Dynamic flood modeling essential to assess the coastal impacts of climate change

Barnard, Patrick L.; Erikson, Li H.; Foxgrover, Amy C.; Hart, Juliette A. Finzi; Limber, Patrick W.; O’Neill, Andrea C.; Ormondt, Maarten van; Vitousek, Sean; Wood, Nathan J.; Hayden, Michael D.; Jones, Jeanne M.

doi:10.1038/s41598-019-40742-z

Cited by 132 publications

(123 citation statements)

References 79 publications

(94 reference statements)

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“…Kantamaneni et al ., ) depends on understanding the extremes of storm behaviour. Including dynamic coastal processes in both short‐ and long‐term hazard planning is essential (Barnard et al ., ). Coastal boulder deposits preserve information about past incursions of high‐energy storm waters, which can help us better prepare for storms of the future.…”

Section: Understanding Coastal Boulder Deposits Is Relevant For Naturmentioning

confidence: 97%

Megagravel deposits on the west coast of Ireland show the impacts of severe storms

Cox

2020

Weather

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Graphical abstractCoastal boulder deposits on Inishmore, one of the Aran Islands off Ireland's west coast, showcase many of the classic features. Note the adult human for scale (in the yellow circle). The cliff‐collapse block in the foreground is the largest ever documented to be moved by storm waves. Weighing 620t, it was shifted several metres during the storms of 2013–2014. The platform on which it sits is 5m above high water, and the block is 30m inland. The cliff at this site is 9m high. The boulder ridges behind the figure on the cliff top are 11–15m above high water, and the front of the pile ranges from 45–64m inland. A number of the biggest blocks on the upper platform, which weigh several tens of tonnes, were also moved sideways during the 2013–2014 storms; and within the ridge, the pale grey colours show smaller (1–15t) boulders that were overturned. Drone photograph taken by Dr Eugene Farrell (NUI Galway). Used with permission.

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Section: Understanding Coastal Boulder Deposits Is Relevant For Naturmentioning

confidence: 97%

Megagravel deposits on the west coast of Ireland show the impacts of severe storms

Cox

2020

Weather

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“…While “bathtub” models represent flooding extent by projecting ESLs, as measured at tide gauges, onto topography (e.g., as measured via high‐resolution LIDAR) without accounting for local atmosphere/ocean dynamics or the frictional interference of the natural or built environment to determine extent of flooding, hydrodynamic models accounting for of the flow of water in the ocean and onto land reveal a more complex story (Deb & Ferreira, ; Lin et al, , ; Orton et al, ; J. Wang et al, ). Nonlinear hydrodynamic responses vary spatially as a function of coastal topography, land use, and storm characteristics (Atkinson et al, ; Anarde et al, ; Barnard et al, ; Ding et al, ; Ferreira et al, ; Glass et al, ; Mousavi et al, ; Passeri et al, ; Smith et al, ; Wang et al, ; Woodruff et al, ; Zhang et al, ). Most studies using hydrodynamic models have focused on the effects of storm surge (e.g., Muis et al, ), though in some areas precipitation‐driven flooding is of crucial importance (Wahl & Chambers, ; Wright et al, ).…”

Section: Projections Of Extreme Sea Level Change and Associated Floodingmentioning

confidence: 99%

“…The coast is not simply a static background over which water flows, though most studies of coastal flood hazards treat it as such. It instead exhibits dynamic growth and destruction of land and ecosystems (Anarde et al, ; Barnard et al, ; Glass et al, ; Le Cozannet et al, ; Passeri et al, ). Waves, currents, and tides redistribute sediment along and across the coastal zone, resulting in shoreline dynamics significantly different than would occur in a static coastal landscape (Ashton et al, ; Murray et al, ; Paola et al, ; Payo et al, ).…”

Section: Coastal Flooding In a Dynamic Physical Environmentmentioning

confidence: 99%

Usable Science for Managing the Risks of Sea‐Level Rise

et al. 2019

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Sea-level rise sits at the frontier of usable climate climate change research, because it involves natural and human systems with long lags, irreversible losses, and deep uncertainty. For example, many of the measures to adapt to sea-level rise involve infrastructure and land-use decisions, which can have multigenerational lifetimes and will further influence responses in both natural and human systems. Thus, sea-level science has increasingly grappled with the implications of (1) deep uncertainty in future climate system projections, particularly of human emissions and ice sheet dynamics; (2) the overlay of slow trends and high-frequency variability (e.g., tides and storms) that give rise to many of the most relevant impacts; (3) the effects of changing sea level on the physical exposure and vulnerability of ecological and socioeconomic systems; and (4) the challenges of engaging stakeholder communities with the scientific process in a way that genuinely increases the utility of the science for adaptation decision making. Much fundamental climate system research remains to be done, but many of the most critical issues sit at the intersection of natural sciences, social sciences, engineering, decision science, and political economy. Addressing these issues demands a better understanding of the coupled interactions of mean and extreme sea levels, coastal geomorphology, economics, and migration; decision-first approaches that identify and focus research upon those scientific uncertainties most relevant to concrete adaptation choices; and a political economy that allows usable science to become used science.Plain Language Summary The impacts of sea-level rise pose growing threats to coastal communities, economies, and ecosystems, and decisions made today-in areas like land-use policies, coastal development, and infrastructure investment-will affect exposure and vulnerability for generations to come. Thus, the usability of sea-level science is a pressing concern. Ensuring usability requires grappling with deep uncertainty in long-term sea-level projections, the relationship between long-term trends and the impacts of short-lived extreme events, and the ways in which the physical coast, as well as people and ecosystems along the coast, respond to increasingly frequent flooding. At the same time, it also requires more extensive and deliberate stakeholder engagement throughout the scientific process, as well as cognizance of the political economy of linking stakeholder-engaged science to action.

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“…Such different TWL components can lead to different impacts on the open beach (Cohn et al, ; Serafin et al, ; Serafin et al, ) and different flood extents in the backshore (Bilskie & Hagen, ). The presented framework can be a tool used to make informed decisions for the input conditions to dynamical numerical modeling studies assessing flood extents and hazard zones contingent on future extremes (Barnard et al, ; Erikson et al, ).…”

Section: Climate Emulator Twl Outputmentioning

confidence: 99%

Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential

et al. 2019

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Rising seas coupled with ever increasing coastal populations present the potential for significant social and economic loss in the 21st century. Relatively short records of the full multidimensional space contributing to total water level coastal flooding events (astronomic tides, sea level anomalies, storm surges, wave run-up, etc.) result in historical observations of only a small fraction of the possible range of conditions that could produce severe flooding. The Time-varying Emulator for Short-and Long-Term analysis of coastal flood hazard potential is presented here as a methodology capable of producing new iterations of the sea-state parameters associated with the present-day Pacific Ocean climate to simulate many synthetic extreme compound events. The emulator utilizes weather typing of fundamental climate drivers (sea surface temperatures, sea level pressures, etc.) to reduce complexity and produces new daily synoptic weather chronologies with an auto-regressive logistic model accounting for conditional dependencies on the El Niño Southern Oscillation, the Madden-Julian Oscillation, seasonality, and the prior two days of weather progression. Joint probabilities of sea-state parameters unique to simulated weather patterns are used to create new time series of the hypothetical components contributing to synthetic total water levels (swells from multiple directions coupled with water levels due to wind setup, temperature anomalies, and tides). The Time-varying Emulator for Short-and Long-Term analysis of coastal flood hazard potential reveals the importance of considering the multivariate nature of extreme coastal flooding, while progressing the ability to incorporate large-scale climate variability into site specific studies assessing hazards within the context of predicted climate change in the 21st century.Plain Language Summary Predicting extreme coastal flooding is a present-day societal need and will only become more relevant as mean water levels increase due to sea level rise. However, the number of processes contributing to such events is too high for relatively short observational records to have measured all of the constructive combinations of waves, surge, wind, and sea level anomalies. We present a framework designed to create hypothetical combinations of relevant flood hazard potential processes by simulating the climate and weather patterns that drive coastal flooding. Including large-scale oceanic and atmospheric patterns as the drivers of coastal hazards reveals the climate a coastal community is most vulnerable to, which will be increasingly more important to understand as the climate changes during the 21st century.

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Dynamic flood modeling essential to assess the coastal impacts of climate change

Cited by 132 publications

References 79 publications

Megagravel deposits on the west coast of Ireland show the impacts of severe storms

Megagravel deposits on the west coast of Ireland show the impacts of severe storms

Usable Science for Managing the Risks of Sea‐Level Rise

Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential

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