Ocean state rising: Storm simulation and vulnerability mapping to predict hurricane impacts for Rhode Island's critical infrastructure.

Predicting the consequences of a major coastal storm is increasingly difficult as the result of global climate change and growing societal dependence on critical infrastructure (CI). Past storms are no longer a reliable predictor of future weather events, and the traditional approach to vulnerability assessment presents accumulated loss in largely quantitative terms that lack the specificity local emergency managers need to develop effective plans and mitigation strategies. The Rhode Island Coastal Hazards Modeling and Prediction (RI-CHAMP) system is a geographic information system (GIS)-based modeling tool that combines high-resolution storm simulations with geolocated vulnerability data to predict specific consequences based on local concerns about impacts to CI. This case study discusses implementing RI-CHAMP for the State of Rhode Island to predict impacts of wind and inundation on its CI during a hurricane, tropical storm, or nor'easter. This paper addresses the collection and field verification of vulnerability data, along with RI-CHAMP's process for integrating those data with storm models. The project deeply engaged end-users (emergency managers, facility managers, and other stakeholders) in developing RI-CHAMP's ArcGIS Online dashboard to ensure it provides specific, actionable data. The results of real and synthetic storm models are presented along with discussion of how the data in these simulations are being used by state and local emergency managers, facility owners, and others.

The Hazard Consequence Prediction System: A Participatory Action Research Approach to Enhance Emergency Management.

Emergency managers (EMs) need nuanced data that contextualize the local-scale risks and impacts posed by major storm events (e.g. hurricanes and nor'easters). Traditional tools available to EMs, such as weather forecasts or storm surge predictions, do not provide actionable data regarding specific local concerns, such as access by emergency vehicles and potential communication disruptions. However, new storm models now have sufficient resolution to make informed emergency management at the local scale. This paper presents a Participatory Action Research (PAR) approach to capture critical infrastructure managers concerns about hurricanes and nor'easters in Providence, Rhode Island (USA). Using these data collection approach, concerns can be integrated into numerical storm models and used in emergency management to flag potential consequences in real time during the advance of a storm. This paper presents the methodology and results from a pilot project conducted for emergency managers and highlights implications for practice and future academic research.

A method for regional estimation of climate change exposure of coastal infrastructure: Case of USVI and the influence of digital elevation models on assessments.

OBJECTIVE: This study tests the impacts of Digital Elevation Model (DEM) data on an exposure assessment methodology developed to quantify flooding of coastal infrastructure from storms and sea level rise on a regional scale. The approach is piloted on the United States Virgin Islands (USVI) for a one-hundred-year storm event in 2050 under the IPCC's 8.5 emission scenario (RCP 8,5). METHOD: Flooding of individual infrastructure was tested against three different digital elevation models using a GIS-based coastal infrastructure database created specifically for the project using aerial images. Inundation for extreme sea levels is based on dynamic simulations using Lisflood-ACC (LFP). RESULTS: The model indicates transport and utility infrastructure in the USVI are considerably exposed to sea level rise and modeled storm impacts from climate change. Prediction of flood extent was improved with a neural network processed SRTM, versus publicly available SRTM (~30 m) seamless C-band DEM but both SRTM based models underestimate flooding compared to LIDAR DEM. The modeled scenario, although conservative, showed significant flood exposure to a large number of access roads to facilities, 113/176 transportation related buildings, and 29/66 electric utility and water treatment buildings including six electric power transformers and six waste water treatment clarifiers. CONCLUSION: The method bridges a gap between large-scale non-specific flood assessments and single-facility detailed assessments and can be used to efficiently quantify and prioritize parcels and large structures in need of further assessment for regions that lack detailed data to assess climate exposure to sea level rise and flooding caused by waves. The method should prove particularly useful for assessment of Small Island Developing State regions that lack LIDAR data, such as the Caribbean.

CO2 mitigation potential of mineral carbonation with industrial alkalinity sources in the United States.

The availability of industrial alkalinity sources is investigated to determine their potential for the simultaneous capture and sequestration of CO2 from point-source emissions in the United States. Industrial alkalinity sources investigated include fly ash, cement kiln dust, and iron and steel slag. Their feasibility for mineral carbonation is determined by their relative abundance for CO2 reactivity and their proximity to point-source CO2 emissions. In addition, the available aggregate markets are investigated as possible sinks for mineral carbonation products. We show that in the U.S., industrial alkaline byproducts have the potential to mitigate approximately 7.6 Mt CO2/yr, of which 7.0 Mt CO2/yr are CO2 captured through mineral carbonation and 0.6 Mt CO2/yr are CO2 emissions avoided through reuse as synthetic aggregate (replacing sand and gravel). The emission reductions represent a small share (i.e., 0.1%) of total U.S. CO2 emissions; however, industrial byproducts may represent comparatively low-cost methods for the advancement of mineral carbonation technologies, which may be extended to more abundant yet expensive natural alkalinity sources.