This research uses state-of-the-art observations, numerical models, and model-data assimilation techniques to better understand their cumulative effect on coastal change.
Coastal change is driven by processes that vary significantly in both space and time. Beach and shoreline evolution occur due to seasonal changes in summer/winter wave environments, extreme storm events, changes in natural sand supply and transport, alongshore variations in coastal geomorphology (cliffs, sandy beaches, vegetated marshes, engineered vs. non-engineered coastlines) and elevated water levels caused by long-term sea-level rise.
The complexity of coastal change lies, in part, in understanding how these processes interact with each other to shape the Nation's evolving coastal landscape. As part of the National Assessment of Coastal Change Hazards Project (NACCH), the USGS is actively researching methods to integrate the numerous coastal processes that drive and impact coastal change. The goal is to combine advances from the individual research tasks within the NACCH Project (storms, shoreline change and sea-level rise) using state-of-the-art observations, numerical models, and model-data assimilation techniques to better understand their cumulative effect on coastal change.
Objectives
- Develop a generalized methodology to predict short- and long-term coastal change (shoreline and cliff positions and trends) that can be applied at a National scale.
- Identify the coastal change resulting from the cumulative impact of extreme storms versus long-term processes including sea-level rise.
- Relate varying scales of coastal change to the oceanographic processes that drive erosion and accretion.
- Determine the dominant interactions between sandy shorelines, bluffs and cliffs, and varying geomorphic characteristics that impact coastal change.
Below are research tasks and science projects associated with this project.
National Assessment of Coastal Change Hazards
Forecasting Coastal Change
National Assessment of Coastal Vulnerability to Sea Level Rise
Long-Term Coastal Change
Below are publications associated with this project.
Coupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network
Extended Kalman Filter framework for forecasting shoreline evolution
Predicting coastal cliff erosion using a Bayesian probabilistic model
Below are data or web applications associated with this project.
iCoast
Help scientists at the U.S. Geological Survey annotate aerial photographs with keyword tags to identify changes to the coast after extreme storms.
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.
- Overview
This research uses state-of-the-art observations, numerical models, and model-data assimilation techniques to better understand their cumulative effect on coastal change.
Coastal change is driven by processes that vary significantly in both space and time. Beach and shoreline evolution occur due to seasonal changes in summer/winter wave environments, extreme storm events, changes in natural sand supply and transport, alongshore variations in coastal geomorphology (cliffs, sandy beaches, vegetated marshes, engineered vs. non-engineered coastlines) and elevated water levels caused by long-term sea-level rise.
The complexity of coastal change lies, in part, in understanding how these processes interact with each other to shape the Nation's evolving coastal landscape. As part of the National Assessment of Coastal Change Hazards Project (NACCH), the USGS is actively researching methods to integrate the numerous coastal processes that drive and impact coastal change. The goal is to combine advances from the individual research tasks within the NACCH Project (storms, shoreline change and sea-level rise) using state-of-the-art observations, numerical models, and model-data assimilation techniques to better understand their cumulative effect on coastal change.
Schematic describing a methodology to integrate short-term (e.g. measured or modeled wave forcing; left panel) and long-term (e.g. decadal shoreline positions from Ocean Beach, California; middle panel) processes to generate predictions of coastal change and uncertainty around future conditions (right panel). The methodology follows the data assimilation approach developed in Long and Plant, 2012. (Public domain.) Spatial and temporal scales associated with examples of hydrodynamic (blue) and morphodynamic (tan) coastal processes. The dashed box represents the range of scales typically relevant to coastal planners and managers. (Public domain.) Objectives
- Develop a generalized methodology to predict short- and long-term coastal change (shoreline and cliff positions and trends) that can be applied at a National scale.
- Identify the coastal change resulting from the cumulative impact of extreme storms versus long-term processes including sea-level rise.
- Relate varying scales of coastal change to the oceanographic processes that drive erosion and accretion.
- Determine the dominant interactions between sandy shorelines, bluffs and cliffs, and varying geomorphic characteristics that impact coastal change.
- Science
Below are research tasks and science projects associated with this project.
National Assessment of Coastal Change Hazards
Research to identify areas that are most vulnerable to coastal change hazards including beach and dune erosion, long-term shoreline change, and sea-level rise.Forecasting Coastal Change
This project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. The overall objective is to improve real-time and scenario-based predictions of coastal change to support management of coastal infrastructure, resources, and safety.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.Long-Term Coastal Change
Goals of this task include developing and improving coastal-change assessments and supporting long-term planning and decision making to ensure sustainable coastal economies, infrastructure, and ecosystems. - Publications
Below are publications associated with this project.
Coupling 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, tidExtended Kalman Filter framework for forecasting shoreline evolution
A shoreline change model incorporating both long- and short-term evolution is integrated into a data assimilation framework that uses sparse observations to generate an updated forecast of shoreline position and to estimate unobserved geophysical variables and model parameters. Application of the assimilation algorithm provides quantitative statistical estimates of combined model-data forecast uncPredicting coastal cliff erosion using a Bayesian probabilistic model
Regional coastal cliff retreat is difficult to model due to the episodic nature of failures and the along-shore variability of retreat events. There is a growing demand, however, for predictive models that can be used to forecast areas vulnerable to coastal erosion hazards. Increasingly, probabilistic models are being employed that require data sets of high temporal density to define the joint pro - Web Tools
Below are data or web applications associated with this project.
iCoast
Help scientists at the U.S. Geological Survey annotate aerial photographs with keyword tags to identify changes to the coast after extreme storms.
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.