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1. The 21st-Century Relative Sea Level Rise in Anne Arundel County, Maryland

   https://doi.org/10.1061/9780784484852.037  

   Ikiriko, Sotonye; Liu, Yi; Qian, Sean; Zhou, Xin (May 2023, American Society of Civil Engineers)

   The rate at which sea level is rising in recent years due to global warming has become a growing concern, most especially as it affects coastal areas of the world. The devastating impact of sea level rise (SLR) on coastal communities, ranging from coastal beach erosion, nuisance high tide flooding, and saltwater pollution of low-lying farmlands to loss of tidal wetlands is leading to a decline in social and economic activities especially in coastal areas. According to the National Oceanic and Atmospheric Administration (NOAA), 40% of the US population living on the coast is inevitably vulnerable to SLR. Therefore, the objective of this study is to project relative sea level rise (RSLR) for Anne Arundel County and to estimate the contribution of land subsidence to RSLR at this location. To project RSLR for Anne Arundel County, this study combines global mean sea level rise (GMSLR) scenarios with local land subsidence measured at GPS LOYF station in Annapolis, Anne Arundel County, Maryland. Current quadratic trend of RSLR in Anne Arundel County projects that by 2100, RSLR for the county will be approximately 1.2 m forecasting from 1992, which is 86% and 174% of the GMSLR intermediate-high and intermediate-low scenarios, respectively. Land subsidence significantly contributed to RSLR in the 20th century; however, since 2001 absolute sea level rise (ASLR) driven by climate change has significantly contributed to RSLR in this location. The results in this paper suggest considering the intermediate-high RSLR scenario for planning and decision-making in Anne Arundel County, Maryland, in relation to SLR. 

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2. Multiple climate change-driven tipping points for coastal systems

   https://doi.org/10.1038/s41598-021-94942-7  

   Barnard, Patrick L.; Dugan, Jenifer E.; Page, Henry M.; Wood, Nathan J.; Hart, Juliette A.; Cayan, Daniel R.; Erikson, Li H.; Hubbard, David M.; Myers, Monique R.; Melack, John M.; et al (December 2021, Scientific Reports)

   Abstract As the climate evolves over the next century, the interaction of accelerating sea level rise (SLR) and storms, combined with confining development and infrastructure, will place greater stresses on physical, ecological, and human systems along the ocean-land margin. Many of these valued coastal systems could reach “tipping points,” at which hazard exposure substantially increases and threatens the present-day form, function, and viability of communities, infrastructure, and ecosystems. Determining the timing and nature of these tipping points is essential for effective climate adaptation planning. Here we present a multidisciplinary case study from Santa Barbara, California (USA), to identify potential climate change-related tipping points for various coastal systems. This study integrates numerical and statistical models of the climate, ocean water levels, beach and cliff evolution, and two soft sediment ecosystems, sandy beaches and tidal wetlands. We find that tipping points for beaches and wetlands could be reached with just 0.25 m or less of SLR (~ 2050), with > 50% subsequent habitat loss that would degrade overall biodiversity and ecosystem function. In contrast, the largest projected changes in socioeconomic exposure to flooding for five communities in this region are not anticipated until SLR exceeds 0.75 m for daily flooding and 1.5 m for storm-driven flooding (~ 2100 or later). These changes are less acute relative to community totals and do not qualify as tipping points given the adaptive capacity of communities. Nonetheless, the natural and human built systems are interconnected such that the loss of natural system function could negatively impact the quality of life of residents and disrupt the local economy, resulting in indirect socioeconomic impacts long before built infrastructure is directly impacted by flooding. 

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3. Hurricane surge prediction using a statistical–deterministic modeling framework: application to St Mary Parish, Louisiana, USA

   https://doi.org/10.1007/s11069-026-08263-x  

   Ma, Xuan; Kaihatu, James M (June 2026, Natural Hazards)

   Abstract Climate change and sea-level rise are escalating the threat of hurricane-induced coastal flooding, demanding new approaches for long-term risk assessment. A linked statistical–deterministic modeling framework for evaluating future hurricane surge hazards with future climate conditions is detailed. The methodology combines a synthetic hurricane track simulator with a high-resolution hydrodynamic model to simulate thousands of potential hurricanes. The model includes scenarios with sea-level rise (SLR) and hurricane climatology change (HCC) factors in the next century. This framework was applied to St. Mary Parish in south Louisiana. The contribution of HCC and SLR to potential surges are assessed. Statistical analyses of the results reveal that sea-level rise is a dominant driver of increased flooding: surge heights in the study area grow significantly under even modest SLR. In contrast, the effects of the projected changes in hurricane frequency/intensity are more subtle – any surge increases due to HCC are relatively minor and not consistently significant across the six city case studies. These findings suggest that rising ocean base levels amplify flood risk more uniformly than shifts in hurricane climatology in the coming 100 years. The study also quantifies local sensitivity to SLR. The results highlight the critical importance of accounting for sea-level rise in coastal flood mitigation strategies. The demonstrated modeling approach offers a flexible, scalable tool for coastal engineers and planners, capable of producing granular surge risk estimates under future scenarios with climate change. 

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4. 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

   https://doi.org/10.3390/geosciences12060224  

   Shen, Yawen; Tahvildari, Navid; Morsy, Mohamed M.; Huxley, Chris; Chen, T. Donna; Goodall, Jonathan Lee (June 2022, Geosciences)

   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. 

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5. Probabilistic tropical cyclone surge hazard under future sea-level rise scenarios: A case study in the Chesapeake Bay region

   https://doi.org/10.1061/9780784484852.117  

   K. Kim, J.-W. Lee (January 2023, 2023 World Environmental & Water Resources Congress)

   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. 

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