Modeling Salt Panne Land-Cover Suitability under Sea-Level Rise

2016 ◽  
Vol 321 ◽  
pp. 1116-1125 ◽  
Author(s):  
Anna C. Linhoss ◽  
William V. Underwood
Keyword(s):  
Land Cover ◽  
Sea Level Rise ◽  
Sea Level

scholarly journals Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

2019 ◽  
Vol 7 (2) ◽  
pp. 429-438 ◽  
Author(s):  
Erika E. Lentz ◽  
Nathaniel G. Plant ◽  
E. Robert Thieler
Keyword(s):  
Land Cover ◽  
Sea Level Rise ◽  
Sea Level ◽  
Poor Quality ◽  
Data Availability ◽  
Data Uncertainty ◽  
Coastal Process ◽  
Prediction Confidence ◽  
Data Error ◽  
Landscape Variability

Abstract. 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.

scholarly journals Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

2018 ◽  
Author(s):  
Erika E. Lentz ◽  
Nathaniel G. Plant ◽  
E. Robert Thieler
Keyword(s):  
Land Cover ◽  
Sea Level Rise ◽  
Sea Level ◽  
Poor Quality ◽  
Data Availability ◽  
Data Uncertainty ◽  
Coastal Process ◽  
Prediction Confidence ◽  
Data Error ◽  
Landscape Variability

Abstract. 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 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.

scholarly journals Dynamic Land Use and Coastline Changes in active Estuarine Regions – A Study of Sundarban Delta

2014 ◽  
Vol XL-8 ◽  
pp. 133-139 ◽  
Author(s):  
J. V. Thomas ◽  
A. Arunachalam ◽  
R. K. Jaiswal ◽  
P. G. Diwakar ◽  
B. Kiran
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
Sea Level Rise ◽  
Sea Level ◽  
Remote Sensing Data ◽  
Shoreline Change ◽  
Long Term Effects ◽  
Change Analysis ◽  
Multi Temporal

Alteration of natural environment in the wake of global warming is one of the most serious issues, which is being discussed across the world. Over the last 100 years, global sea level rose by 1.0–2.5 mm/y. Present estimates of future sea-level rise induced by climate change range from 28 to 98 cm for the year 2100. It has been estimated that a 1-m rise in sea-level could displace nearly 7 million people from their homes in India. The climate change and associated sea level rise is proclaimed to be a serious threat especially to the low lying coastal areas. Thus, study of long term effects on an estuarine region not only gives opportunity for identifying the vulnerable areas but also gives a clue to the periods where the sea level rise was significant and verifies climate change impact on sea level rise. Multi-temporal remote sensing data and GIS tools are often used to study the pattern of erosion/ accretion in an area and to predict the future coast lines. The present study has been carried out in the Indian Sundarbans area. Major land cover/ land use classes has been delineated and change analysis of the land cover/ land use feature was performed using multi-temporal satellite images (Landsat MSS, TM, ETM+) from 1973 to 2010. Multivariate GIS based analysis was carried out to depict vulnerability and its trend, spatially. Digital Shoreline change analysis also was attempted for two islands, namely, Ghoramara and Sagar Islands using the past 40 years of satellite data and validated with 2012 Resourcesat-2 LISS III data.

scholarly journals Future Coastal Population and Ecosystem Exposure to Sea Level Rise Along the European Coastline

2020 ◽  
Vol 1 (5) ◽  
Author(s):  
Shrinidhi Ambinakudige
Keyword(s):  
Land Cover ◽  
Sea Level Rise ◽  
Sea Level ◽  
Sea Water ◽  
Climate Change Scenarios ◽  
Coastal Regions ◽  
Land Cover Types ◽  
Sea Level Anomalies ◽  
Significant Area ◽  
Global Sea Level

The average global sea level has been predicted to rise anywhere between 0.53–2.5 m by 2100 with some local and regional variations in various climate change scenarios. Relative sea level change along most of the European coastline is similar to the global average. The objective of this paper is to estimate the extent of impact regarding three sea level rise (SLR) scenarios on European coastal regions. First, three inundation models estimate the area affected by the base sea level, 1 m SLR, and 2 m SLR. Then, based on the population and land cover classes in the coastal regions, land cover types and the estimated future population affected by the SLR scenarios are analyzed. This study used an inundation model (EU-DEM v1.1 digital elevation model). Land cover data from CLC2018 and monthly averaged sea level anomalies (SLA) files from 2013 to 2015 were used in the model. In the SLR0 scenario, about 8.7 million people are estimated to be affected at 2100. An estimated 11.6 million people will be affected in the SLR1 scenario; and an estimated 14.8 million people will be affected by the SLR2 scenarios. Arable lands and pastures are the two top land cover classes that will be affected by SLR. However, land under urban fabric and transportation are also two important land cover types affected by SLR which can induce major economic costs to coastal countries. A significant area of underwater bodies and wetlands will come in contact with sea water due to the extreme events caused by SLR.

scholarly journals Probabilistic patterns of inundation and biogeomorphic changes due to sea-level rise along the northeastern U.S. Atlantic coast

2020 ◽  
Author(s):  
Erika E. Lentz ◽  
Sara L. Zeigler ◽  
E. Robert Thieler ◽  
Nathaniel G. Plant
Keyword(s):  
Land Cover ◽  
Sea Level Rise ◽  
Sea Level ◽  
Atlantic Coast ◽  
Lateral Migration ◽  
Coastal Landscape ◽  
Land Cover Types ◽  
Coastal Landscapes ◽  
Landscape Response ◽  
Ecological Feedbacks

Abstract Context Coastal 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. Objectives To examine the role of land cover diversity and composition in landscape response to SLR across the northeastern United States. Methods Using an existing probabilistic framework, we quantify the probability of inundation, a measure of vulnerability, under different SLR scenarios on the coastal landscape. Resistant areas—wherein a dynamic response is anticipated—are defined as unlikely (p < 0.33) to inundate. Results are assessed regionally for different land cover types and at 26 sites representing varying levels of land cover diversity. Results Modeling results suggest that by the 2050s, 44% of low-lying, habitable land in the region is unlikely to inundate, further declining to 36% by the 2080s. In addition to a decrease in SLR resistance with time, these results show an increasing uncertainty that the coastal landscape will continue to evolve in response to SLR as it has in the past. We also find that resistance to SLR is correlated with land cover composition, wherein sites containing land cover types adaptable to SLR impacts show greater potential to undergo biogeomorphic state shifts rather than inundating with time. Conclusions Our findings support other studies that have highlighted the importance of ecological composition and diversity in stabilizing the physical landscape and suggest that flexible planning strategies, such as adaptive management, are particularly well suited for SLR preparation in diverse coastal settings.

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