Changes to extreme wave climates of islands within the Western Tropical Pacific throughout the 21st century under RCP 4.5 and RCP 8.5, with implications for island vulnerability and sustainability

Shope, James B.; Storlazzi, Curt D.; Erikson, Li H.; Hegermiller, Christie A.

doi:10.1016/j.gloplacha.2016.03.009

Cited by 51 publications

(34 citation statements)

References 57 publications

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“…In addition to EWS, the BEWARE system can be used to investigate hypothetical climate change scenarios, such as changes to sea level, wave climate, or reef roughness due to coral degradation or restoration. Shope et al () used the formulation of Stockdon et al () (developed on the basis of runup data obtained on sandy sloping beaches under nonextreme offshore forcing) to estimate Pacific island runup under future climate change scenarios. The BEWARE system developed for this study could provide a more comprehensive estimate than those based on the Stockdon et al () equations by accounting for input uncertainty and considering the full suite of processes involved in reef hydrodynamics (including resonance) and the resulting wave‐driven flooding.…”

Section: Discussionmentioning

confidence: 99%

A Bayesian‐Based System to Assess Wave‐Driven Flooding Hazards on Coral Reef‐Lined Coasts

et al. 2017

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Many low‐elevation, coral reef‐lined, tropical coasts are vulnerable to the effects of climate change, sea level rise, and wave‐induced flooding. The considerable morphological diversity of these coasts and the variability of the hydrodynamic forcing that they are exposed to make predicting wave‐induced flooding a challenge. A process‐based wave‐resolving hydrodynamic model (XBeach Non‐Hydrostatic, “XBNH”) was used to create a large synthetic database for use in a “Bayesian Estimator for Wave Attack in Reef Environments” (BEWARE), relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef‐lined coasts. Building on previous work, BEWARE improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on reef‐lined coasts. The Bayesian estimator has high predictive skill for the XBNH model outputs that are flooding indicators, and was validated for a number of available field cases. It was found that, in order to accurately predict flooding hazards, water depth over the reef flat, incident wave conditions, and reef flat width are the most essential factors, whereas other factors such as beach slope and bed friction due to the presence or absence of corals are less important. BEWARE is a potentially powerful tool for use in early warning systems or risk assessment studies, and can be used to make projections about how wave‐induced flooding on coral reef‐lined coasts may change due to climate change.

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Section: Discussionmentioning

confidence: 99%

A Bayesian‐Based System to Assess Wave‐Driven Flooding Hazards on Coral Reef‐Lined Coasts

et al. 2017

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“…Climate change drives potential changes to wave climate [Hemer et al, 2013;Erikson et al, 2015;Shope et al, 2016], which must be accounted for when predicting long-term coastal evolution. CoSMoS-COAST is driven with hindcast (1995 to 2011) and projected time series (2011 to 2100) of daily maximum wave heights and corresponding wave periods and directions from Erikson et al [2015] and Hegermiller et al [2016], who used a series of global-to-local nested wave models (i.e., WaveWatch III (WW3) and SWAN), shown in Figure 4.…”

Section: Wave Forcingmentioning

confidence: 99%

A model integrating longshore and cross‐shore processes for predicting long‐term shoreline response to climate change

et al. 2017

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We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS‐COAST (Coastal One‐line Assimilated Simulation Tool), is a transect‐based, one‐line model that predicts short‐term and long‐term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process‐based models of coastline evolution due to longshore and cross‐shore transport by waves and sea level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long‐term predictions. We apply CoSMoS‐COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995–2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010–2100) driven by GCM‐projected wave and sea level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea level rise scenarios of 0.93 to 2.0 m.

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“…In near‐term, internal variability (Bromirski et al, ) due to a combination of dynamic and static equilibrium effects (Hay et al, ; Kopp et al, ) would significantly affect the estimation of local sea level changes (Kopp et al, ). Also, the frequency and intensity of storm events are expected to be altered in northeastern Pacific Ocean under a changing climate (Shope et al, ). For example, significant wave height is expected to decrease south of ∼50°N.…”

Section: Resultsmentioning

confidence: 99%

Translating Uncertain Sea Level Projections Into Infrastructure Impacts Using a Bayesian Framework

Moftakhari

AghaKouchak

Sanders

et al. 2017

Geophysical Research Letters

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Climate change may affect ocean‐driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean‐atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near‐future (1998–2063) and mid‐future (2018–2083). The results show that road flooding rates will be significantly higher in the near‐future and mid‐future compared to the recent past (1950–2015) if adaptation measures are not implemented.

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Changes to extreme wave climates of islands within the Western Tropical Pacific throughout the 21st century under RCP 4.5 and RCP 8.5, with implications for island vulnerability and sustainability

Cited by 51 publications

References 57 publications

A Bayesian‐Based System to Assess Wave‐Driven Flooding Hazards on Coral Reef‐Lined Coasts

A Bayesian‐Based System to Assess Wave‐Driven Flooding Hazards on Coral Reef‐Lined Coasts

A model integrating longshore and cross‐shore processes for predicting long‐term shoreline response to climate change

Translating Uncertain Sea Level Projections Into Infrastructure Impacts Using a Bayesian Framework

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