Abstract
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.
Highlights
Hydrodynamic models are best validated with water level sensors, due to the precision afforded by defining the timing and depth of inundation at a location in an automated manner [1,2,3,4]
The network’s primary goal is to monitor and transmit automated flooding alerts in real time when inundation occurs [8,9] an additional function of these sensors is the integration with federal sensor data from the US National Oceanic and Atmospheric Administration (NOAA) and the US Geological Survey (USGS) to validate and improve the Virginia Institute of Marine Science’s (VIMS) flood forecast models [7,8,9]
These Tidewatch forecast maps were benchmarked in Hampton Roads by >100,000 Global Positioning System (GPS)-reported high water marks collected by citizen scientists during two king tide flooding events occurring in 2017 and 2018
Summary
Hydrodynamic models are best validated with water level sensors, due to the precision afforded by defining the timing and depth of inundation at a location in an automated manner [1,2,3,4]. Reliable inundation prediction depends upon accurate simulation of large-scale inundation of the tidal long wave during a king tide to successfully propagate from the ocean, through the continental shelf, estuarine systems, into creeks, and city streets, and rigorous conservation of fluid momentum and mass as flood waters permeate the built environment These Tidewatch forecast maps were benchmarked in Hampton Roads by >100,000 GPS-reported high water marks collected by citizen scientists during two king tide flooding events occurring in 2017 and 2018. What follows is a description of: (1) A citizen science flood mapping project called Catch the King based in Hampton Roads, VA, (2) effective volunteer training methods using cell phones to provide meaningful GPS observations for effective model validation, (3) hydrodynamic modeling approaches used for expediently simulating and publicly mapping near-term inundation, along with (4) a summary of the results. The resulting maps shown in the results and discussion sections present these dense data maps surveyed during the 2017 and 2018 king tide inundation events, and present the mean horizontal distance difference (MHDD) comparative spatial calculations between the modeled maximum flood extent contours and citizen science flood validation data sets for each king tide flood event, followed by the lessons learned
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