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Article

Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA

1
Center for Coastal Resources Management, Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, VA, 23062, USA
2
Department of Physical Sciences, Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, VA 23062, USA
3
Tides that Bind LLC, Norfolk, VA 23517, USA
4
Wetlands Watch, Norfolk, VA 23517, USA
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2019, 7(8), 242; https://doi.org/10.3390/jmse7080242
Received: 1 May 2019 / Revised: 28 June 2019 / Accepted: 12 July 2019 / Published: 26 July 2019
Changes in the eustatic sea level have enhanced the impact of inundation events in the coastal zone, ranging in significance from tropical storm surges to pervasive nuisance flooding events. The increased frequency of these inundation events has stimulated the production of interactive web-map tracking tools to cope with changes in our changing coastal environment. Tidewatch Maps, developed by the Virginia Institute of Marine Science (VIMS), is an effective example of an emerging street-level inundation mapping tool. Leveraging the Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM) as the engine, Tidewatch operationally disseminates 36-h inundation forecast maps with a 12-h update frequency. SCHISM’s storm tide forecasts provide surge guidance for the legacy VIMS Tidewatch Charts sensor-based tidal prediction platform, while simultaneously providing an interactive and operationally functional forecast mapping tool with hourly temporal resolution and a 5 m spatial resolution throughout the coastal plain of Virginia, USA. This manuscript delves into the hydrodynamic modeling and geospatial methods used at VIMS to automate the 36-h street-level flood forecasts currently available via Tidewatch Maps, and the paradigm-altering efforts involved in validating the spatial, vertical, and temporal accuracy of the model.
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Keywords: hydrodynamic; modeling; sea level rise; mobile application; app; crowdsourcing; SCHISM; Tidewatch; StormSense; Catch the King hydrodynamic; modeling; sea level rise; mobile application; app; crowdsourcing; SCHISM; Tidewatch; StormSense; Catch the King
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Figure 1

  • Externally hosted supplementary file 1
    Doi: https://doi.org/10.25773/276h-2b45
    Link: https://scholarworks.wm.edu/data/406/
    Description: Catch the King Tide GPS data points were collected by volunteers to effectively breadcrumb their path tracing the tidal high water contour lines by pressing the 'Save Data' button in the free Sea Level Rise Mobile App every few steps along the water's edge during the high tide on the morning of November 5th, 2017.
MDPI and ACS Style

Loftis, J.D.; Mitchell, M.; Schatt, D.; Forrest, D.R.; Wang, H.V.; Mayfield, D.; Stiles, W.A. Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA. J. Mar. Sci. Eng. 2019, 7, 242. https://doi.org/10.3390/jmse7080242

AMA Style

Loftis JD, Mitchell M, Schatt D, Forrest DR, Wang HV, Mayfield D, Stiles WA. Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA. Journal of Marine Science and Engineering. 2019; 7(8):242. https://doi.org/10.3390/jmse7080242

Chicago/Turabian Style

Loftis, Jon D., Molly Mitchell, Daniel Schatt, David R. Forrest, Harry V. Wang, David Mayfield, and William A. Stiles 2019. "Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA" Journal of Marine Science and Engineering 7, no. 8: 242. https://doi.org/10.3390/jmse7080242

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