# Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA
## 3 Results  
Spatial data collected for each king tide event were aggregated through the Sea Level Rise mobile app and shared online using interactive web maps, so that volunteers with minimal GIS experience could visualize their own GPS observations populate the Tidewatch model's predictions in near-real time (Figure 6). This level of data accountability implemented an open interaction where users could conduct their own analyses and make their own mean difference calculations through ArcGIS Online's distance and area measuring tools from their data in a web browser while viewing the public map. This data interactivity spurred high engagement and participation for students involved in STEM research or related educational classes. Figure 6A shows an aggregated point map of 59,718 high water marks superposed with the Tidewatch Maps throughout the greater Hampton Roads region and  
Figure 6: (**A**) Overview of study region in Hampton Roads, VA, featuring the 2017 king tide maximum inundation forecast from the Tidewatch Map in blue, GPS citizen science observations as blue dots, and water level sensors from the Tidewatch Charts as red dots. (**B**) Inset shows a high-density concentration of flood validation data in the historic Hague region of Norfolk, VA, USA, where the model had favorable agreement with the citizen science observations. Here, the model yielded a 94% spatial match, and a 4.2 m MHDD. See the map online: [[http://arcg.is/1](http://arcg.is/1) HLOPS]([http://arcg.is/1](http://arcg.is/1) HLOPS). showcases the extent of areas not covered by automated sensors that were surveyed through _Catch the King_ in 2017 (Table 1). Comparatively, 2018 surveyed an even broader area than was monitored in 2017, but with less density (Figure 4). For example, Figure 6B shows Norfolk's Hague, where thousands of GPS data were collected in both years. In areas where a significant point density is reported, data points can form more than simple flood contours when combined with digital elevation survey data. With sufficient point density, one can form their own observation-driven interpolated flood model for comparison with hydrodynamic simulation results. In this case, the areal extent of The Hague shown in Figure 6B yielded a 94% match with the raster polygon built from the interpolated GPS points, with the Tidewatch model slightly erring on the side of over-prediction. The cursory overview of the Greater Hampton Roads Region shown in Figure 6 shows the Tidewatch Map in blue, GPS citizen science observations as aggregated blue dots, which render and dis-aggregate based upon the zoom level, and water level sensors from the Tidewatch Charts as red dots. This legend theme will persist through the next several spatial comparison figures, and was designated through the Sea Level Rise app for the observation data, and for the model via a meeting of the CCRFR with emergency managers [44]. Given that flood impacts for king tides are simply tidal calibration data that are likely to be aligned along similar elevation contours without intervening atmospheric conditions, linear distance metrics can be useful to compute spatial differences in relatively flat areas. The standard distance formula may then be computed by GIS software to calculate the difference between each GPS data point to the nearest predicted inundated space. To compute this, the modeled geospatial flood depths served through VIMS' Tidewatch Maps were converted into vector data polygons, with the maximum flood extent representing the 0 m flood contour. As the volunteers were instructed to map inundation in their communities by dropping time-stamped digital GPS breadcrumbs, the citizen scientists' data should ideally represent the observed GPS flood extents, and in most places, the model had an overwhelmingly favorable agreement.