# Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA
## 5 Conclusions
In each spatial validation effort, less than 9% of the surveyed data were excluded from the analysis. Lessons learned from citizen science surveys have improved the model through cost-effective hydrologic correction of mission conduits for fluid flow. These were identified by filtered GPS observations that the model missed in its initial automated forecast, but were corrected in hindcast, in preparation for the next significant inundation event. Errors in hydro-correction did not relate to errors in friction parameterization of the model, but were more associated with flow pathways that were occluded from aerial lidar surveys. These areas included bridges, culverts, and stormwater drainage systems without tidal backflow prevention valves, which formed artificial dams in the digital surface model embedded in the forecasted Tidewatch Maps. Many of these identified areas have been corrected and have recently been used alongside the successful model validation in Hampton Roads to expand the forecast area of the Tidewatch Maps beyond southeast Virginia to include the entire coastal zone of Virginia in 2019. As king tides are currently simply nuisance floods, which primarily inundate streets and driveways without significantly damaging infrastructural assets, the issues are presently geared towards traffic and transportation issues. Common concerns from citizen scientists involved in the _Catch the King_ mapping events involved concerns regarding whether their vehicle could be safely street parked or if their vehicles needed to be safely moved into a garage during king tides. Others questioned whether they should take an alternate route to work or school or the store due to potential street flooding. As technology progresses, these questions will become more prevalent as we aim to ascertain whether modern route guidance mobile applications will be intelligent enough to account for intermittent inundation, or unintentionally lead vehicles down flooded streets simply because there is no traffic detected on them while an adjacent elevated street is congested. Some navigation applications, such as Waze, have aimed to crowdsource all road hazard data through their \"Connected Citizens\" program,but this method is only a temporary solution, as a model cannot currently automate road hazard flags for flooded locations where those particular app users are not or have not logged data. Naturally, adeptly answering these questions becomes increasingly difficult once self-driving vehicles are involved. Thus, the outcomes of inundation modeling efforts for this tidal calibration effort will more significantly be realized once this trained citizen scientist army is deputized into post-hurricane surveys. Since 2011 Hurricane Irene was the last hurricane to significantly impact Virginia's Hampton Roads region, the Tidewatch automated mapping model has yet to demonstrate widespread accuracy amidst a significant inundation event since the Sea Level Rise app's advent in 2014. The goal is to continue to improve the model with each _Catch the King_ tidal calibration and train volunteers so they will be aware of where to find the latest flood forecast information, and how to collect meaningful flood validation data. Thus, this monitoring coordination approach with hydrodynamic modeling provided a novel procedural release of information to depict predicted maximum inundation extents for expediently effective model validation through the use of an overwhelming quantity of quality event data with relatively low risk to volunteer citizen scientists. Conceptualization, J.D.L., H.V.W., D.M., and W.A.S. ; experiments conduction, J.D.L. and D.R.F. ; formal analysis, J.D.L., D.S. ; writing--original draft preparation, J.D.L. and M.M. ; writing--review and editing, J.D.L. VIMS' flood modeling research for this study was funded by the CCRFR.