# Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA
## 5 Conclusions  
A large-scale flood monitoring citizen science data collection effort was used to favorably validate an automated browser-based flood mapping service driven by a cross-scale hydrodynamic model predicting storm tide inundation in coastal Virginia, USA. The operational modeling effort for predicting tidal flooding can be mapped using multiple methods, yet the most effective method was found to be the automated implementation of a street-level hydrodynamic model. The Tidewatch Maps implemented  
Figure 14: Citizen Science flood extent observations aided in hydrologic correction for an ephemeral stream feeding Wolfsnare Creek in Virginia Beach, VA. (**A**) Citizen scientists mapped the tidal inundation extent approximately an hour before the king tide’s peak. (**B**) Hydrologic correction fixed the lidar-derived DEM to permit flow through the small box culvert beneath the bridge to enhance the model’s spatial accuracy via better estimation of cross-sectional flow and volume conservation. Figure 13: Conceptualization of macro-roughness features in an urban environment resolved within the street level inundation forecasts. In the example shown in (**A**), a bridge artificially obstructs flow from passing through to the hydrodynamic model grid cell below the one shown. With hydrologic correction provided by the citizen scientists via _Catch the King_ (**B**) shows the grid opened, where a nearby flood contour can be extracted and applied to translate the inundation underneath the bridge and open flow to the opposite side, no longer impeding fluid flow. by the Virginia Institute of Marine Science (VIMS) leveraged their SCHISM hydrodynamic model with inputs of: atmospheric wind and pressure data, tidal harmonic predictions at the open boundary, and prevailing ocean current inputs, such as the Gulf Stream. This information was successfully computed from a large scale model and translated to the street-level via SCHISM's computationally efficient non-linear solvers, and semi-implicit numerical formulations aided by a sub-grid geometric mesh with embedded lidar elevations. Validation in the vertical scale found that the SCHISM model outputs via the Tidewatch web mapping platform compared well in Hampton Roads among the 32 extant water level sensors during the highest astronomical tide of the year on 5 November 2017, a king tide, yielding an aggregate RMSE of 3.5 cm. The region expanded its sensor base to 48 through an IoT sensor project, StormSense, to compare well again during the king tide on 27 October 2018, resulting in an RMSE of 3.7 cm. Horizontal validation was aided by time-stamped GPS flood extent data collected by citizen scientists through the world's largest environmental survey (in terms of the most contributions in the least amount of time), _Catch the King_. The citizen science flood mapping survey was established in Hampton Roads in 2017 and recruited volunteers through local, print, and social media outlets. The survey's organizers then trained the citizen scientists in the use of the free Sea Level Rise mobile flood mapping application at frequently inundated public spaces in the months leading up to each king tide event. The citizen scientists' flood monitoring data formed time-indexed GPS breadcrumbs to form contours that were successfully aggregated and compared with the maximum inundation extents of the same time interval from VIMS' Tidewatch Maps. The data were filtered to minimize bias attributed to errors related to observing flooding duration, depth, and degree. Once the _Catch the King_ survey data were filtered for these three things, it was found that the Tidewatch Map comparisons on 5 November 2017 had an overall mean horizontal distance difference of 5.9 m (19.3 ft). The model comparison with the observations collected during the king tide on 27 October 2018 were found to be less favorable, yielding an average distance deviation of 6.2 m (24.6 ft), likely attributed to the winds from the mild nor'easter that occurred in the hours leading up to the event.