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

Sridhar Katragadda

Systems Analyst,

City of Virginia Beach

Dr. Jon Derek Loftis

Asst. Research Scientist,

VA Inst. of Marine Science

ESRI User Conference, July 11, 2017

Advanced Computational Methods in Water Resources

Kyle Spencer

GIS Team Supervisor

City of Norfolk

Outline1. 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. 19-22, 2016)

▪ Hurricane Matthew (Oct. 8-9, 2016)

4. Conclusions

1. Introduction ▪ Emerging Flood Model Verification Methods

(Sensors, Citizen Science, and Drones)

▪ Sensor Network and Sub-Grid Modeling Approach

• 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

1. Introduction StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

https://twitter.com/NASA_Rain/status/786304424847167488/video/1

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

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)(less)

Conventionalit

y

(more

)

Scie

ntific R

elia

bili

ty

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

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:

Project Partners (as of July 2017):

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Project Partners

2. Methods

▪ StormSense Model Setup

▪ Grid Development

IoT Stream Gauge

Network

StormSense

Hydrodynamic

Forecast Model

Server StormSense

Web Portal

6-min automated

retrieval script

Observations & Predictions

stormsense.com

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Sensor Network and StormSense Model Inputs

StormSense Model Output Methods

• 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 .

Amazon Web Service for

StormSense Sensor Data:

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Model Grid Development with Lidar-Derived DEM

12

Central Norfolk Superposed with Sub-gridCentral Norfolk Represented by Sub-gridDowntown Norfolk and City HallCity Hall Superposed with Sub-gridCity Hall Represented by Sub-grid

Norfolk Tides

Stadium

Norfolk

City Hall

Norfolk

Scope

Arena

City of NorfolkOld Dominion University and PeninsulaODU Peninsula Superposed with Sub-gridODU Peninsula Represented by Sub-grid

Edgewater

Haven

Foreman Field

ODU

President’s

Residence

Chesterfield Heights, Grandy Park, and Broad CreekChesterfield Heights Represented by Sub-Grid

Middle Towne

Arch

Moseley

CreekGrandy

Park

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Model Setup (Rainfall)

*Radar

Derived

Cumulative

Rainfall totals

(in.) over 72

hours from

Sept. 19-22,

2016.

*Rainfall totals

from HRSD

(in/15 min)

HRSD Rainfall Sensors

(15 min intervals)

MMPS-004-RAINGAUGE-56 John B. Dey

3. Results & Discussion

▪ Tropical Storm Julia (Sept. 19-22, 2016)

▪ Hurricane Matthew (Oct. 8-9, 2016)

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Tropical Storm Julia & Hurricane Matthew

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

City Dashboard Systems

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Matthew - Crowd-Sourced Damage Assessments

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Hurricane MatthewDrone2Map Survey

Drone Video by John Ehlers, Norfolk

https://youtu.be/R8ZYxubUo-w

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 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

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Hurricane MatthewDrone2Map Survey

Forecast Modeled Extents at 2:30pm on Oct. 9, 2016 @ Llewellyn AvePlotted with Maximum Inundation Extents from Drone2Map

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Drone2Map SurveyLinear Path

Avg. Horizontal Dist. Diff. =

11.21 (n = 263 GPS points)

Legend:

Drone Video Still Image

SLR App Data Point

So why is the model over-predicting flooding here?

*Model DEM is sourced with 2009 lidar before the ground was raised

Current Flooding Extent

Avg. Horizontal Dist. Diff.

= 14.39m (n = 137 points)

Drone Video Footage > Images > Drone2Map

Drone video footage of Monticello Ave near Haven Creek Boat Ramp in Norfolk at 3:00pm on Oct. 9, 2016

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Drone2Map SurveyPanoramic Path

Drone2Map Flooding

Extent at 3:00pm

Avg. Horizontal Dist. Diff.

= 14.39m (n = 137 points)

StormSense ProjectForecasting Flooding from Storm Surge, Rain, and

Tide

Crowd-Sourced Damage Assessments

Video > Images > Edge Detection > Laplace Transform of Pixel Values > Supervised Classification > Drone2Map

4. Conclusions

▪ The sub-grid model forecasted tidal flooding during

Hurricane Matthew in Sept. 2016 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 11.21 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.

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).

Thank You