Prediction System

Background

The prediction system is based on the ADvanced CIRCulation (ADCIRC) modeling platform for storm surge/coastal flooding predictions widely used across academia, goverment agencies and the private sectorIt includes critical physics (e.g., high resolution representation of bathymetry and topography via unstructured meshes; spatially varying land cover; coupled waves, surge, tides and runoff; multiple metorological model forcings), accurate numerics, optimization for high performance computing and an active community that continues to advance the model’s capabilities. 

As part of the ongoing effort funded by the DHS Coastal Resilience Center, the ADCIRC model grid has been reconfigured with high resolution for the first time in Southern New England, including the Narragansett Bay and RI coastal waters. The grid is highly refined in order to properly resolve the complicated coastal geometry of the Southern New England coast. The computational domain boundaries over land are reconfigured to allow river inflows from the major rivers for combined inland and coastal flood modeling (Ullman et al. 2019).

URI researchers use a secure database and dashboard to allow RI-CHAMP users an interface between the following three components:

  1. The Esri ArcGIS Enterprise GIS system used in many state and local Emergency Operations Centers (EOCs)
  2. Hydrodynamic storm model outputs (from ADCIRC)
  3. Local qualitative hazard impact-related data collected in advance of a storm  

Figure 1: The prediction system indexes data on the infrastructure consequence thresholds collected from local facility and emergency managers, combined directly into storm models, and provides a visualization and substantive information about potential consequences for infrastructure as a storm advances toward the region.

Background

The prediction system is based on the ADvanced CIRCulation (ADCIRC) modeling platform for storm surge/coastal flooding predictions widely used across academia, goverment agencies and the private sectorIt includes critical physics (e.g., high resolution representation of bathymetry and topography via unstructured meshes; spatially varying land cover; coupled waves, surge, tides and runoff; multiple metorological model forcings), accurate numerics, optimization for high performance computing and an active community that continues to advance the model’s capabilities. 

As part of the ongoing effort funded by the DHS Coastal Resilience Center, the ADCIRC model grid has been reconfigured with high resolution for the first time in Southern New England, including the Narragansett Bay and RI coastal waters. The grid is highly refined in order to properly resolve the complicated coastal geometry of the Southern New England coast. The computational domain boundaries over land are reconfigured to allow river inflows from the major rivers for combined inland and coastal flood modeling (Ullman et al. 2019).

URI researchers use a secure database and dashboard to allow RI-CHAMP users an interface between the following three components:

  1. The Esri ArcGIS Enterprise GIS system used in many state and local Emergency Operations Centers (EOCs)
  2. Hydrodynamic storm model outputs (from ADCIRC)
  3. Local qualitative hazard impact-related data collected in advance of a storm  

Figure 1: The prediction system indexes data on the infrastructure consequence thresholds collected from local facility and emergency managers, combined directly into storm models, and provides a visualization and substantive information about potential consequences for infrastructure as a storm advances toward the region.

Database

The database can be customized for specific end users, displaying information relevant for the most relevant users or infrastructure sectors. Additionally, the database supports tiered user security access based on requirements of facility managers to ensure adequate protection of sensitive critical infrastructure information. Storm model outputs can be regularly updated in the dashboard to provide emergency managers with the most up-to-date storm conditions. The system can integrate with the outputs from modeled synthetic storm scenarios as well as real-time storm modeling used by emergency managers in their emergency operation centers.

Dashboard

RI-CHAMP’s online dashboard organizes storm consequence predictions as a time series, allowing users to scroll through a storm timeline and anticipate when specific impacts will need to be addressed. For example, the system will be able to not only project the time during a storm that a critical backup generator may be flooded, but will also predict the consequences of the resulting power loss on dependent infrastructure.

Predictions are automatically revised each time RI-CHAMP processes an updated storm forecast. Data displayed on the RI-CHAMP’s dashboard is automatically filtered based on a user’s system role and permissions. Entries can also be filtered based on critical infrastructure sector and other criteria. See a demo in this video.

Database

The database can be customized for specific end users, displaying information relevant for the most relevant users or infrastructure sectors. Additionally, the database supports tiered user security access based on requirements of facility managers to ensure adequate protection of sensitive critical infrastructure information. Storm model outputs can be regularly updated in the dashboard to provide emergency managers with the most up-to-date storm conditions. The system can integrate with the outputs from modeled synthetic storm scenarios as well as real-time storm modeling used by emergency managers in their emergency operation centers.

Dashboard

RI-CHAMP’s online dashboard organizes storm consequence predictions as a time series, allowing users to scroll through a storm timeline and anticipate when specific impacts will need to be addressed. For example, the system will be able to not only project the time during a storm that a critical backup generator may be flooded, but will also predict the consequences of the resulting power loss on dependent infrastructure.

Predictions are automatically revised each time RI-CHAMP processes an updated storm forecast. Data displayed on the RI-CHAMP’s dashboard is automatically filtered based on a user’s system role and permissions. Entries can also be filtered based on critical infrastructure sector and other criteria. See a demo in this video.

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Case Studies

Westerly
case study
Research in Westerly identified more
than 100 "consequence thresholds"
resulting from impacts to 11 critical infrastructure facilities in the floodplain.

Click here to learn more.
Providence
case study
Research in Providence
identified approximately 300
"consequence thresholds"
resulting from impacts to
about 100 assets across
the 45 critical infrastructure facilities
in the floodplain.
Naval Station Newport
on Aquidneck Island
case study
Our latest case study focuses on
"A hazard resilient future for Naval Station Newport within its coastal
Community: Military installation resilience
review for short-term preparedness
and long-term planning."
Wastewater Treatment Facilities
case study
In this case, a customized planning tool was developed to help the Rhode Island Department of Emergency Management (RI DEM) plan for the 19 major wastewater treatment facilities that it regulates.
Westerly
case study
Research in Westerly identified more than 100 "consequence thresholds" resulting
from impacts to
11 critical infrastructure
facilities in the floodplain.
Providence
case study
Research in Providence
identified approximately 300
"consequence thresholds" resulting from
impacts to about
100 assets across
the 45 critical
infrastructure facilities
in the floodplain.
Naval Station Newport
case study
Our latest case study focuses on
"A hazard resilient future for Naval Station Newport
within its coastal
Community: Military installation resilience review for short-term preparedness and long-term planning."
Wastewater Treatment Facilities
case study
In this case, a customized planning tool was developed
to help the Rhode Island Department of Emergency Management (RI DEM) plan
for the 19 major wastewater treatment facilities that it regulates.
Westerly
case study
Research in Westerly identified more than
100 "consequence thresholds"
resulting from impacts to 11 critical
infrastructure facilities in the floodplain.
Providence
case study
Research in Providence identified
approximately 300 "consequence thresholds" resulting from impacts to about
100 assets across the 45 critical
infrastructure facilities in the floodplain.
Naval Station
Newport
case study
Our latest case study focuses on "a
hazard resilient future for Naval Station
Newport within its coastal community: 
Military installation resilience review
for short-term preparedness and
long-term planning."
Wastewater Treatment
Facilities
case study
In this case, a customized planning tool was developed to help the Rhode Island
Department of Emergency
Management (RI DEM) plan for
the 19 major wastewater treatment
facilities that it regulates.
Westerly
case study
Research in Westerly identified more than 100
"consequence thresholds" resulting from impacts to
11 critical infrastructure
facilities in the floodplain.
Providence
case study
Research in Providence identified approximately 300 "consequence thresholds" resulting from impacts
to about 100 assets
across the 45 critical
infrastructure facilities in the floodplain.
Naval Station
Newport
case study
Our latest case study focuses on "a hazard resilient future for Naval Station Newport
within its coastal community: Military
installation resilience review for short-term preparedness and long-term planning."
Wastewater Treatment
Facilities
case study
In this case, a customized planning tool was developed
to help the Rhode Island
Department of
Emergency Management (RI DEM) plan for
the 19 major wastewater treatment facilities that it regulates.