An official website of the United States government
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, especially under climate change and sea level rise (SLR). Thus, to increase flood resilience of coastal cities, modeling techniques to improve the 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 impact on coastal cities under selected future climate scenarios 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 impact of possible future climate scenarios on transportation infrastructure within Norfolk, Virginia, USA. A series of combined storm events are modeled for current (2020) and projected future (2070) climate scenarios. The 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 flood model was compared with a widely used but physically simplistic bathtub method to assess the difference resulting from the more complex modeling presented in this study. The 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 the 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 compared to more simplistic bathtub approaches, in climate adaptive planning and policy in coastal communities.
more »
« less
Estuarine response to storm surge and sea-level rise associated with channel deepening: a flood vulnerability assessment of southwest Louisiana, USA
Abstract This study investigates the sensitivity of the Calcasieu Lake estuarine region to channel deepening in southwest Louisiana in the USA. We test the hypothesis that the depth increase in a navigational channel in an estuarine region results in the amplification of the inland penetration of storm surge, thereby increasing the flood vulnerability of the region. We run numerical experiments using the Delft3D modeling suite (validated with observational data) with different historic channel depth scenarios. Model results show that channel deepening facilitates increased water movement into the lake–estuary system during a storm surge event. The inland peak water level increases by 37% in the presence of the deepest channel. Moreover, the peak volumetric flow rate increases by 291.6% along the navigational channel. Furthermore, the tidal prism and the volume of surge prism passing through the channel inlet increase by 487% and 153.3%, respectively. In our study, the presence of the deepest channel results in extra 56.72 km2of flooded area (approximately 12% increase) which is an indication that channel deepening over the years has rendered the region more vulnerable to hurricane-induced flooding. The study also analyzes the impact of channel deepening on storm surge in estuaries under different future sea-level rise (SLR) scenarios. Simulations suggest that even the most conservative scenario of SLR will cause an approximately 51% increase in flooded area in the presence of the deepest ship channel, thereby suggesting that rising sea level will cause increased surge penetration and increased flood risk.
more »
« less
- Award ID(s):
- 2139882
- PAR ID:
- 10398170
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Natural Hazards
- Volume:
- 116
- Issue:
- 3
- ISSN:
- 0921-030X
- Page Range / eLocation ID:
- p. 3879-3897
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storms of interest and assign a probability to its likelihood. The framework leverages the Joint Probability Method with Response Surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: One is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise.more » « less
-
Abstract Exposure to sea-level rise (SLR) and flooding will make some areas uninhabitable, and the increased demand for housing in safer areas may cause displacement through economic pressures. Anticipating such direct and indirect impacts of SLR is important for equitable adaptation policies. Here we build upon recent advances in flood exposure modeling and social vulnerability assessment to demonstrate a framework for estimating the direct and indirect impacts of SLR on mobility. Using two spatially distributed indicators of vulnerability and exposure, four specific modes of climate mobility are characterized: (1) minimally exposed to SLR (Stable), (2) directly exposed to SLR with capacity to relocate (Migrating), (3) indirectly exposed to SLR through economic pressures (Displaced), and (4) directly exposed to SLR without capacity to relocate (Trapped). We explore these dynamics within Miami-Dade County, USA, a metropolitan region with substantial social inequality and SLR exposure. Social vulnerability is estimated by cluster analysis using 13 social indicators at the census tract scale. Exposure is estimated under increasing SLR using a 1.5 m resolution compound flood hazard model accounting for inundation from high tides and rising groundwater and flooding from extreme precipitation and storm surge. Social vulnerability and exposure are intersected at the scale of residential buildings where exposed population is estimated by dasymetric methods. Under 1 m SLR, 56% of residents in areas of low flood hazard may experience displacement, whereas 26% of the population risks being trapped (19%) in or migrating (7%) from areas of high flood hazard, and concerns of depopulation and fiscal stress increase within at least 9 municipalities where 50% or more of their total population is exposed to flooding. As SLR increases from 1 to 2 m, the dominant flood driver shifts from precipitation to inundation, with population exposed to inundation rising from 2.8% to 54.7%. Understanding shifting geographies of flood risks and the potential for different modes of climate mobility can enable adaptation planning across household-to-regional scales.more » « less
-
null (Ed.)Gated storm surge barriers are being studied by the United States Army Corps of Engineers (USACE) for coastal storm risk management for the New York City metropolitan area. Surge barrier gates are only closed when storm tides exceeding a specific “trigger” water level might occur in a storm. Gate closure frequency and duration both strongly influence the physical and environmental effects on enclosed estuaries. In this paper, we use historical observations to represent future storm tide hazard, and we superimpose local relative sea-level rise (SLR) to study the potential future changes to closure frequency and duration. We account for the effects of forecast uncertainty on closures, using a relationship between past storm surge and forecast uncertainty from an operational ensemble forecast system. A concern during a storm surge is that closed gates will trap river streamflow and could cause a new problem with trapped river water flooding. Similarly, we evaluate this possibility using historical data to represent river flood hazard, complemented by hydrodynamic model simulations to capture how waters rise when a hypothetical barrier is closed. The results show that SLR causes an exponential increase of the gate closure frequency, a lengthening of the closure duration, and a rising probability of trapped river water flooding. The USACE has proposed to prevent these SLR-driven increases by periodically raising the trigger water level (e.g., to match a prescribed storm return period). However, this alternative management approach for dealing with SLR requires waterfront seawalls to be raised at a high, and ongoing, additional future expense. For seawalls, costs and benefits will likely need to be weighed on a neighborhood-by-neighborhood basis, and in some cases retreat or other non-structural options may be preferable.more » « less
-
Abstract Accurate delineation of compound flood hazard requires joint simulation of rainfall‐runoff and storm surges within high‐resolution flood models, which may be computationally expensive. There is a need for supplementing physical models with efficient, probabilistic methodologies for compound flood hazard assessment that can be applied under a range of climate and environment conditions. Here we propose an extension to the joint probability optimal sampling method (JPM‐OS), which has been widely used for storm surge assessment, and apply it for rainfall‐surge compound hazard assessment under climate change at the catchment‐scale. We utilize thousands of synthetic tropical cyclones (TCs) and physics‐based models to characterize storm surge and rainfall hazards at the coast. Then we implement a Bayesian quadrature optimization approach (JPM‐OS‐BQ) to select a small number (∼100) of storms, which are simulated within a high‐resolution flood model to characterize the compound flood hazard. We show that the limited JPM‐OS‐BQ simulations can capture historical flood return levels within 0.25 m compared to a high‐fidelity Monte Carlo approach. We find that the combined impact of 2100 sea‐level rise (SLR) and TC climatology changes on flood hazard change in the Cape Fear Estuary, NC will increase the 100‐year flood extent by 27% and increase inundation volume by 62%. Moreover, we show that probabilistic incorporation of SLR in the JPM‐OS‐BQ framework leads to different 100‐year flood maps compared to using a single mean SLR projection. Our framework can be applied to catchments across the United States Atlantic and Gulf coasts under a variety of climate and environment scenarios.more » « less
