Integrated Ocean, Earth, and Atmospheric Observations for Resilience Planning in Hampton Roads, Virginia

AbstractBuilding flood resilience in coastal communities requires a precise understanding of the temporal and spatial scales of inundation and the ability to detect and predict changes in flooding. In Hampton Roads, the Intergovernmental Pilot Project's Scientific Advisory Committee recommended...

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Veröffentlicht in:Marine Technology Society journal 2018-03, Vol.52 (2), p.68-83
Hauptverfasser: Loftis, Jon Derek, Mitchell, Molly, Atkinson, Larry, Hamlington, Ben, Allen, Thomas R., Forrest, David, Updyke, Teresa, Tahvildari, Navid, Bekaert, David, Bushnell, Mark
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Sprache:eng
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Zusammenfassung:AbstractBuilding flood resilience in coastal communities requires a precise understanding of the temporal and spatial scales of inundation and the ability to detect and predict changes in flooding. In Hampton Roads, the Intergovernmental Pilot Project's Scientific Advisory Committee recommended an integrated network of ocean, earth, and atmospheric data collection from both private and public sector organizations that engage in active scientific monitoring and observing. Since its establishment, the network has grown to include monitoring of water levels, land subsidence, wave measurements, current measurements, and atmospheric conditions. High-resolution land elevation and land cover data sets have also been developed. These products have been incorporated into a number of portals and integrated tools to help support resilience planning. Significant challenges to building the network included establishing consistent data standards across organizations to allow for the integration of the data into multiple, unique products and funding the expansion of the network components. Recommendations to the network development in Hampton Roads include the need to continue to support and expand the publicly available network of sensors; enhance integration between ocean, earth, and atmospheric networks; and improve shallow water bathymetry data used in spatial flooding models.
ISSN:0025-3324
1948-1209
DOI:10.4031/MTSJ.52.2.8