[1] This paper investigates the effects of waves on storm surge, currents, and inundation in the Outer Banks and Chesapeake Bay during Hurricane Isabel in 2003 through detailed comparison between observed wind, wave, surge, and inundation data and results from an integrated storm surge modeling system, CH3D-SSMS. CH3D-SSMS, which includes coupled coastal and basin-scale storm surge and wave models, successfully simulated measured winds, waves, storm surge, currents, and inundation during Isabel. Comprehensive modeling and data analysis revealed noticeable effects of waves on storm surge, currents, and inundation. Among the processes that represent wave effects, radiation stress (outside the estuaries) and wave-induced stress (outside and inside the estuaries) are more important than wave-induced bottom stress in affecting the water level. Maximum surge was 3 m, while maximum wave height was 20 m offshore and 2.5 m inside the Chesapeake Bay, where the maximum wave-induced water level reached 1 m. Significant waves reached 3.5 m and 16 s at Duck Pier, North Carolina, and 1.6 m and 5 s at Gloucester, Virginia. At Duck, wave effects accounted for $36 cm or 20% of the peak surge elevation of 1.71 m. Inside the Chesapeake Bay, wave effects account for 5-10% of observed peak surge level. A two-layer flow is found at Kitty Hawk, North Carolina, during the peak of storm surge owing to the combined effects of wind and wave breaking.

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With recent advances in virtual computing and the revelation that compute-intensive tasks run well on system virtual machines (VMs), the ability to develop, deploy, and manage distributed systems has been ameliorated. This paper explores the design space of VM-based sandboxes where the following techniques that facilitate the deployment of secure nodes in Widearea Overlays of virtual Workstations (WOWs) are employed: DHCP-based virtual IP address allocation, self-configuring virtual networks supporting peer-to-peer NAT traversal, stacked file systems, and IPsec-based host authentication and end-to-end encryption of communication channels.Experiments with implementations of single-image VM sandboxes, which incorporate the above features and are easily deployable on hosted I/O VMMs, show execution time overheads of 10.6% or less for a batchoriented CPU-intensive benchmark.

To create more useful storm surge and inundation forecast products, probabilistic elements are being incorporated. To achieve the highest levels of confidence in these products, it is essential that as many simulations as possible are performed during the limited amount of time available. This paper develops a framework by which probabilistic storm surge and inundation forecasts within the Curvilinear Hydrodynamics in 3D (CH3D) Storm Surge Modeling System and the Southeastern Universities Research Association Coastal Ocean Observing and Prediction Program's forecasting systems are initiated with specific focus on the application of these methods in a limited-resource environment. Ensemble sets are created by dividing probability density functions (PDFs) of the National Hurricane Center model forecast error into bins, which are then grouped into priority levels (PLs) such that each subsequent level relies on results computed earlier and has an increasing confidence associated with it. The PDFs are then used to develop an ensemble of analytic wind and pressure fields for use by storm surge and inundation models. Using this approach applied with official National Hurricane Center (OFCL) forecast errors, an analysis of Hurricane Charley is performed. After first validating the simulation of storm surge, a series of ensemble simulations are performed representing the forecast errors for the 72-, 48-, 24-, and 12-h forecasts. Analysis of the aggregated products shows that PL4 (27 members) is sufficient to resolve 90% of the inundation within the domain and appears to represent the best balance between accuracy and timeliness of computed products for this case study. A 5-day forecast using the PL4 set is shown to complete in 83 min, while the intermediate PL2 and PL3 products, representing slightly less confidence, complete in 14 and 28 min, respectively.

6A prototype of an efficient and accurate rapid forecasting and mapping system (RFMS) of storm 7 surge is presented. Given a storm advisory from the National Hurricane Center, the RFMS can 8 generate a coastal inundation map on a high-resolution grid in one minute (reference system Intel 9 Core i7-3770K). The foundation of the RFMS is a storm surge database consisting of high-10 resolution simulations of 490 optimal storms generated by a robust storm surge modeling system, 11 CH3D (Curvilinear-grid Hydrodynamics in 3D) -SSMS. The RFMS uses an efficient Quick 12 Kriging interpolation scheme to interpolate the surge response from the storm surge database, 13 which considers tens of thousands of combinations of five landfall parameters of storms: central 14 pressure deficit, radius to maximum wind, forward speed, heading direction, and landfall location. 15 The RFMS is applied to Southwest Florida using data from Hurricane Charley in 2004, Hurricane 16 Irma in 2017, and to Florida Panhandle using data from Hurricane Michael in 2018 and validated 17 with observed high water mark data. The RFMS results agree well with observation and direct 18 simulation of the high-resolution CH3D-SSMS. The RFMS can be used for real-time forecasting 19 during a hurricane or "what-if" scenarios for mitigation planning and preparedness training, or to 20 produce a probabilistic flood map. The RFMS can provide more accurate surge prediction with 21 uncertainties if NHC can provide more accurate storm forecasts in the future. By incorporating 22 storms for future climate and sea level rise, the RFMS could be used to generate future flood maps 23 for coastal resilience and adaptation planning.24 25

Coastal communities in New Jersey (NJ), New York (NY), and Connecticut (CT) sustained huge structural loss during Sandy in 2012. We present a comprehensive science-based study to assess the role of coastal wetlands in buffering surge and wave in the tri-state by considering Sandy, a hypothetical Black Swan (BS) storm, and the 1% annual chance flood and wave event. Model simulations were conducted with and without existing coastal wetlands, using a dynamically coupled surge-wave model with two types of coastal wetlands. Simulated surge and wave for Sandy were verified with data at numerous stations. Structural loss estimated using real property data and latest damage functions agreed well with loss payout data. Results show that, on zip-code scale, the relative structural loss varies significantly with the percent wetland cover, the at-risk structural value, and the average wave crest height. Reduction in structural loss by coastal wetlands was low in Sandy, modest in the BS storm, and significant in the 1% annual chance flood and wave event. NJ wetlands helped to avoid 8%, 26%, 52% loss during Sandy, BS storm, and 1% event, respectively. This regression model can be used for wetland restoration planning to further reduce structural loss in coastal communities.

The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100-200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.

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