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There has been a growing interest in research on how to define and build indicators of resilience to address challenges associated with sea-level rise. Most of the proposed methods rely on lagging indicators constructed based on the historical performance of an infrastructure sub-system. These indicators are traditionally utilized to build curves that describe the past response of the sub-system to stressors; these curves are then used to predict the future resilience of the sub-system to hypothesized events. However, there is now a growing concern that this approach cannot provide the best insights for adaptive decision-making across the broader context of multiple sub-systems and stakeholders. As an alternative, leading indicators that are built on the structural characteristics that embody system resilience have been gaining in popularity. This structure-based approach can reveal problems and gaps in resilience planning and shed light on the effectiveness of potential adaptation activities. Here, we survey the relevant literature for these leading indicators within the context of sea-level rise and then synthesize the gained insights into a broader examination of the current research challenges. We propose research directions on leveraging leading indicators as effective instruments for incorporating resilience into integrated decision-making on the adaptation of infrastructure systems. 

Wave forcing from hurricanes, nor’easters, and energetic storms can cause erosion of the berm and beach face resulting in increased vulnerability of dunes and coastal infrastructure. LIDAR or other surveying techniques have quantified post-event morphology, but there is a lack of in situ hydrodynamic and morphodynamic measurements during extreme storm events. Two field studies were conducted in March 2018 and April 2019 at Bethany Beach, Delaware, where in situ hydrodynamic and morphodynamic measurements were made during a nor’easter (Nor’easter Riley) and an energetic storm (Easter Eve Storm). An array of sensors to measure water velocity, water depth, water elevation and bed elevation were mounted to scaffold pipes and deployed in a single cross-shore transect. Water velocity was measured using an electro-magnetic current meter while water and bed elevations were measured using an acoustic distance meter along with an algorithm to differentiate between the water and bed during swash processes. GPS profiles of the beach face were measured during every day-time low tide throughout the storm events. Both accretion and erosion were measured at different cross-shore positions and at different times during the storm events. Morphodynamic change along the back-beach was found to be related to berm erosion, suggesting an important morphologic feedback mechanism. Accumulated wave energy and wave energy flux per unit area between Nor’easter Riley and a recent mid-Atlantic hurricane (Hurricane Dorian) were calculated and compared. Coastal Observations: JALBTCX/NCMP emergency-response airborne Lidar coastal mapping & quick response data products for 2016/2017/2018 hurricane impact assessments 

There is a growing understanding that cross‐sector risks faced by critical infrastructure assets in natural disasters require a collaborative foresight from multiple disciplines. However, current contributions to infrastructure interdependency analysis remain centered in discipline‐specific methodologies often constrained by underlying theories and assumptions. This perspective article contributes to ongoing discussions about the uses, challenges, and opportunities provided by interdisciplinary research in critical infrastructure interdependency analysis. In doing so, several modes of integration of computational modeling with contributions from the social sciences and other disciplines are explored to advance knowledge that can improve the infrastructure system resilience under extreme events. Three basic modes of method integration are identified and discussed: (a) integrating engineering models and social science research, (b) engaging communities in participative and collaborative forms of social learning and problem solving using simulation models to facilitate synthesis, exploration, and evaluation of scenarios, and (c) developing interactive simulations where IT systems and humans act as “peers” leveraging the capacity of distributed networked platforms and human‐in‐the‐loop architectures for improving situational awareness, real‐time decision making, and response capabilities in natural disasters. Depending on the conceptualization of the issues under investigation, these broadly defined modes of integration can coalesce to address key issues in promoting interdisciplinary research by outlining potential areas of future inquiry that would be most beneficial to the critical infrastructure protection communities.

 

Hurricanes and extreme weather events can cause widespread damage and disruption to infrastructure services and consequently delay household and community recovery. A subset of data from a cross-sectional survey of 989 households in central and south Florida is used to examine the effects of Hurricane Irma on post-disaster recovery eight months after the landfall. Using logistic regression modeling, we find that physical damage to property, disruption of infrastructure services such as loss of electric power and cell phone/internet services and other factors (i.e., homeowner’s or renter’s insurance coverage, receiving disaster assistance and loss of income) are significant predictors of post-disaster recovery when controlling for age and race/ethnicity. 

Large-scale damage to the power infrastructure from hurricanes and high-wind events can have devastating ripple effects on infrastructure, the broader economy, households, com- munities, and regions. Using Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates between urban and rural counties; (2) the duration of electric power outages in counties exposed to tropical storm force winds versus hurricane Category 1 force winds; and (3) the rela- tionship between the duration of power outage and socioeconomic vulnerability. We used power outage data for the period September 9, 2017–September 29, 2017. At the peak of the power outages following Hurricane Irma, over 36% of all accounts in Florida were without electricity. We found that the rural counties, predominantly served by rural electric cooperatives and municipally owned utilities, experienced longer power outages and much slower and uneven restoration times. Results of three spatial lag models show that large percentages of customers served by rural electric cooperatives and municipally owned utilities were a strong predictor of the duration of extended power outages. There was also a strong positive association across all three models between power outage duration and urban/rural county designation. Finally, there is positive spatial dependence between power outages and several social vulnerability indicators. Three socioeconomic variables found to be statistically significant highlight three different aspects of vulnerability to power outages: minority groups, population with sensory, physical and mental disability, and economic vulnerability expressed as unemployment rate. The findings from our study have broader planning and policy relevance beyond our case study area, and highlight the need for additional research to deepen our understanding of how power restoration after hurricanes contributes to and is impacted by the socioeconomic vulnerabilities of communities.