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The intensity of an atmospheric river (AR) is only one of the factors influencing the damage it will cause. We use random forest models fit to hazard, exposure, and vulnerability data at different spatial and temporal scales in California to predict the probability that a given AR event will cause flood damage, as measured by National Flood Insurance Program (NFIP) claims. We first demonstrate the usefulness of data-driven models and interpretable machine learning to identify and describe drivers of AR flood damage. Hazard features, particularly measures of AR intensity such as total precipitation, increase the probability of damage with increasing values up to a threshold point, after which the probability of damage saturates. Although hazard is generally the most important risk dimension across all models, exposure and vulnerability contribute up to a third of the explanatory power. Exposure and variability features generally increase the probability of damage with increasing values, apart from a few instances which can be explained by physical intuition, but tend to affect the probability of damage less for the largest AR events. Comparisons between random forest models at different spatial and temporal scales showed general agreement. We then examine limitations inherent in publicly available exposure, vulnerability, and loss data, focusing on the difference in temporal resolution between variables from different risk dimensions and discrepancies between NFIP claims and total flood losses, and describe how those limitations may affect the model results. Overall, the application of interpretable machine learning to understand the contributions of exposure and vulnerability to AR-driven flood risk has identified potential community risk drivers and strategies for resilience, but the results must be considered in the context of the data that produced them. [ABSTRACT FROM AUTHOR]

Total water levels (TWLs), including the contribution of wind waves, associated with tropical cyclones (TCs) are among the most damaging hazards faced by coastal communities. TC-induced economic losses are expected to increase because of stronger TC intensity, sea level rise, and increased populations along the coasts. TC intensity, translation speed, and distance to the coast affect the magnitude and duration of increased TWLs and wind waves. Under climate change, the proportion of high-intensity TCs is projected to increase globally, whereas the variation pattern of TC translation speed also depends on the ocean basin and latitude. There is an urgent need to improve our understanding of the linkages between TC characteristics and TWL components. In the past few years, hurricanes Matthew (2016), Dorian (2019), and Isaias (2020) propagated over the South Atlantic Bight (SAB) with similar paths but resulted in different coastal impacts. We combined in situ observations and numerical simulations with the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to analyze the extreme TWLs under the three TCs. Model verification showed that the TWL components were well reproduced by the present model setup. Our results showed that the peak storm surge and the peak wave runup depended mainly on the TC intensity, the distance to the TC eye, and the TC heading direction. A decrease in TC translation speed primarily led to longer exceedance durations of TWLs, which may result in more severe economic losses. Wave-dependent water level components (i.e., wave setup and wave swash) were found to dominate the peak TWL within the near-TC field. Our results also showed that in specific conditions, the prestorm wave runup associated with the TC-induced swell may lead to TWLs higher than at the peak of the storm. This was the case along the SAB during Hurricane Isaias. Isaias's fast TC translation speed and the fact that its swell was not blocked by any islands were the main factors contributing to these peak TWLs ahead of the storm peak. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are a class of meteorologic phenomena that cause significant precipitation and flooding on the US West Coast. This work presents a new Performance-based Atmospheric River Risk Analysis (PARRA) framework that adapts existing concepts from probabilistic risk analysis and performance-based engineering for application in the context of AR-driven fluvial flooding. The PARRA framework is a chain of physically based models that link the atmospheric forcings, hydrologic impacts, and economic consequences of AR-driven fluvial flood risk together at consistent "pinch point" variables. Organizing around these pinch points makes the framework modular, in that models between pinch points can be updated without affecting the rest of the model chain, and it produces a probabilistic result that quantifies the uncertainty in the underlying system states. The PARRA framework can produce results beyond analyses of individual scenario events and can look towards prospective assessment of events or system changes that have not been seen in the historic record. The utility of the PARRA framework is demonstrated through a series of analyses in Sonoma County, California. Evaluation of a February 2019 case study AR event shows that the individual component models produce simulated distributions that capture the observed precipitation, streamflow, inundation, and damage. The component models are then run in sequence to generate a first-of-its-kind AR flood loss exceedance curve for Sonoma County. The prospective capabilities of the PARRA framework are presented through the evaluation of a hypothetical mitigation action. It was found elevating 150 homes, selected based on their proximity to the Russian River, was able to reduce the average annual loss by half. The loss results from the mitigated building portfolio are compared against the original case. While expected benefits were minimal for the smallest events, the larger, more damaging ARs were expected to see loss reductions of approximately $50 million per event. These results indicate the potential of the PARRA framework for examining other changes to flood risk at the community level, including future changes to the hazard, through climate change; exposure, through development; and/or vulnerability, through flood mitigation investments. [ABSTRACT FROM AUTHOR]

Four profiles were measured on two barrier beaches in Groton, Connecticut to compare the affects of storms, seasonality and time on beach erosion for the unconstrained versus the seawall-constrained systems. Paired sets of profiles at the two beaches were collected with autolevels and tape measures on ten different days in 2003 and 2007 during the spring, late summer and fall seasons. Limited sediment sampling was also conducted to evaluate particle-size variations at the two sites. The two transects at the unconstrained beach follow a typical cut and fill seasonal cycle, display removal of a berm from higher wave energies present during storms, and show net accretion over the four year period. Two transects beginning at the beach with the seawall display little change in beach morphology in response to changes in seasons and storm activity, and exhibit considerable variability in long-term accretion and erosion. Although different degrees of recovery from previous storm erosion create challenges in predicting beach behavior in the long term, there is evidence of reduced sand availability on the beach constrained by the seawall compared to the unconstrained beach over the four-year period. [ABSTRACT FROM AUTHOR]