Relationships 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 the sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern US by exploring several data inputs and outcomes. Using elevation and land cover datasets, we determine where data error is likely, quantify its effect on predictions, and evaluate its influence on prediction confidence. Results show data error is concentrated in low-lying areas with little impact on prediction skill, as the inherent correlation between the datasets can be exploited to reduce data uncertainty using Bayesian inference. This suggests the approach may be extended to regions with limited data availability and/or poor quality. Furthermore, we verify that model sensitivity in these first-order landscape change assessments is well-matched to larger coastal process uncertainties, for which process-based models are important complements to further reduce uncertainty.</p

Evaluating 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, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online

Chalk cliff retreat in East Sussex and Kent 1870s to 2001

The retreat of chalk cliffs fringing the eastern English Channel contributes shingle to the beaches which helps to protect the cliffs and slow down erosion. Conversely, cliff retreat endangers settlements and infrastructure on the clifftop. Rates of retreat have been calculated by a variety of methods over the past century, but no attempt has been made to provide a complete coverage that allows for a true comparison of retreat rates over the entire coastline. Using historic maps and recent orthophotos, cliff retreat rates have been calculated for consecutive 50 m sections of chalk cliff along the English side of the entire eastern English Channel for a period of 125 years. The chalk cliffs of East Sussex erode at an average rate of 0.25 - 0.3 m y−1 while those in Kent at a rate of 0.1 m y−1

Temporal shoreline series analysis using GNSS

In recent decades, Boa Viagem beach located in the city of Recife-PE and Piedade in Jaboatão dos Guararapes-PE (Brazil) has seen urbanization near the coastline causing changes in social, economic and morphological aspects, where coastal erosion problems are observed. This study uses GNSS (global navigation satellite system) shoreline monitoring approach, which is quicker, and provides continuously updatable data at cm-level accuracy to analyze and determine temporal positional shifts of the shoreline as well as annual average rates through EPR (end point rate). To achieve this, kinematic GNSS survey data for the years 2007, 2009, 2010 and 2012 were used. The results show sectorial trends over the years, with the highest annual retreat rate of 8.16 m /year occurring during the period 2007-2009. Variety of different patterns over the shoreline were also observed. These findings could be essential for decision making in coastal environments

Synthesis and Assessment Product

This document is part of the Synthesis and Assessment Products described in the U.S. Climate Change Science Program (CCSP) Strategic Plan. The U.S. Government's CCSP is responsible for providing the best science-based knowledge possible to inform management of the risks and opportunities associated with changes in the climate and related environmental systems. To support its mission, the CCSP has commissioned 21 "synthesis and assessment products" (SAPs) to advance decision making on climate change-related issues by providing current evaluations of climate change science and identifying priorities for research, observation, and decision support. This Synthesis and Assessment Product (SAP), developed as part of the U.S. Climate Change Science Program, examines potential effects of sea-level rise from climate change during the twenty-first century, with a focus on the mid-Atlantic coast of the United States. Using scientific literature and policy-related documents, the SAP describes the physical environments; potential changes to coastal environments, wetlands, and vulnerable species; societal impacts and implications of sea-level rise; decisions that may be sensitive to sea-level rise; opportunities for adaptation; and institutional barriers to adaptation

Climate change and cultural heritage : a landscape vulnerability framework

This paper proposes a new framework for calculating vulnerability indices within archaeological resource management on a landscape-scale. Current approaches consider archaeological sites in isolation from their context within the historic landscape. The new framework advocated in this article assesses the vulnerability of landscape character areas, as defined through historic landscape characterisation. This framework uses a two-step vulnerability index: the first assesses the vulnerability of archaeological sites and landscape features; the second uses the results of the first vulnerability index, as well as spatial data on the landscape character areas and the threat in question to calculate the vulnerability of each landscape character area. The framework is applied to a brief case study in coastal North Wales, UK

Birth of the Modern Chesapeake Bay Estuary Between 7.4 and 8.2 Ka and Implications for Global Sea-Level Rise

Two major pulses of sea-level rise are thought to have taken place since the last glacial maximum – meltwater pulses (mwp) 1A (12 cal ka) and 1B (9.5 cal ka). Between mwp 1B and about 6 cal ka, many of the complex coastal ecosystems which ring the world’s oceans began to form. Here we report data for rhenium, carbon isotopes, total organic carbon, and fossil oysters from Chesapeake Bay which span the transition from fresh to brackish water conditions in the bay in the mid- Holocene. These data constrain sea-level change and resulting environmental change in the bay. They indicate that the transition was rapid, and that it was produced by (1) a third pulse of rapid eustatic sea-level rise, or (2) a geometry of the prehistoric Chesapeake Bay basin which predisposed it to a nonlinear response to a steadily rising sea level. Similar nonlinear changes in vulnerable coastal environments are likely to take place in the future due to polar warming, regardless of the timing or rate of sea-level rise