Quantifying the effects of sea level rise driven marsh migration on wave attenuation.

Sea level rise (SLR) is the most significant climate change-related threat to coastal wetlands, driving major transformations in coastal regions through marsh migration. Landscape transformations due to marsh migration are manifested in terms of horizontal and vertical changes in land cover and elevation, respectively. These processes will have an impact on saltmarsh wave attenuation that is yet to be explored. This study stands as a comprehensive analysis of spatially distributed wave attenuation by vegetation in the context of a changing climate. Our results show that: i) changes in saltmarsh cover have little to no effect on the attenuation of floods, while ii) changes in elevation can significantly reduce flood extents and water depths; iii) overland wave heights are directly influenced by marsh migration, although iv) being indirectly attenuated by the water depth limiting effects of water depth attenuation driven by changes in elevation; v) the influence of saltmarsh accretion on wave attenuation is largely evident near the marsh edge, where the increasing elevations can drive major wave energy losses via wave breaking. Lastly, vi) considering the synergy between SLR, marsh migration, and changes in elevation results in significantly more wave attenuation than considering the eustatic effects of SLR and/or horizontal marsh migration alone, and therefore should be adopted in future studies.

Numerical modeling of wave attenuation: implications of representing vegetation found in coastal saltmarshes in the Chesapeake Bay.

Coastal communities are vulnerable to wave and storm surges during extreme events, highlighting the need to increase community resilience. The effectiveness of natural wetlands in attenuating waves is vital to designing strategies for protecting public safety. This study aimed to understand how vegetation attenuates waves and determine the best method for modeling vegetation's impact on wave dynamics. The researchers compared two different vegetation representations in numerical models, implicit and explicit, using SWAN and XBeach at varying spatial resolutions. The study focused on two marshes in the Chesapeake Bay, using field measurements to investigate the accuracy of each method in representing wave attenuation by vegetation and the implications of explicitly representing average characteristics of one vegetation species on a regional level. Results showed that explicit modeling using average vegetation characteristics provided more accurate results than the implicit model, which only showed wave attenuation due to topography. The finer scale resolution and site-specific vegetation characteristics further improved the accuracy of wave attenuation observed. Understanding the trade-offs between different vegetation representations in numerical models is essential to accurately represent wave attenuation and design effective protection strategies for coastal communities.

Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction.

A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood's intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA's Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.

Projecting the effects of land subsidence and sea level rise on storm surge flooding in Coastal North Carolina.

Much of the United States Atlantic coastline continues to undergo subsidence due to post glacial settlement and ground water depletion. Combined with eustatic sea level rise (SLR), this contributes to an increased rate of relative SLR. In this work, we utilize the ADvanced CIRCulation model to project storm surges across coastal North Carolina. Recent hurricanes Irene and Matthew are simulated considering SLR and subsidence estimates for 2100. Relative to present day conditions, storm surge susceptible regions increase by 27% (Irene) to 40% (Matthew) due to subsidence. Combined with SLR (+ 74 cm), results suggest more than a doubling of areal flood extent for Irene and more than a three-fold increase for Hurricane Matthew. Considering current regional population distributions, this translates to an increase in at-risk populations of 18% to 61% due to subsidence. Even further, exposed populations are projected to swell relative to Matthew and Irene baseline simulations (8200 and 28,500) by more than 70,000 in all SLR scenarios (79,400 to 133,600). While increases in surge inundation are driven primarily by SLR in the region, there remains a substantial contribution due to vertical land movement. This outlines the importance of exploring spatially variable land movement in surge prediction, independent of SLR.

Valuing natural habitats for enhancing coastal resilience: Wetlands reduce property damage from storm surge and sea level rise.

Storm surge and sea level rise (SLR) are affecting coastal communities, properties, and ecosystems. While coastal ecosystems, such as wetlands and marshes, have the capacity to reduce the impacts of storm surge and coastal flooding, the increasing rate of SLR can induce the transformation and migration of these natural habitats. In this study, we combined coastal storm surge modeling and economic analysis to evaluate the role of natural habitats in coastal flood protection. We focused on a selected cross-section of three coastal counties in New Jersey adjacent to the Jacques Cousteau National Estuarine Research Reserve (JCNERR) that is protected by wetlands and marshes. The coupled coastal hydrodynamic and wave models, ADCIRC+SWAN, were applied to simulate flooding from historical and synthetic storms in the Mid-Atlantic US for current and future SLR scenarios. The Sea Level Affecting Marshes Model (SLAMM) was used to project the potential migration and habitat transformation in coastal marshes due to SLR in the year 2050. Furthermore, a counterfactual land cover approach, in which marshes are converted to open water in the model, was implemented for each storm scenario in the present and the future to estimate the amount of flooding that is avoided due to the presence of natural habitats and the subsequent reduction in residential property damage. The results indicate that this salt marshes can reduce up to 14% of both the flood depth and property damage during relatively low intensity storm events, demonstrating the efficacy of natural flood protection for recurrent storm events. Monetarily, this translates to the avoidance of up to $13.1 and $32.1 million in residential property damage in the selected coastal counties during the '50-year storm' simulation and hurricane Sandy under current sea level conditions, and in the year '2050 SLR scenario', respectively. This research suggests that protecting and preserving natural habitats can contribute to enhance coastal resilience.

Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk.

In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind-related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.