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GRANIT Coastal Data Viewer

Lead Agency:
NH GRANIT, UNH Earth Systems Research Center

Primary Contact:
David Justice

Start Date - End Date
10/01/2012 - 09/30/2013

Description:
The GRANIT System is developing an interactive, web-based data viewer to host and disseminate a number of spatially explicit data sets covering coastal NH and extending into southern Maine. The viewer will incorporate data and tools that are specifically focused on past and potential future climate change impacts, including the following two recently-released (and/or recently updated) data sets:

  • Water depth grids. Wake, C., et al. 2011. Climate Change in the Piscataqua/Great Bay Region: Past, Present, and Future. The data sets resulting from this effort include the spatial extent of 6-foot, 9-foot, and 12-foot increases in water depths above mean higher high water in southern NH/Maine. The project report does not predict any specific depth, but describes a suite of flooding scenarios that could result from a combination of factors (sea level rise, storm surge, etc.)
  • Great Bay Estuary eelgrass. Short, F., 1981, and 1986-2011. The data sets show the locations where eelgrass was observed in the Great Bay Estuary for each year, based on low-altitude aerial surveillance.

The viewer will incorporate a number of additional data sets that provide context for the climate change impacts data series. These will include core base vector data sets (e.g. political boundaries, roads, surface water features, etc.), various topographic derivatives from a recent coastal LiDAR data collection effort, and current, high resolution imagery.

Development will be based on the ArcGIS Viewer for Flex framework and ESRI's ArcGIS for Server services, allowing us to leverage much of the effort implemented in the multi-purpose GRANITView map viewer. Key to the new viewer will be functionality which will harness the capabilities of a slider control to animate the primary data layers noted above. This will allow users to visualize either water depth (for inundation data) or year (for the eelgrass data series). By viewing these animated layers in concert with other pertinent data sets, local and regional stakeholders will have a powerful, decision-relevant analytical capability.



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