An official website of the United States government
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
Dynamic Modeling of Inland Flooding and Storm Surge on Coastal Cities under Climate Change Scenarios: Transportation Infrastructure Impacts in Norfolk, Virginia USA as a Case Study
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
- PAR ID:
- 10339200
- Date Published:
- Journal Name:
- Geosciences
- Volume:
- 12
- Issue:
- 6
- ISSN:
- 2076-3263
- Page Range / eLocation ID:
- 224
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Coastal flooding from tropical cyclone (TC)‐induced storm surges is among the most devastating natural hazards in the US. Accurately quantifying storm surge hazards is crucial for risk mitigation and climate adaptation. In this study, we conduct climatology‐hydrodynamic modeling to estimate TC surge hazards along the US northeast coastline under future climate scenarios. In this methodology, we generate synthetic TCs for the northeastern US to drive a hydrodynamic model (ADCIRC) to simulate storm surges. Observing their significant effect on storm surge, for the first time, we bias‐correct landfall angles of synthetic TCs, in addition to bias‐correcting their frequency and intensity. Our findings show that under the combined effects of sea level rise (SLR) and TC climatology change, historical 100‐year extreme water levels (EWLs) along the US northeast coastline would occur annually at the end of the century in both SSP2‐4.5 and SSP5‐8.5 emissions scenarios. 500‐year EWLs are also projected to occur every 1–60 (1–20) years under SSP2‐4.5 (SSP5‐8.5). SLR is the dominant factor in the dramatic changes in the EWLs. However, while in higher latitudes () TC climatology change modestly affect EWLs ( contribution for 100‐year and for 500‐year EWL changes), in lower latitudes the impact is more significant (up to 40% contribution to 100‐year and 55% for 500‐year EWL changes). Extending previous methods, the physics‐based probabilistic framework presented here can be applied to project future coastal flood hazards under the effects of SLR and storm climatology change for any TC‐prone region.more » « less
-
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 Sea‐level rise is leading to increasingly frequent coastal floods globally. Recent research shows that changes in tidal properties and storm surge magnitudes can further exacerbate sea‐level rise‐related increases in flood frequencies. However, such non‐stationarity in tide and storm surge statistics are largely neglected in existing coastal flood projection methodologies. Here we develop a framework to explore the effect that different realizations of various sources of uncertainty have on projections of coastal flood frequencies, including changes in tidal range and storminess. Our projection methodology captures how observed flood rates depend on how storm surges coincide with tidal extremes. We show that higher flood rates and earlier emergence of chronic flooding are associated with larger sea‐level rise rates, lower flood thresholds, and increases in tidal range and skew surge magnitudes. Smaller sea‐level rise rates, higher flood thresholds and decreases in sea level variability lead to commensurately lower flood rates. Percentagewise, changes in tidal amplitudes generally have a much larger impact on flood frequencies than equivalent percentagewise changes in storm surge magnitudes. We explore several implications of these findings. Firstly, understanding future local changes in storm surges and tides is required to fully quantify future flood hazards. Secondly, existing hazard assessments may underestimate future flood rates as changes in tides are not considered. Finally, identifying the flood frequencies and severities relevant to local coastal managers is imperative to develop useable and policy‐relevant projections for decisionmakers.more » « less
-
The intensification of coastal storms, combined with declining sea ice cover, sea level rise, and changes to permafrost conditions, will likely increase the incidence and impact of storm surge flooding in Arctic coastal environments. In coastal communities accurate information on the exposure of infrastructure can make an important contribution to adaptation planning. In this study, we use high resolution elevation data from airborne LiDAR to generate storm flooding scenarios for three coastal communities (Utqiag_ vik, Wainwright, and Kaktovik) in northern Alaska. To estimate the potential for damage to infrastructure caused by flooding for each community, we generated data on replacement costs and used it to estimate the financial impact of 24 storm flooding scenarios of varying intensities. This analysis shows that all three communities are exposed to storm surges, but highlights the fact that infrastructure in Utqiag_ vik (the administrative center of the North Slope Borough) is significantly more exposed than buildings in Wainwright and Kaktovik. Our findings show that flooding scenarios can complement information gained from past events and help to inform local-decision making.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/geosciences12060224
