Background
The prediction system is based on the ADvanced CIRCulation (ADCIRC) modeling platform for storm surge/coastal flooding predictions widely used across academia, government agencies and the private sector. ADCIRC includes critical physics, accurate numerical implementation, and optimization for high performance computing environments, critical for real time forecasting. The model features (e.g., high resolution unstructured mesh; spatially varying attributes; wave coupling, numerous meteorological forcing options) and the active developer and user community continue 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:
- The Esri ArcGIS Enterprise GIS system used in many state and local Emergency Operations Centers (EOCs)
- Hydrodynamic storm model outputs (from ADCIRC)
- 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:
- The Esri ArcGIS Enterprise GIS system used in many state and local Emergency Operations Centers (EOCs)
- Hydrodynamic storm model outputs (from ADCIRC)
- 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.
