StormSense

StormSense
StormSense
Team Organizations	Virginia Institute of Marine Science (VIMS) College of William and Mary Commonwealth Center for Recurrent Flooding Resilience State of VA Department of Health
Team Leaders	Jon Loftis
Participating Municipalities	Newport News VA Virginia Beach VA Norfolk VA Hampton VA Portsmouth VA Chesapeake VA Williamsburg VA York County VA
Status	Implemented
Document	None

Description

Objectives

• Apply modeling to address multiple-flood types to determine the probable areas at risk by utilizing fixed sensors, crowd-sourced data collection verified by post-flood analysis.
• Use new state-of-the-art high resolution hydrodynamic models driven with atmospheric model weather predictions to forecast flooding from storm surge, rain, and tides at the street-level scale to improve disaster preparedness.

Challenges

• Communities are often at risk from different types for flooding, however, current models tend to focus on a single type/source
• Sensors and gauges are typically deployed to detect single types of flooding (riverine, tidal, rainfall, etc.)
• Forecast model integration with existing GIS systems for short term pre-disaster planning and deployment of resources

Solutions

A Long-Term Predictive Modeling Plan for local, regional, national, and global applications described here.

Major Requirements

• Determine applicable models best suited to local conditions
• Beginning with tidal/surge modeling, conduct local verification of models vs. “ground truth”
• Determine locations for additional sensors and types
• Utilize the existing WiFi connectivity for sensors, and alerting systems
• Recruit/train local personnel to take real-time readings to update the forecasting
• Expand the program to neighboring jurisdictions, then regionally

Performance Targets

Key Performance Indicators (KPIs)	Measurement Methods
Verify the accuracy of the VIMS TideWatch predictions with post-storm analysis Verify the accuracy of the multi-flood type models with post-storm analysis with future goals are to improve prediction of flood depths and extents by 10% between stages 2 and 3; data was collected during Jan. 2016 winter storm event; not yet validated.	Statistical analysis of forecasts vs. time issued, actual flood area and depth. Emergency response-time analytics during upcoming flood scenarios

Standards, Replicability, Scalability, and Sustainability

• Sensors utilizing common internet protocols over the city’s WiFi and existing SCADA systems
• Crowd sourced data collection via a smartphone application
• Modeling will be accessible via a website
• Linkages to alerting would be via the flood model’s Online GIS Data Release Platform and potentially through the SLR mobile App
• Expand the program and modeled areas to neighboring jurisdictions, then regionally via A Long-Term Predictive Modeling Plan for local, regional, national, and global applications

Cybersecurity and Privacy

TBD

Impacts

• Predicts the timing of flooding and flooded evacuation routes
• Aids in rerouting emergency routes for public safety
• Saves lives and reduces property damage
• Identified future mitigation projects

Demonstration/Deployment

• Phase I Pilot/Demonstration:

1. Deploy the VIMS forecast and Tidal/Surge maps
2. Deploy the data collection application
3. Verify accuracy of maps and forecasts as opportunities arise

• Phase II Deployment:

1. Add additional flood forecasting tools and display methods
2. Add additional fixed sensors and collection infrastructure
3. Add additional TideWatch gauges in the James River
4. Expand the project throughout the Peninsula and into other parts of the Hampton Roads Region