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{{ActionCluster
{{ActionCluster
 
|image=stormsense-logo.png
| title            = StormSense - Tidal, Riverine, Inland, Surge Flood Management Program
|team=Virginia Institute of Marine Science (VIMS), College of William and Mary, Commonwealth Center for Recurrent Flooding Resilience, State of VA, Department of Health
 
|leader=Jon Loftis
| team             = Virginia Institute of Marine Science (VIMS), College of William and Mary, Commonwealth Center for Recurrent Flooding Resilience, State of VA, Department of Health
|imagecaption=StormSense
 
|municipalities=Newport News VA, Virginia Beach VA, Norfolk VA, Hampton VA, Portsmouth VA, Chesapeake VA, Williamsburg VA, York County VA
| leader           = J. Derek Loftis
|status=Implemented
| email            = jdloftis@vims.edu
|website=https://vims-wm.maps.arcgis.com/apps/MapJournal/index.html?appid=62c80853313743f3acf5a83ab420d015
| image            = stormsense-logo.png
|description=Objectives
| imagecaption     = StormSense
| municipalities   = Newport News, VA
<!-- , City of Virginia Beach, City of Norfolk, City of Hampton, City of Portsmouth, City of Chesapeake, City of Williamsburg, York County, VA -->
| status           = Implemented
| website           = [https://vims-wm.maps.arcgis.com/apps/MapJournal/index.html?appid=62c80853313743f3acf5a83ab420d015 StormSense]
| download          =
 
| description       =  
* 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.  
* 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.
* 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
 
| 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.)
* 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
* 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 [https://wm1693.app.box.com/s/v2nw4b01cwj1py6xwaxi6i7r0hbl9s57 here].
| solutions             =  
|requirements=* Determine applicable models best suited to local conditions
A Long-Term Predictive Modeling Plan for local, regional, national, and global applications described [https://wm1693.app.box.com/s/v2nw4b01cwj1py6xwaxi6i7r0hbl9s57 here].
 
 
| requirements       =  
* Determine applicable models best suited to local conditions
* Beginning with tidal/surge modeling, conduct local verification of models vs. “ground truth”
* Beginning with tidal/surge modeling, conduct local verification of models vs. “ground truth”
* Determine locations for additional sensors and types
* Determine locations for additional sensors and types
Line 36: Line 20:
* Recruit/train local personnel to take real-time readings to update the forecasting
* Recruit/train local personnel to take real-time readings to update the forecasting
* Expand the program to neighboring jurisdictions, then regionally
* Expand the program to neighboring jurisdictions, then regionally
 
|kpi=* Verify the accuracy of the VIMS TideWatch predictions with post-storm analysis   
 
 
 
| kpi               =  
* 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.
* 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.
 
|measurement=* Statistical analysis of forecasts vs. time issued, actual flood area and depth.
 
 
| measurement       =  
* Statistical analysis of forecasts vs. time issued, actual flood area and depth.
* Emergency response-time analytics during upcoming flood scenarios
* Emergency response-time analytics during upcoming flood scenarios
 
|standards=* Sensors utilizing common internet protocols over the city’s WiFi and existing SCADA systems
 
 
| standards         =  
* Sensors utilizing common internet protocols over the city’s WiFi and existing SCADA systems
* Crowd sourced data collection via a smartphone application
* Crowd sourced data collection via a smartphone application
* Modeling will be accessible via a website
* 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
* 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  
* 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=TBD
 
|impacts=* Predicts the timing of flooding and flooded evacuation routes
| cybersecurity         =  
 
| impacts                 =  
* Predicts the timing of flooding and flooded evacuation routes
* Aids in rerouting emergency routes for public safety
* Aids in rerouting emergency routes for public safety
* Saves lives and reduces property damage
* Saves lives and reduces property damage
* Identified future mitigation projects
* Identified future mitigation projects
 
|demonstration=* Phase I Pilot/Demonstration:
 
| demonstration       =
 
* Phase I Pilot/Demonstration:
# Deploy the VIMS forecast and Tidal/Surge maps
# Deploy the VIMS forecast and Tidal/Surge maps
# Deploy the data collection application
# Deploy the data collection application
Line 81: Line 44:
# Add additional TideWatch gauges in the James River
# Add additional TideWatch gauges in the James River
# Expand the project throughout the Peninsula and into other parts of the Hampton Roads Region
# Expand the project throughout the Peninsula and into other parts of the Hampton Roads Region
 
|chapter=Predictive Modeling
 
|supercluster=Public Safety
| supercluster           = Public Safety
|year=2016, 2017, 2018
| year                   = 2017
|title=StormSense - Tidal, Riverine, Inland, Surge Flood Management Program
 
}}
}}
[[Category:Year_2016]]
[[Category:Year_2018]]

Latest revision as of 05:53, January 25, 2023


StormSense
GCTC logo 344x80.png
Stormsense-logo.png
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