Probabilistic Projection of Mean Sea Level and Coastal Extremes

Obeysekera, Jayantha; Park, Joseph; Irizarry-Ortiz, Michelle M.; Barnes, Jenifer; Trimble, Paul

doi:10.1061/(asce)ww.1943-5460.0000154

Cited by 8 publications

(11 citation statements)

References 41 publications

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“…Whereas the significance of impacts on some aspects of the hydrological cycle such as streamflows remains debatable and inconclusive (e.g., Cohn and Lins 2005;Hirsch and Ryberg 2012), some hydrologists have declared that "stationarity is dead" (Milly et al 2008), and suggest that nonstationary probabilistic models need to be identified and possibly used in some practical cases. Furthermore, warming associated with climate change may be causing sea-level rising globally and recent projections of sea level include varying degrees of acceleration in the sea-level rise rate (e.g., Bindoff et al 2007;Rahmstorf 2007;Vermeer and Rahmstorf 2009;Obeysekera et al 2012;Sallenger et al 2012). Such increases in sea levels are expected to increase flooding due to storm surges in coastal regions and reduce the reliability of flood-protection systems in coastal watersheds (Obeysekera et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Concepts of Return Period and Risk for Nonstationary Hydrologic Extreme Events

2014

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Current practice using probabilistic methods applied for designing hydraulic structures generally assume that extreme events are stationary. However, many studies in the past decades have shown that hydrological records exhibit some type of nonstationarity such as trends and shifts. Human intervention in river basins (e.g., urbanization), the effect of low-frequency climatic variability (e.g., Pacific Decadal Oscillation), and climate change due to increased greenhouse gasses in the atmosphere have been suggested to be the leading causes of changes in the hydrologic cycle of river basins in addition to changes in the magnitude and frequency of extreme floods and extreme sea levels. To tackle nonstationarity in hydrologic extremes, several approaches have been proposed in the literature such as frequency analysis, in which the parameters of a given model vary in accordance with time. The aim of this paper is to show that some basic concepts and methods used in designing flood-related hydraulic structures assuming a stationary world can be extended into a nonstationary framework. In particular, the concepts of return period and risk are formulated by extending the geometric distribution to allow for changing exceeding probabilities over time. Building on previous developments suggested in the statistical and climate change literature, the writers present a simple and unified framework to estimate the return period and risk for nonstationary hydrologic events along with examples and applications so that it can be accessible to a broad audience in the field. The applications demonstrate that the return period and risk estimates for nonstationary situations can be quite different than those corresponding to stationary conditions. They also suggest that the nonstationary analysis can be helpful in making an appropriate assessment of the risk of a hydraulic structure during the planned project-life.

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Section: Introductionmentioning

confidence: 99%

Revisiting the Concepts of Return Period and Risk for Nonstationary Hydrologic Extreme Events

2014

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“…Chief among these approaches is the nonstationary Generalized Extreme Value (GEV) distribution and Generalized Pareto Distribution (GPD). Nonstationarity of sea level conditions is typically taken into account by time‐dependent distribution parameters for GEV (Boettle et al, ; Menéndez & Woodworth, ; Obeysekera et al, ; Salas & Obeysekera, ) or GP (Kyselý et al, ; Méndez et al, ) distributions. The increasing frequency of minor flooding due to SLR has motivated several recent studies (Dahl et al, ; Moftakhari, AghaKouchak, et al, ; Sweet et al, , ).…”

Section: Introductionmentioning

confidence: 99%

“…This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Woodworth, 2010; Obeysekera et al, 2013;Salas & Obeysekera, 2014) or GP (Kyselý et al, 2010;Méndez et al, 2006) distributions. The increasing frequency of minor flooding due to SLR has motivated several recent studies (Dahl et al, 2017;Sweet et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

A Coherent Statistical Model for Coastal Flood Frequency Analysis Under Nonstationary Sea Level Conditions

et al. 2019

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Flood exposure is increasing in coastal communities due to rising sea levels. Understanding the effects of sea level rise (SLR) on frequency and consequences of coastal flooding and subsequent social and economic impacts is of utmost importance for policymakers to implement effective adaptation strategies. Effective strategies may consider impacts from cumulative losses from minor flooding as well as acute losses from major events. In the present study, a statistically coherent Mixture Normal‐Generalized Pareto Distribution model was developed, which reconciles the probabilistic characteristics of the upper tail as well as the bulk of the sea level data. The nonstationary sea level condition was incorporated in the mixture model using Quantile Regression method to characterize variable Generalized Pareto Distribution thresholds as a function of SLR. The performance validity of the mixture model was corroborated for 68 tidal stations along the Contiguous United States (CONUS) coast with long‐term observed data. The method was subsequently employed to assess existing and future coastal minor and major flood frequencies. The results indicate that the frequency of minor and major flooding will increase along all CONUS coastal regions in response to SLR. By the end of the century, under the “Intermediate” SLR scenario, major flooding is anticipated to occur with return period less than a year throughout the coastal CONUS. However, these changes vary geographically and temporally. The mixture model was reconciled with the property exposure curve to characterize how SLR might influence Average Annual Exposure to coastal flooding in 20 major CONUS coastal cities.

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“…Geographic variability in the rates of sea level rise is also expected (White et al, 2005). Therefore, a predicted mean sea level increase of 0.6 m by the 2090s (relative to 1980) was selected as an average estimate based on global and regional projections of sea level rise (IPCC, 2007;Rahmstorf, 2007;Obeysekera, 2013). The hydrogeological system of Andros Island is considered recharge-limited rather than topographylimited, because there is some capacity for the freshwater lens to rise in the unsaturated zone without leading to surface flooding .…”

Section: Climate Change Simulationsmentioning

confidence: 99%

From days to decades: numerical modelling of freshwater lens response to climate change stressors on small low-lying islands

Holding

Allen²

2015

Hydrol. Earth Syst. Sci.

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Abstract. Freshwater lenses on small islands are vulnerable to many climate change-related stressors, which can act over relatively long time periods, on the order of decades (e.g., sea level rise, changes in recharge), or short time periods, such as days (storm surge overwash). This study evaluates the response of the freshwater lens on a small lowlying island to various stressors. To account for the varying temporal and spatial scales of the stressors, two different density-dependent flow and solute transport codes are used: SEAWAT (saturated) and HydroGeoSphere (unsaturated/saturated). The study site is Andros Island in the Bahamas, which is characteristic of other low-lying carbonate islands in the Caribbean and Pacific regions. In addition to projected sea level rise and reduced recharge under future climate change, Andros Island experienced a storm surge overwash event during Hurricane Francis in 2004, which contaminated the main wellfield. Simulations of reduced recharge result in a greater loss of freshwater lens volume (up to 19 %), while sea level rise contributes a lower volume loss (up to 5 %) due to the flux-controlled conceptualization of Andros Island, which limits the impact of sea level rise. Reduced recharge and sea level rise were simulated as incremental instantaneous shifts. The lens responds relatively quickly to these stressors, within 0.5 to 3 years, with response time increasing as the magnitude of the stressor increases. Simulations of the storm surge overwash indicate that the freshwater lens recovers over time; however, prompt remedial action can restore the lens to potable concentrations up to 1 month sooner.

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Probabilistic Projection of Mean Sea Level and Coastal Extremes

Cited by 8 publications

References 41 publications

Revisiting the Concepts of Return Period and Risk for Nonstationary Hydrologic Extreme Events

Revisiting the Concepts of Return Period and Risk for Nonstationary Hydrologic Extreme Events

A Coherent Statistical Model for Coastal Flood Frequency Analysis Under Nonstationary Sea Level Conditions

From days to decades: numerical modelling of freshwater lens response to climate change stressors on small low-lying islands

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