# Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA
## 4 Discussion  
As citizen scientists contribute significant amounts of their time to collect these intricate geospatial data sets, care is taken by the custodians of those data to perform quality assurance and quality control on those data before subjecting them to rigorous scientific analysis. The raw volunteer data for each _Catch the King_ survey in 2017 and 2018 were modified after initial statistics were reported to filter out and otherwise minimize bias in this study. This was in an effort to provide the most meaningful model validation statistics, which were reported in the results section, and were honed to validate three important factors for model validation: Duration, depth, and degree of inundation:  
**Duration**  
1. Points with a reported timestamp more than an hour outside of the time window in which the king tide occurred at that location were not included in the comparison. 2. Those points within the window were rounded to the nearest hour to split them into comparative groups for each hourly model output for comparison (as depicted in Figure 8). **Depth**  
3. Surveyed points were merged with the topobathymetric DEM used to build the model, developed by the USGS and published in [45], to append elevation values. 4. Any points >0.91 m (3 ft) elevation above the North American Vertical Datum of 1988 (NAVD88) were not included in the model comparison, as the king tide from neither year exceeded this height at any water level sensor in the region (Figure 7). **Degree**  
5. Points with an appended photograph were collected back away from the water's edge (for land marking and visual perspective), and thus were not included as part of the flood contour comparison. If included, these would over-predict the degree of flooding. 6. Points with a radial accuracy metric >10 m (32 ft) were too inaccurate to include, as they misrepresent the degree an area was inundated, favoring over-prediction (Figure 10). The extent that duration of inundation was addressed and timing of when a flood event will arrive dictates the potential mitigating actions that may be taken. Tidal inundation events can easily be predicted through harmonic algorithms, and hydrodynamic models can improve upon this by informing citizen scientists, community planners, and emergency managers alike when the flood waters will arrive. This information is useful for personal preparation of one's home and assets that may be in low lying areas, route planning and guidance for personal and emergency response vehicles, and for scheduling road closures to minimize vehicular loss. Figures 4 and 8 illustrate the difference an hour makes in terms of accuracy on model validation, and in the future, the recommendation for more frequent spatial mapping has already been recommended for the future development of Tidewatch Maps to eventually shift to 30 min time steps for the online time-aware layers for depictingmore temporally-resolute flood mapping beyond hourly updates. However, presently, Tidewatch Maps are used to map all of Virginia's coastal floodplain via SCHISM's model outputs at a 1 to 5 m resolution (depending upon the accuracy of lidar point spacing for the model's underlying digital elevation assumptions). A 36-h Tidewatch forecast already consists of 37 state-wide coastal flood maps being automatically produced every 12 h. Thus, doubling that number to 72 iterative Tidewatch Maps per cycle is both computationally expensive for the model's post-processing, and impractical for users loading its flood forecasts via the web without newer technology to enhance loading times. Since users have most frequently accessed Tidewatch Maps using their smart phones to view its flood predictions during periods of significant power and internet outages, greater temporal resolution for 30-min update intervals is not likely to be implemented soon, as additional loading times are even more cumbersome for mobile devices.