Coastal Vulnerability Viewer

Area of Interest

Select the portion of Coastal Connecticut. The selection can be a town, COG or custom.
N: °
W: ° E: °
S: °

Assessment Type

Contributor Layers
Vulnerability Layers
Exposure, Sensitivity & Adaptive Capacity
Weighted Vulnerability

Contributor Layer Selection

Select an contributor layer to examine.

Vulnerability Layer Selection

Select a vulnerability layer to examine.

Layer Selection

Select an exposure, sensitivity & adaptive capacity layer to examine.

Analysis Options

Explore the tradeoffs yourself. Select an option.

Base Layer

Satellite Map
Street Map
Topographic Map

View Options

Add sea-level overlay.
Change color scheme.

Coastal Vulnerability Assessment

This viewer examines the direct and indirect future impacts of sea level rise on Connecticut's coastal towns using an index based spatial model. It aims to guide stakeholders to understand the vulnerable regions and their underlying indicators to prioritize resiliency projects.

Cite this viewer as: Balleisen, Z., Onat, Y., Massidda, C., Fake, T. and O'Donnell, J. (2020)."Coastal Vulnerability Index Viewer", University of Connecticut, Connecticut Institute for Resilience and Climate Adaptation, Retrieved from Last accessed:XX/XX/XXXX.


This information is provided with the understanding that it is not guaranteed to be correct or complete and conclusions drawn from such information are the sole responsibility of the user. Attempts have been made to ensure that this data or documentation is accurate and reliable; The University of Connecticut, nor the Department of Marine Sciences, does not assume liability for any damages caused by inaccuracies in this data or documentation, or as a result of the failure of the data or software to function in a particular manner. The University of Connecticut, nor the CIRCA, makes no warranty, expressed or implied, as to the accuracy, completeness, or utility of this information, nor does the fact of distribution constitute a warranty.

Methodology and Sources

The collected and gridded data layers are ordered from their highest to lowest values in the coastal boundary. The values are divided into five quantiles (0-20% represents the lowest 20% of the dataset, whereas 80-100% represents the highest quantile of data). Each quantile is assigned a rank value to relatively allocate the vulnerability of the layer to the climate hazard (0-20% is assigned to 1 (very low), similarly, 80-100% is assigned to 5(very high)). Thus, the contributor layers are created.