The Sea-Level Rise Hazards and Decision-Support project assesses present and future coastal vulnerability to provide actionable information for management of our Nation’s coasts. Through multidisciplinary research and collaborative partnerships with decision-makers, physical, biological, and social factors that describe landscape and habitat changes are incorporated in a probabilistic modeling framework to explore the future likelihood of a variety of impacts and outcomes. Scenario-based products and tools can be applied to inform adaptation strategies, evaluate tradeoffs, and examine mitigation options.
Although the general nature of the changes that can occur on ocean coasts in response to sea-level rise (SLR) is widely recognized, it is difficult to predict exactly what changes may occur, or when they may occur. The ability to predict the extent of these changes is limited by uncertainties in both currently available data that describe the coastal environment, as well as gaps in understanding of some of the driving processes that contribute to coastal change (e.g., rate and magnitude of sea level rise, changes in storminess). Additionally, the cumulative impacts of physical and biological change on the quantity and quality of coastal habitats are not well understood, and potential societal responses to SLR are uncertain. Nonetheless, coastal managers need actionable information to make decisions that account for future hazards, including SLR.
This project brings together scientists from the disciplines of geology, hydrology, geography, biology, and ecology to synthesize information on coastal environments to address the effects of SLR on our Nation’s coasts. The approach uses a probabilistic framework, which allows researchers to incorporate observations and account for uncertainties, to evaluate the likelihood of a variety of SLR impacts, including:
- land loss from inundation and erosion,
- migration of coastal landforms,
- changes to groundwater systems, and
- changes to coastal habitat.
Decision makers depend on the future coastal environment having certain characteristics. For example, homeowners desire a home that is at low risk of loss due to coastal erosion. Local planners and managers also need to be able to identify infrastructure that could be at risk to make effective long-term adaptation or mitigation decisions. Land managers may target parcels for acquisition that provide critical habitat for threatened and endangered species. Flora and fauna require specific habitat attributes to survive and flourish. To proactively plan for an uncertain future, decision makers need the ability to consider alternative response measures and assess the benefits and costs of options. Consequently, there is a need to develop decision frameworks that combine detailed and sometimes complicated scientific information in a way that improves the ability to translate it into decision making scenarios.
Probabilistic Framing
The Bayesian statistical framework is ideal for using data sets derived from historical or modern observations such as long-term shoreline change or wetland accretion/elevation trends. This information can be combined with model simulations and used to define the relationships between key variables in coastal environments. A Bayesian network provides a means of integrating these data to evaluate competing hypotheses regarding the relationships between forcing factors (e.g., rate of SLR, suspended sediment concentration, elevation change) and responses (e.g., shoreline change, wetland vertical accretion, water table change). This framework allows scientists to make probabilistic predictions of the future state of coastal environments for outcomes such as shoreline change, wetland survival, and changes in the depth to groundwater. The predictions also have estimates of outcome uncertainty that can be expressed as both numbers (e.g., 90%) and words (e.g., very likely). The ability to communicate SLR impacts in terms of a probabilistic prediction can improve scientists’ ability to support decision making and evaluate specific management questions about alternatives for addressing SLR.
Below are other science projects associated with this project.
Probabilistic Framing
Sea Level Change
Estuarine Processes, Hazards, and Ecosystems
Coastal Landscape Response to Sea-Level Rise Assessment for the Northeastern United States
Beach-dependent Shorebirds
National Assessment of Coastal Vulnerability to Sea Level Rise
Empowering decision-makers: A dynamic web interface for running Bayesian networks
Relative Coastal Vulnerability Assessment of National Park Units to Sea-Level Rise
Below are publications associated with this project.
Probabilistic patterns of inundation and biogeomorphic changes due to sea-level rise along the northeastern U.S. Atlantic coast
Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model
Smartphone technologies and Bayesian networks to assess shorebird habitat selection
Global and regional sea level rise scenarios for the United States
Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability
Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood
Coupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network
Using a Bayesian network to predict barrier island geomorphologic characteristics
Evaluating coastal landscape response to sea-level rise in the northeastern United States: approach and methods
Using a Bayesian Network to predict shore-line change vulnerability to sea-level rise for the coasts of the United States
A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover (Charadrius melodus) using barrier island features
Effects of sea-level rise on barrier island groundwater system dynamics: ecohydrological implications
Bridging groundwater models and decision support with a Bayesian network
Below are data or web applications associated with this project.
Sea Level Change
An Interactive Guide to Global and Regional Sea Level Rise Scenarios for the United States
Coastal Change Hazards Portal
Interactive access to coastal change science and data for our Nation’s coasts. Information and products are organized within three coastal change hazard themes: 1) extreme storms, 2) shoreline change, and 3) sea-level rise. Displays probabilities of coastal erosion.
Below are software products associated with this project.
bi-transect-extractor
This package is used to calculate coastal geomorphology variables along shore-normal transects. The calculated variables are used as inputs for modeling geomorphology using a Bayesian Network (BN).
iPlover
iPlover was developed by the U.S. Geological Survey Woods Hole Coastal and Marine Science Center and the USGS Center for Integrated Data Analytics. It is used by trained and vetted personnel to record information about habitats on coastal beaches and he environment surrounding them.
Below are news stories associated with this project.
Below are partners associated with this project.
- Overview
The Sea-Level Rise Hazards and Decision-Support project assesses present and future coastal vulnerability to provide actionable information for management of our Nation’s coasts. Through multidisciplinary research and collaborative partnerships with decision-makers, physical, biological, and social factors that describe landscape and habitat changes are incorporated in a probabilistic modeling framework to explore the future likelihood of a variety of impacts and outcomes. Scenario-based products and tools can be applied to inform adaptation strategies, evaluate tradeoffs, and examine mitigation options.
Although the general nature of the changes that can occur on ocean coasts in response to sea-level rise (SLR) is widely recognized, it is difficult to predict exactly what changes may occur, or when they may occur. The ability to predict the extent of these changes is limited by uncertainties in both currently available data that describe the coastal environment, as well as gaps in understanding of some of the driving processes that contribute to coastal change (e.g., rate and magnitude of sea level rise, changes in storminess). Additionally, the cumulative impacts of physical and biological change on the quantity and quality of coastal habitats are not well understood, and potential societal responses to SLR are uncertain. Nonetheless, coastal managers need actionable information to make decisions that account for future hazards, including SLR.
This project brings together scientists from the disciplines of geology, hydrology, geography, biology, and ecology to synthesize information on coastal environments to address the effects of SLR on our Nation’s coasts. The approach uses a probabilistic framework, which allows researchers to incorporate observations and account for uncertainties, to evaluate the likelihood of a variety of SLR impacts, including:
- land loss from inundation and erosion,
- migration of coastal landforms,
- changes to groundwater systems, and
- changes to coastal habitat.
Decision makers depend on the future coastal environment having certain characteristics. For example, homeowners desire a home that is at low risk of loss due to coastal erosion. Local planners and managers also need to be able to identify infrastructure that could be at risk to make effective long-term adaptation or mitigation decisions. Land managers may target parcels for acquisition that provide critical habitat for threatened and endangered species. Flora and fauna require specific habitat attributes to survive and flourish. To proactively plan for an uncertain future, decision makers need the ability to consider alternative response measures and assess the benefits and costs of options. Consequently, there is a need to develop decision frameworks that combine detailed and sometimes complicated scientific information in a way that improves the ability to translate it into decision making scenarios.
Conceptual diagram demonstrating how Bayesian networks used in this project incorporate data and knowledge to provide predictions with decision-support applications. Learn more Probabilistic Framing
The Bayesian statistical framework is ideal for using data sets derived from historical or modern observations such as long-term shoreline change or wetland accretion/elevation trends. This information can be combined with model simulations and used to define the relationships between key variables in coastal environments. A Bayesian network provides a means of integrating these data to evaluate competing hypotheses regarding the relationships between forcing factors (e.g., rate of SLR, suspended sediment concentration, elevation change) and responses (e.g., shoreline change, wetland vertical accretion, water table change). This framework allows scientists to make probabilistic predictions of the future state of coastal environments for outcomes such as shoreline change, wetland survival, and changes in the depth to groundwater. The predictions also have estimates of outcome uncertainty that can be expressed as both numbers (e.g., 90%) and words (e.g., very likely). The ability to communicate SLR impacts in terms of a probabilistic prediction can improve scientists’ ability to support decision making and evaluate specific management questions about alternatives for addressing SLR.
- Science
Below are other science projects associated with this project.
Probabilistic Framing
The Bayesian statistical framework is ideal for using data sets derived from historical or modern observations such as long-term shoreline change or wetland accretion/elevation trends. This information can be combined with model simulations and used to define the relationships between key variables in coastal environments.Sea Level Change
An interactive guide to global and regional sea level rise scenarios for the United States.Estuarine Processes, Hazards, and Ecosystems
Estuarine processes, hazards, and ecosystems describes several interdisciplinary projects that aim to quantify and understand estuarine processes through observations and numerical modeling. Both the spatial and temporal scales of these mechanisms are important, and therefore require modern instrumentation and state-of-the-art hydrodynamic models. These projects are led from the U.S. Geological...Coastal Landscape Response to Sea-Level Rise Assessment for the Northeastern United States
As part of the USGS Sea-Level Rise Hazards and Decision-Support project, this assessment seeks to predict the response to sea-level rise across the coastal landscape under a range of future scenarios by evaluating the likelihood of inundation as well as dynamic coastal change. The research is being conducted in conjunction with resource managers and decision makers from federal and state agencies...Beach-dependent Shorebirds
Policy-makers, individuals from government agencies, and natural resource managers are under increasing pressure to manage changing coastal areas to meet social, economic, and natural resource demands, particularly under a regime of sea-level rise. Scientific knowledge of coastal processes and habitat-use can support decision-makers as they balance these often-conflicting human and ecological...National Assessment of Coastal Vulnerability to Sea Level Rise
The original national coastal vulnerability index (CVI) assessment was motivated by expected accelerated sea-level rise (SLR) and the uncertainty in the response of the coastline to SLR. This research was conducted between 1999 and 2001, and is currently being updated using new data sources and methodology. This original study was part of the National Assessment of Coastal Change Hazards project.Empowering decision-makers: A dynamic web interface for running Bayesian networks
U.S. Geological Survey (USGS) scientists are at the forefront of research that is critical for decision-making, particularly through the development of models (Bayesian networks, or BNs) that forecast coastal change. The utility of these tools outside the scientific community has been limited because they rely on expensive, technical software and a moderate understanding of statistical analyses. WRelative Coastal Vulnerability Assessment of National Park Units to Sea-Level Rise
The National Park Service (NPS) is responsible for managing nearly 12,000 km (7,500 miles) of shoreline along oceans and lakes. In 2001 the U.S. Geological Survey (USGS), in partnership with the NPS Geologic Resources Division, began conducting hazard assessments of future sea-level change by creating maps to assist NPS in managing its valuable resources. This website contains results of the... - Multimedia
- Publications
Below are publications associated with this project.
Probabilistic patterns of inundation and biogeomorphic changes due to sea-level rise along the northeastern U.S. Atlantic coast
ContextCoastal landscapes evolve in response to sea-level rise (SLR) through a variety of geologic processes and ecological feedbacks. When the SLR rate surpasses the rate at which these processes build elevation and drive lateral migration, inundation is likely.ObjectivesTo examine the role of land cover diversity and composition in landscape response to SLR across the northeastern United States.AuthorsErika Lentz, Sara L. Zeigler, E. Robert Thieler, Nathaniel G. PlantFilter Total Items: 18Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model
Understanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern U.S. by exploring several data inputs and outcomes. Using elevation and land cover datasets, we determine where datAuthorsErika Lentz, Nathaniel G. Plant, E. Robert ThielerSmartphone technologies and Bayesian networks to assess shorebird habitat selection
Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. However, studies of habitat selection can be time consuming and expensive over broad spatial scales, and a lack of standardized monitoring targets or methods can impede the generalization of site-based studies.AuthorsSara L. Zeigler, E. Robert Thieler, Benjamin T. Gutierrez, Nathaniel G. Plant, Megan Hines, James D. Fraser, Daniel H. Catlin, Sarah M. KarpantyGlobal and regional sea level rise scenarios for the United States
The Sea Level Rise and Coastal Flood Hazard Scenarios and Tools Interagency Task Force, jointly convened by the U.S. Global Change Research Program (USGCRP) and the National Ocean Council (NOC), began its work in August 2015. The Task Force has focused its efforts on three primary tasks: 1) updating scenarios of global mean sea level (GMSL) rise, 2) integrating the global scenarios with regional fAuthorsW. Sweet, R.E. Kopp, C.P. Weaver, J Obeysekera, Radley M. Horton, E. Robert Thieler, C. ZervasSmartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability
Understanding and managing dynamic coastal landscapes for beach-dependent species requires biological and geological data across the range of relevant environments and habitats. It is difficult to acquire such information; data often have limited focus due to resource constraints, are collected by non-specialists, or lack observational uniformity. We developed an open-source smartphone applicationAuthorsE. Robert Thieler, Sara L. Zeigler, Luke Winslow, Megan Hines, Jordan S. Read, Jordan I. WalkerEvaluation of dynamic coastal response to sea-level rise modifies inundation likelihood
Sea-level rise (SLR) poses a range of threats to natural and built environments1, 2, making assessments of SLR-induced hazards essential for informed decision making3. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30 × 30 m resolutionAuthorsErika E. Lentz, E. Robert Thieler, Nathaniel G. Plant, Sawyer R. Stippa, Radley M. Horton, Dean B. GeschCoupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network
Predictions of coastal evolution driven by episodic and persistent processes associated with storms and relative sea-level rise (SLR) are required to test our understanding, evaluate our predictive capability, and to provide guidance for coastal management decisions. Previous work demonstrated that the spatial variability of long-term shoreline change can be predicted using observed SLR rates, tidAuthorsNathaniel G. PlantUsing a Bayesian network to predict barrier island geomorphologic characteristics
Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncAuthorsBenjamin T. Gutierrez, Nathaniel G. Plant, E. Robert Thieler, Aaron TurecekEvaluating coastal landscape response to sea-level rise in the northeastern United States: approach and methods
The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, inclAuthorsErika E. Lentz, Sawyer R. Stippa, E. Robert Thieler, Nathaniel G. Plant, Dean B. Gesch, Radley M. HortonUsing a Bayesian Network to predict shore-line change vulnerability to sea-level rise for the coasts of the United States
Sea-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 erosionAuthorsBenjamin T. Gutierrez, Nathaniel G. Plant, Elizabeth A. Pendleton, E. Robert ThielerA Bayesian network approach to predicting nest presence of thefederally-threatened piping plover (Charadrius melodus) using barrier island features
Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat chAuthorsKatherina D. Gieder, Sarah M. Karpanty, James D. Fraser, Daniel H. Catlin, Benjamin T. Gutierrez, Nathaniel G. Plant, Aaron M. Turecek, E. Robert ThielerEffects of sea-level rise on barrier island groundwater system dynamics: ecohydrological implications
We used a numerical model to investigate how a barrier island groundwater system responds to increases of up to 60 cm in sea level. We found that a sea-level rise of 20 cm leads to substantial changes in the depth of the water table and the extent and depth of saltwater intrusion, which are key determinants in the establishment, distribution and succession of vegetation assemblages and habitat suiAuthorsJohn P. Masterson, Michael N. Fienen, E. Robert Thieler, Dean B. Gesch, Benjamin T. Gutierrez, Nathaniel G. PlantBridging groundwater models and decision support with a Bayesian network
Resource managers need to make decisions to plan for future environmental conditions, particularly sea level rise, in the face of substantial uncertainty. Many interacting processes factor in to the decisions they face. Advances in process models and the quantification of uncertainty have made models a valuable tool for this purpose. Long-simulation runtimes and, often, numerical instability makeAuthorsMichael N. Fienen, John P. Masterson, Nathaniel G. Plant, Benjamin T. Gutierrez, E. Robert Thieler - Web Tools
Below are data or web applications associated with this project.
Sea Level Change
An Interactive Guide to Global and Regional Sea Level Rise Scenarios for the United States
Coastal Change Hazards Portal
Interactive access to coastal change science and data for our Nation’s coasts. Information and products are organized within three coastal change hazard themes: 1) extreme storms, 2) shoreline change, and 3) sea-level rise. Displays probabilities of coastal erosion.
ByNatural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, Pacific Coastal and Marine Science Center, St. Petersburg Coastal and Marine Science Center, Woods Hole Coastal and Marine Science Center, Gulf of Mexico, Hurricane Dorian, Hurricane Harvey, Hurricane Ian, Hurricane Irma, Hurricane Isaias, Hurricane Jose, Hurricane Laura, Hurricane Marco, Hurricane Maria, Hurricane Matthew, Hurricane Michael, Hurricane Nate, Hurricane Sandy, Hurricanes - Software
Below are software products associated with this project.
bi-transect-extractor
This package is used to calculate coastal geomorphology variables along shore-normal transects. The calculated variables are used as inputs for modeling geomorphology using a Bayesian Network (BN).
iPlover
iPlover was developed by the U.S. Geological Survey Woods Hole Coastal and Marine Science Center and the USGS Center for Integrated Data Analytics. It is used by trained and vetted personnel to record information about habitats on coastal beaches and he environment surrounding them.
- News
Below are news stories associated with this project.
- Partners
Below are partners associated with this project.