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snippet: Displays shoreline change data in various formats for long term time horizons (~1880s - 2006).
summary: Displays shoreline change data in various formats for long term time horizons (~1880s - 2006).
extent: [[-73.6703531790025,40.9848074819217],[-71.8115335622424,41.4293120905196]]
accessInformation: CT DEEP, UCONN, CT SeaGrant, NOAA, USGS
thumbnail: thumbnail/thumbnail.png
typeKeywords: ["Data","Service","Map Service","ArcGIS Server"]
description: Shorelines are continuously moving in response to winds, waves, tides, sediment supply, changes in relative sea level, and human activities. Shoreline changes are generally not constant through time and frequently switch from negative (erosion) to positive (accretion) and vice versa. Cyclic and non-cyclic processes change the position of the shoreline over a variety of timescales, from the daily and seasonal effects of winds and waves, to changes in sea level over a century to thousands of years. The shoreline "rate of change" statistic thus reflects a cumulative summary of the processes that altered the shoreline for the time period analyzed. Long-term rates of shoreline change were determined by fitting a least squares regression line to all shoreline positions from the earliest (1880s) to the most recent (2006). The rate of change is the slope of the regression line. Negative values indicate erosion (movement of the shoreline away from the established baseline) and positive values indicate accretion (movement of the the shoreline towards the established baseline.) The calculation of linear regression rates requires a minimum of three shoreline years at each transect, and rates calculated with many shoreline positions can increase confidence by reducing potential errors associated with the source data, and fluctuating short-term changes (Dolan et al.and others, 1991). The linear regression method for determining shoreline change rates assumes a linear trend of change among the shoreline dates. However, in locations where shoreline change rates have not remained constant through time, a linear trend would not exist. For example, a shoreline may exhibit accretion over the first 100 years, but in later years, the shoreline may shift to an erosional trend. In these cases, it is expected that using a linear fit to the data is poorer, and as a result the uncertainty asociated with these shoreline change rates is higher than those whose trend is more linear.
licenseInfo:
catalogPath:
title: CT Shoreline Long Term Change
type: Map Service
url:
tags: ["Shoreline","Connecticut"]
culture: en-US
name: Shoreline_Change_Long_Term
guid: B8824D77-3AE9-4F21-9C12-9850C92ABACA
spatialReference: WGS_1984_Web_Mercator_Auxiliary_Sphere