Urban Street-Scale Hydrodynamic Flood Modeling of Micro-Burst Rainfall

Transcription

1 Urban Street-Scale Hydrodynamic Flood Modeling of Micro-Burst Rainfall Dr. Jon Derek Loftis Asst. Research Scientist, VA Inst. of Marine Science Sridhar Katragadda Systems Analyst, City of Virginia Beach Kyle Spencer GIS Team Supervisor City of Norfolk ESRI User Conference, July 11, 2017 Advanced Computational Methods in Water Resources

2 Outline 1. Introduction Emerging Flood Model Verification Methods (Sensors, Citizen Science, and Drones) Sensor Network and Sub-Grid Modeling Approach 2. Methods: Model Setup and Grid Development 3. Results & Discussion Tropical Storm Julia (Sept , 2016) Hurricane Matthew (Oct. 8-9, 2016) 4. Conclusions

3 1. Introduction Emerging Flood Model Verification Methods (Sensors, Citizen Science, and Drones) Sensor Network and Sub-Grid Modeling Approach

4 1. Introduction The Hampton Roads region is the second-largest population center in the U.S. at risk from sea level rise (Boon, Brubaker and Forrest, 2010; Mitchell et al., 2013) More than 400,000 properties exposed to flood or storm surge inundation (CoreLogic, 2015) Population of over 1.7 million people, living and traveling on roads exposed to severe and increasing frequent chronic nuisance flooding (Ezer and Atkinson, 2014) Existing flood communication and messaging systems have not yet responded to the changing risk patterns brought by sea level rise and have not been able to meet the needs of diverse at-risk communications audiences (IoT sensors and predictive models can help) A better understanding of flood risk perception, information-seeking behavior and decision-making can inform the development of new communications tools and flood risk messaging

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6 (less) Conventionality (more) Scientific Reliability Emerging Flood Model Verification Methods Water Level Sensors (NOAA, USGS, COOS, VIMS) Ultrasonic Sensors (Cities) GPS Citizen-Science Sea Level Rise Mobile App (Wetlands Watch) ArcGIS Collector App (ESRI) 4K Aerial Drone Surveys Drone2Map (ESRI) Photosynth (Microsoft)

7 Sea Level Rise App How it s used Collect GPS max. flood extent data Frequently flooded areas are identified in trouble section What information is gathered? Pics of flooding Text descriptions How do I use it? Assess accuracy of flood forecasts Web Map of Suggested App Features & Updates:

8 Project Partners Project Partners (as of July 2017):

9 2. Methods StormSense Model Setup Grid Development

10 Sensor Network and StormSense Model Inputs Observations & Predictions 6-min automated retrieval script IoT Stream Gauge Network StormSense Hydrodynamic Forecast Model Server StormSense Web Portal stormsense.com

11 StormSense Model Output Methods Amazon Web Service for StormSense Sensor Data: Water levels extracted from grid cells with water level observations Perl and python scripts run in the background to produce geotiff rasters of water level and flood heights (water level- land elevation) for each 6-minute interval Spatial outputs are prepared as.kml files and javascript-layers for production of open layers maps, Google Maps, and Google Earth animations.

12 Model Grid Development with Lidar-Derived DEM Chesterfield Heights, Grandy Park, and Broad Creek City of Norfolk Central Norfolk Superposed with Chesterfield Heights Represented by Sub-Grid Downtown Norfolk and Hall City Hall Superposed with Sub-grid ODU ODU City Peninsula Peninsula Hall Represented Superposed Represented bycity with Sub-grid by Sub-grid Sub-grid Old Dominion University and Peninsula Central Norfolk Edgewater Haven Middle Towne Arch Norfolk Scope Arena ODU President s Residence Foreman Field Moseley Creek Grandy Park Norfolk City Hall 12 Norfolk s Stadium

13 Model Setup (Rainfall) *Radar Derived Cumulative Rainfall totals (in.) over 72 hours from Sept , HRSD Rainfall Sensors (15 min intervals) MMPS-004-RAINGAUGE-56 John B. Dey *Rainfall totals from HRSD (in/15 min)

14 3. Results & Discussion Tropical Storm Julia (Sept , 2016) Hurricane Matthew (Oct. 8-9, 2016)

15 Tropical Storm Julia & Hurricane Matthew

16 City Dashboard Systems

17 Matthew - Crowd-Sourced Damage Assessments

18 Hurricane Matthew Drone2Map Survey Workflow: Capture Video Parse to Images (0.25 sec) Edge Detection Laplace Transform of Pixel Values (Sobel) Supervised Classification Import to Drone2Map with XYZ Drone Video by John Ehlers, Norfolk

19 Hurricane Matthew Drone2Map Survey Drone video footage of Llewellyn Ave near Haven Creek Boat Ramp in Norfolk at 2:30pm on Oct. 9, 2016 Video > Images > Edge Detection > Laplace Transform of Pixel Values > Supervised Classification > Drone2Map

20 Drone2Map Survey Linear Path Forecast Modeled Extents at 2:30pm on Oct. 9, Llewellyn Ave Plotted with Maximum Inundation Extents from Drone2Map Legend: Drone Video Still Image SLR App Data Point Avg. Horizontal Dist. Diff. = (n = 263 GPS points)

21 So why is the model over-predicting flooding here? *Model DEM is sourced with 2009 lidar before the ground was raised

22 Drone2Map Survey Panoramic Path Drone video footage of Monticello Ave near Haven Creek Boat Ramp in Norfolk at 3:00pm on Oct. 9, 2016 Drone Video Footage > Images > Drone2Map Current Flooding Extent Avg. Horizontal Dist. Diff. = 14.39m (n = 137 points)

23 Crowd-Sourced Damage Assessments Drone2Map Flooding Extent at 3:00pm Avg. Horizontal Dist. Diff. = 14.39m (n = 137 points) Video > Images > Edge Detection > Laplace Transform of Pixel Values > Supervised Classification > Drone2Map

24 4. Conclusions The sub-grid model forecasted tidal flooding during Hurricane Matthew in Sept and was well validated via tide gauges and Sea Level Rise App GPS extent data: Vertical Accuracy: aggregate RMSE of 8.19 cm (n=5; 416ts each) Horizontal Accuracy: distance diff. of m (n=263; GPS pts) Through StormSense, 24 more sensors are planned for installation throughout Hampton Roads by the end of July, courtesy of NIST RSCT funds, VDEM, & Virginia Beach CIP.

25 4. Conclusions (cont d) Tropical Storm Julia caused more than 14 inches of rainfall over 3 days time in parts of Norfolk, Chesapeake, and Virginia Beach. The NWS under-predicted this amount by as much as 4 inches in some parts of Hampton Roads This caused model under-prediction for Hurricane Matthew in inland regions when compared with Drone2Map surveyed extents for an Avg. Horizontal Dist. Diff.=14.39m (n = 263 pts).

26 Thank You