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This paper describes a probabilistic approach for mapping of coastal flood hazards associated with sea-level rise and storm intensification toward the end of the 21st century. Under the Representative Concentration Pathway (RCP) 8.5, the Coupled Model Intercomparison Project Phase 5 (CMIP5) predicts a 0.6-m ensemble mean of sea-level rise for the Central Pacific from the 1986–2005 to 2081–2100 epochs. Fifty downscaling simulations of the 2080–2099 period from the CMIP5 NCAR-CCSM4 model produce 2492 hurricanes around the Hawaiian Islands. In comparison with a control dataset for the 1980–1999 period, the simulated future hurricanes show a slight increase in number and a northward shift of the tracks toward the Hawaiian Islands. There are 627 hurricanes in the 2080–2099 dataset with potential impact on Oahu, and the top 24 storms selected by wind speed at the urban Honolulu coast define a scenario set for inundation mapping. A suite of spectral wave, circulation, and Boussinesq models in a nested grid system describes generation and propagation of surge and waves across the ocean as well as wave setup and runup at the coast. The interoperable package includes phase-averaged and phase-resolving processes to determine the coastal flood hazards over a range of spatial and temporal scales during a hurricane event. Since the simulated dataset corresponds to a quasi 1000-year period, barring the tail end of the distribution, the suite of inundation scenarios enables definition of flood hazard maps with return periods of up to 500 years or annual exceedance probabilities of 0.2% or greater for climate change adaptation. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines adaptation to current storm surge flood risk as one indicator of anticipatory adaptation to climate change impacts. Focusing on community businesses, a web-based survey obtained information on hurricane preparedness and risk perceptions in Sarasota County, Florida. A model of current precautionary action that utilized previously identified business characteristics predictive of business preparedness and post-disaster recovery, a spatial variable indicating storm surge exposure, and items measuring the risk perception and information-seeking behavior of business owners explained a majority of the variance in current levels of preparedness. Use of online risk information sources and the geographic distribution of customers and were the strongest predictors of preparedness. Results indicate that the adaptive capacity of businesses may vary significantly with types of businesses, locations, and socio-cognitive characteristics of business owners. To encourage overall community resilience, risk communication efforts should be targeted based on specific elements that predict business preparedness. [Copyright &y& Elsevier]

Abstract: This paper describes the impacts of sea surface temperature (SST) rise and sea-level rise (SLR) on cyclonic storm surge flooding in western Bangladesh. A calibrated numerical hydrodynamic model was used to simulate surge wave propagation through the rivers and overland flooding. The model was calibrated with base condition (present climate), and then eight flooding scenarios of plausible future conditions were assessed by considering increased surge heights. Flooded area, flooding depth and surge intrusion length were computed by superimposing the predicted maximum water level information on a digital elevation model (DEM). This analysis showed that for a storm surge under 2°C SST rise and 0.3m SLR, flood risk area would be 15.3% greater than the present risk area and depth of flooding would increase by as much as 22.7% within 20km from the coastline. Within the risk area, the study identified 5690km2 land (22% of exposed coast) as a high-risk zone (HRZ) where flooding of depth 1m or more might occur, and people should move to nearby cyclone shelters during extreme cyclonic events. Predicted area of HRZ is 1.26 times the currently demarcated HRZ. It was estimated that 320 additional shelters are required to accommodate people in the newly identified HRZ. This information would be of value to policy and decision makers for future shelter planning and designing shelter heights. [Copyright &y& Elsevier]