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Accelerated sea level rise has resulted in recurrent flooding in coastal regions, increasingly impacting both transportation systems and local populations. Using the Hampton Roads region in Virginia as a case study, this study a. identifies hotspots with frequent, significant accessibility reduction for work and nonwork travel utilizing crowdsourced WAZE flood report data during the month of August over 5 years: 2018 to 2022; and b. examines the shifts in social vulnerability in populations residing in these hotspots over the 5 year period using 2016 and 2021 American Community Survey data. Results show that approximately 12 percent and 3 percent of the population of the region reside in hotspots experiencing significant recurrent flooding-induced accessibility reduction for work and nonwork trips. Social vulnerability analysis revealed that populations with greater socioeconomic and transportation vulnerabilities are more susceptible to recurrent flooding induced accessibility impacts in terms of both extent and frequency. Furthermore, a comparison of social vulnerability indices between 2016 and 2021 shows an increasing trend of social vulnerability for highly impacted zones, with low income, disabled, and households with young children having restricted ability to relocate from these zones. The findings reinforce the necessity for spatially and temporally disaggregated studies of climate event impacts. Furthermore, the longer term population trends highlight the importance of dynamic assessment of climate event impacts at different time scales.

Due to accelerated sea level rise and climate change, the transportation system is increasingly affected by recurrent flooding coastal regions, yet the cumulative travel disruption effects are not well understood. In Part 1 of this study, the accessibility impacts of recurrent flooding on the auto mode were examined. In this paper (Part 2 of the study), the impact of recurrent flooding on transit service accessibility was quantified with the aid of spatially and temporally disaggregated crowdsourced flood incident data from WAZE. A fixed route transit network is built for five time of day periods for 710 traffic analysis zones (TAZs), to capture the spatial and temporal variation of transit accessibility reduction due to recurrent flooding. Results show that the greatest transit accessibility reduction occurs during the morning peak hour, with individual TAZ transit accessibility reduction ranging from 0 to 88.2% for work trips (with an average of 6.4%) and ranging from 0 to 99.9% for non-work trips (with an average of 3.7%). Furthermore, social vulnerability analysis indicates that TAZs with a greater share of people with higher vulnerability in transportation and socioeconomic status are more likely to experience recurrent flooding-induced transit accessibility reduction. Results from this study reinforce the notion that transportation impacts under recurrent flooding are not uniformly experienced throughout a region, and this spatial and temporal variation translates to different impacts borne by various population groups. Disaggregate impact analysis like this study can support transportation engineers and planners to prioritize resources to ensure equitable transit accessibility under increasing climate disruptions., Comment: Under review of the Journal of Transport Geography

Recurrent flooding has increased rapidly in coastal regions due to sea level rise and climate change. A key metric for evaluating transportation system degradation is accessibility, yet the lack of temporally and spatially disaggregate data means that the impact of recurrent flooding on accessibility, and hence transportation system performance: is not well understood. Using crowdsourced WAZE flood incident data from the Hampton Roads region in Virginia, this study (Part 1) examines changes in the roadway network accessibility for travelers residing in 1,113 traffic analysis zones (TAZs) across five time of day periods. Additionally, a social vulnerability index framework is developed to understand the socioeconomic characteristics of TAZs that experience high accessibility reduction under recurrent flooding. Results show that TAZs experience the most accessibility reduction under recurrent flooding during the morning peak period (6 to 9am) with large differences across different zones, ranging from 0 to 49.6 (percentage) for work trips (with population weighted mean reduction of 1.71 percent) and 0 to 87.9 (percentage) for nonwork trips (with population weighted mean reduction of 0.81 percent). Furthermore, the social vulnerability analysis showed that zones with higher percentages of lower socioeconomic status, unemployed, less educated, and limited English proficiency residents experience greater accessibility reduction for work trips. In contrast to previous studies that aggregate the effects of recurrent flooding across a city, these results demonstrate that there exists large spatial and temporal variation in recurrent floodings impacts on accessibility. This study also highlights the need to include social vulnerability analysis in assessing impacts of climate events, to ensure equitable outcomes as investments are made to create resilient transportation infrastructure., Comment: Under review of the Journal of Transport Geography

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This investigation underscores the importance of using a model architecture that supports the communication of prediction uncertainty and the effective integration of relevant, multi-modal features., Comment: 10 pages, 8 figures

Recent accessibility of affordable sensing technologies, microcontrollers, and wireless communication technology has made it possible for stormwater systems to be retrofitted with an assortment of sensors and actuators. These smart stormwater systems have enabled the real-time sensing of their surrounding environmental dynamics, and subsequently, provide the basis for autonomous and adaptive operational control strategies. Additionally, these systems allow for inexpensive and minimally-invasive stormwater control interventions (e.g. hydraulic valve operated by cellularly-connected actuator) in lieu of new construction. However promising this area of smart stormwater control, there still remain barriers -- for experts and novices alike -- to access a set of shared tools and methods for investigating, developing, and contributing to it. In an effort to make smart stormwater control research more methodical, efficient, and accessible, we present pystorms, a Python-based simulation sandbox that facilitates the quantitative evaluation and comparison of control strategies. The pystorms simulation sandbox comes with (i) a collection of real world-inspired stormwater control scenarios on which any number of control strategies can be applied and tested via (ii) an accompanying Python programming interface coupled with a stormwater simulator. For the first time, pystorms enables rigorous and efficient evaluation of smart stormwater control methodologies across a diverse set of watersheds with only a few lines of code. We present the details of pystorms here and demonstrate its usage by applying and evaluating two stormwater control strategies.

Low-lying coastal cities across the world are vulnerable to the combined impact of rainfall and storm tide. However, existing approaches lack the ability to model the combined effect of these flood mechanisms. Thus, to increase flood resilience, modeling techniques to improve understanding and prediction of the combined effect of these flood hazards are critical. To address this need, this study presents a modeling system for assessing the combined flood risk to coastal cities under changing climate conditions that leverages ocean modeling with land surface modeling capable of resolving urban drainage infrastructure within the city. The modeling approach is demonstrated in quantifying the future impact on transportation infrastructure within Norfolk, Virginia USA. A series of combined storms events are modeled for current (2020) and projected future (2070) climate conditions. Results show that pluvial flooding causes a larger interruption to the transportation network compared to tidal flooding under current climate conditions. By 2070, however, tidal flooding will be the dominant flooding mechanism with even nuisance flooding expected to happen daily due to SLR. In 2070, nuisance flooding is expected to cause a 4.6% total link close time (TLC), which is more than two times that of a 50-year storm surge (1.8% TLC) in 2020. The coupled model was compared with a widely used but physically simplistic bathtub method to assess the difference resulting from the more complex modeling presented. Results show that the bathtub method overestimated the flooded area near the shoreline by 9.5% and 3.1% for a 10-year storm surge event in 2020 and 2070, respectively, but underestimated flooded area in the inland region by 9.0% and 4.0% for the same events. The findings demonstrate the benefit of sophisticated modeling methods in climate adaptive planning and policy in coastal communities.