model SCHISM –open-source coastal Water flow and friction NASA-Unified WRF Model Street level flood ocean model Digital Elev. Models Simulate the hazard visualization Robust Storm Tide Predictions Topography SLR Rate 3D Hydrodynamic Model Influence of long-term hazard Local Subsidence Rates Map larger hazards to local-scale impacts – First Floor Elevation Influence of long-term hazard depicts time-relevant flood extent and depth Predicted water depth relative to flooded structure Use to Compare / Validate Model Outcomes 10m MODIS Footprint Remote Sensing Flood and Depth Maps 30m Landsat Depth Maps Validate and improve modeling based upon observations Maps Results in Building-level impact analysis in areas of intensive risk 2011 Irene Simulations J. Case, J. Srikishen, A. Molthan, NASA Marshall • Using the NASA-Unified WRF Model to create a reanalysis of an Irene-like storm, based upon GFS analysis data available from 2011. • OBS • 9 km • 3 km • 1 km • Early issues with simulating the storm from GFS analyses: landfall was too early, storm was too intense. Exploring solutions through various initialization times, parameterizations, TC initial conditions, perhaps switch to HWRF. • For exploratory work and fitting to other models, track and timing are reasonable for approach to NC/VA. ECMWF also provides good Irene simulations Large-Scale Storm Tide Modeling Z. Liu, H. Wang, Virginia Institute of Marine Science • SCHISM (Semi-implicit, Cross-scale, Hydro- science Integrated System Model)  Operationally tested and proven (NOAA, DWR, CWB…) SCHISM Simulation Results Driven with ECMWF Wind and Pressure Data Animation of Model Output near Chesapeake Bay Goal: Establish confidence in model capabilities for predicting coastal hazards *Reasonable comparison by when combined with supporting numerical SCHISMwithobservationsusing weather prediction inputs, then explore ECMWF forecast data, while WRFsimulationimproves with varying intensity and other types of coastal change (sea level, subsidence). Street-Level Flood Modeling J.D. Loftis, Virginia Institute of Marine Science SSuCbH-IGSMrid E Masot dCeola: sNto Mrfoodlkel Domain • Driven with water levels and atmospheric forecasts from Large-Scale SCHISM model • Compares well with local sensor data RMSE=3.7 cm 2011 Hurricane Irene Max. Flood Extent Forecast: Contour Map in Norfolk’s Larchmont Neighborhood Colonial 0 Pl. (Fugro) Downtown Pump St. (Fugro) Rec. Center (Fugro) Money Point (NOAA) Goal: Develop dynamic flood RMSE=4.8 cm RMSE=5.3 cm forecast maps to effectively and quickly communicate inundation risk; further aid Colonial Place (Fugro) prediction of long-term coastal storm hazards with sea level rise & subsidence predictions. Geospatial Integration for Hurricane Irene Reanalysis Dr. Tom Allen, Geography Program and Commonwealth Center for Recurrent Flooding Resiliency (CCRFR) Old Dominion University, Norfolk, VA • Demonstrate geospatial analysis and integration for flood impacts from storm surge models, SAR, and LiDAR • Source and provide supporting data for Irene • NC Flood Inundation Mapping and Alert Network • High Water Marks (NWS and USGS) • Dasymetric population demographics • NHC Hurricane forecast track, cone, watches, warnings, and surges https://fiman.nc.gov/fiman/ • SLOSH surge MOMs and MEOWS, NC-CERA/Renci ADCIRC • Engage stakeholders for needs and applications • NWS, cities, and Hampton Roads Planning District Commission • Hampton Roads Coastal Resiliency Working Group • Leverage modeling and remote sensing for time- dependent planning, response, and resiliency • Develop lessons learned, uncertainties, and improvements for future research • Sentinel, NISAR, other platforms and data sets http://nc-cera.renci.org/ Revisiting Flood Maps with new VIIRS Flood Mapping Capability Dr. Lillian Sun, George Mason University • Team from George Mason University is revisiting MODIS and Landsat remote sensing adopting new techniques developed for VIIRS: • Reduced impact of terrain shadows, cloud shadows, and other potential errors Improving Access to Flood Mapping Dr. W.B. Moore, Hampton University • Incorporating VIIRS algorithm for processing of NRT MODIS and other imagery • Share with partners to improve flood mapping capabilities from NASA MODIS sensors