# Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA
## 1 Introduction
Additionally, the model's predictive acumen can be enhanced via improved calibration of assumptions, such as: (1) Better friction parameterization of different land cover types, (2) improved aerial elevation estimates of occluded roadway overpasses, and (3) identification of tidally-susceptible subterranean drainage infrastructure junctions (where tidal waters can enter city streets several blocks from the water's edge). Thus, quality assurance of flood validation data near these fine-scale features can become valuable model improvement assets through the proper training of a citizen scientist network [16]. Through technological progression, many effective methods for mapping inundation and flood depths have been developed using GPS, photo tagging, Augmented Reality (AR) image landmark recognition, and Quick Response (QR) codes [7; 11; 13]. Naturally, the emergence and growing necessity of smart phones in modern living has popularized the prominence of these recording methods. Additionally, the ease of access afforded by mobile applications for making insurance claims, verifying flooding for municipal government attention, and greater scientific aspirations has increased the intrinsic value of personal flood mapping [17; 18]. Thus, flood-observing mobile applications, like \"MyCoast\" [19] and \"Sea Level Rise\" [12], or crowdsourcing web data geo-forms, like those implemented at the state [20; 21], country [22; 23], and international level [24], have emerged for myriad resiliency purposes. Typically, these applications exist to verify claims of flooding, validate flood forecast models, or inform long-term flood planning efforts [19; 20; 21; 22; 23; 24]. Mobile flood mapping platforms and applications have recently become information repositories that provide a living data archive of flood observation data with sufficient recording frequency and data density in urban areas where flooding is prevalent [25]. However, these tools have been shown to be of less utility in rural coastal areas, where statistically, less people are present and motivated to vigilantly monitor inundation, and where enhanced-GPS signal strength is diminished due to less reliable cellular broadband coverage [26]. Yet, over time, these data sets can even become their own autonomous data-driven flood prediction models via sea level trend extrapolation when combined with Digital Elevation Models (DEM) [16]. Thus, high-resolution street-scale hydrodynamic models have recently found a new way to validate their predictions, and a cost-effective method for correcting erroneous elevation assumptions from aerial lidar surveys. This includes occluded areas in heavily canopied flood-prone areas and built infrastructure, such as box culverts, highway overpasses, and bridges that impact proper hydrologic drainage in flooding conditions [27]. A proactive and safe way to leverage these technological advances in citizen science flood monitoring methods without waiting for a major storm to elucidate inaccurate model assumptions is to map the incidence of \"nuisance flooding.\" This approach takes advantage of mapping inundation in places where it frequently occurs with minimal danger to the reporter, and can identify issues with modeled flood forecasts without waiting for a major tropical or extratropical storm event to identify them first [12]. Hampton Roads, VA, USA experiences tidal nuisance flooding 12 to 18 times a year [28]. This is a frequency that amounts to no less than one cumulative week per year that low-lying streets in the region are inundated [29; 30]. This chronic flooding fatigue can make it easy to forget that intermittent tidal flooding events cost cities and their residents time and money [30; 31]. Of these tidal inundation events, the highest astronomical tide of the year has become known as the king tide [20]. While not a scientific term, a king tide is a name that refers to an exceptionally high tide, without the consideration of atmospheric amplification from wind or waves [21; 23].