Coastal vulnerability to sea-level rise: a preliminary database for the U.S. Atlantic, Pacific, and Gulf of Mexico coasts

Hammar-Klose, Erika S.; Thieler, E. Robert

doi:10.3133/ds68

Cited by 86 publications

(63 citation statements)

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“…Additionally, there is a need to monitor NNBI projects during and after storm events to assess performance under extreme conditions [34,37]. Finally, the use and refinement of existing national or regional socio-ecological indices, such as the Coastal Vulnerability Index [51] and Coastal Hazard Index [17], and the development of new indices could allow for more efficient, cost-effective, and appropriate siting of NNBI [14,17]. These efforts will help to demonstrate the effectiveness of NNBI and to reduce uncertainty about the role of NNBI in coastal resilience efforts.…”

Section: Challenges To Implementing Nnbi Solutionsmentioning

confidence: 99%

Investing in Natural and Nature-Based Infrastructure: Building Better Along Our Coasts

et al. 2018

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Much of the United States' critical infrastructure is either aging or requires significant repair, leaving U.S. communities and the economy vulnerable. Outdated and dilapidated infrastructure places coastal communities, in particular, at risk from the increasingly frequent and intense coastal storm events and rising sea levels. Therefore, investments in coastal infrastructure are urgently needed to ensure community safety and prosperity; however, these investments should not jeopardize the ecosystems and natural resources that underlie economic wealth and human well-being. Over the past 50 years, efforts have been made to integrate built infrastructure with natural landscape features, often termed "green" infrastructure, in order to sustain and restore valuable ecosystem functions and services. For example, significant advances have been made in implementing green infrastructure approaches for stormwater management, wastewater treatment, and drinking water conservation and delivery. However, the implementation of natural and nature-based infrastructure (NNBI) aimed at flood prevention and coastal erosion protection is lagging. There is an opportunity now, as the U.S. government reacts to the recent, unprecedented flooding and hurricane damage and considers greater infrastructure investments, to incorporate NNBI into coastal infrastructure projects. Doing so will increase resilience and provide critical services to local communities in a cost-effective manner and thereby help to sustain a growing economy.

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Section: Challenges To Implementing Nnbi Solutionsmentioning

confidence: 99%

Investing in Natural and Nature-Based Infrastructure: Building Better Along Our Coasts

et al. 2018

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“…The dataset used in this study includes that published in (Gutierrez and others, 2011b), which was based on data from (Thieler and Hammar-Klose, 1999) and data from Gulf and Pacific coasts (Hammar-Klose and Thieler, 2001) of the United States, and newer data obtained for Alaska and Hawaii using the methodology of Thieler and Hammar-Klose (1999) Figure 1. The structure of the Bayesian Network (BN) used for this study.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…There are a number of approaches that can be applied to forecast shoreline changes (Leatherman, 2001;Gutierrez and others, 2009). In previous work others, 2011a and2011b) an approach using a Bayesian Network (BN) was developed that used previously published U.S. Geological Survey (USGS) coastal vulnerability index (CVI) datasets (Thieler and Hammar-Klose, 1999;Hammar-Klose and Thieler, 2001) to identify and evaluate the potential for future shoreline changes. Gutierrez and others (2011a) demonstrated that BNs provide a useful method to evaluate relations between forcing factors (for example, rate of sea-level rise, wave height, or tidal range), and shoreline-change rate.…”

Section: Introductionmentioning

confidence: 99%

Using a Bayesian Network to predict shore-line change vulnerability to sea-level rise for the coasts of the United States

Gutierrez¹,

Plant²,

Pendleton³

et al. 2014

Open-File Report

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For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit http://www.usgs.gov or call 1-888-ASK-USGS For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprodTo order this and other USGS information products, visit http://store.usgs.gov Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.Suggested citation: Gutierrez, B.T., Plant, N.G., Pendleton, E.A., and Thieler, E.R., 2014, Using a Bayesian Network to predict shoreline change vulnerability to sea-level rise for the coasts of the United States: U.S. Geological Survey Open-File Report 2014-1083, 26 p., http://dx.doi.org/10.3133/ofr20141083. ISSN 2331-1258 AbstractSea-level rise is an ongoing phenomenon that is expected to continue and is projected to have a wide range of effects on coastal environments and infrastructure during the 21st century and beyond. Consequently, there is a need to assemble relevant datasets and to develop modeling or other analytical approaches to evaluate the likelihood of particular sea-level rise impacts, such as coastal erosion, and to inform coastal management decisions with this information. This report builds on previous work that compiled oceanographic and geomorphic data as part of the U.S. Geological Survey's Coastal Vulnerability Index (CVI) for the U.S. Atlantic coast, and developed a Bayesian Network to predict shoreline-change rates based on sea-level rise plus variables that describe the hydrodynamic and geologic setting. This report extends the previous analysis to include the Gulf and Pacific coasts of the continental United States and Alaska and Hawaii, which required using methods applied to the USGS CVI dataset to extract data for these regions. The Bayesian Network converts inputs that include observations of local rates of relative sea-level change, mean wave height, mean tide range, a geomorphic classification, coastal slope, and observed shoreline-change rates to calculate the probability of the shoreline-erosion rate exceeding a threshold level of 1 meter per year for the coasts of the United States. The calculated probabilities were compared to the historical observations of shoreline change to evaluate the hindcast success rate of the most likely probability of shoreline change. Highest accuracy was determined for the coast of Hawaii (98 percent success rate) and lowest accuracy was determined for the Gulf of Mexico (34 percent success rate). The minimum success rate rose to nearly 80 percent (Atlantic and Gulf coasts) when success included shoreline-change outcomes that were adjacent to the most likely outcome. Additionally, the probabilistic ap...

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“…In the first instance it strengthens our understanding of the mechanisms which control the behaviour of the wetland system as a largescale unit within the physical landscape. Identifying 'hotspots' of wetland loss and a broad-scale assessment of levels of wetland vulnerability enables coastal managers and national organisations to make decisions on the best use of limited resources (Hammar-Klose & Thieler, 2001). Such modelling forms a basis from which effective plans can be developed to manage wetland change.…”

Section: Broad-scale Modelling Of Wetland Behaviourmentioning

confidence: 99%

Broad-scale modelling of coastal wetlands: what is required?

2007

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A Wetland Change Model has been developed to identify the vulnerability of coastal wetlands at broad spatial (regional to global (mean spatial resolution of 85 km)) and temporal scales (modelling period of 100 years). The model provides a dynamic and integrated assessment of wetland loss, and a means of estimating the transitions between different vegetated wetland types and open water under a range of scenarios of sea-level rise and changes in accommodation space from human intervention. This paper is an overview of key issues raised in the process of quantifying broad-scale vulnerabilities of coastal wetlands to forcing from sea-level rise discussing controlling factors of tidal range, sediment availability and accommodation space, identification of response lags and defining the threshold for wetland loss and transition.

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Coastal vulnerability to sea-level rise: a preliminary database for the U.S. Atlantic, Pacific, and Gulf of Mexico coasts

Cited by 86 publications

References 0 publications

Investing in Natural and Nature-Based Infrastructure: Building Better Along Our Coasts

Investing in Natural and Nature-Based Infrastructure: Building Better Along Our Coasts

Using a Bayesian Network to predict shore-line change vulnerability to sea-level rise for the coasts of the United States

Broad-scale modelling of coastal wetlands: what is required?

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