# Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA
## 1 Introduction  
Inherently, hydrodynamic models are best validated with water level sensors, due to the precision afforded by defining the timing and depth of inundation at a location in an automated manner [1; 2; 3; 4]. As a result of decreased technological costs, low-cost low-energy networks of water level sensors leveraging the Internet of Things (IoT) are beginning to dramatically densify the flood data available in urban environments in coastal areas throughout the world [5; 6]. Hampton Roads, VA, USA, hosts one of these IoT networks called StormSense. The network functions as a flooding resiliency partnership between the Virginia Commonwealth Center for Recurrent Flooding Resiliency (CCRFR) and several coastal cities in Hampton Roads [7]. The network's primary goal is to monitor and transmit automated flooding alerts in real time when inundation occurs [8; 9] However, an additional function of these sensors is the integration with federal sensor data from the US National Oceanic and Atmospheric Administration (NOAA) and the US Geological Survey (USGS) to validate and improve the Virginia Institute of Marine Science's (VIMS) flood forecast models [7; 8; 9]. However, when it comes to logistical considerations and the high cost of maintenance involved in deploying traditional _in situ_ or remote water level observing systems, these factors can limit sensor density when even finer scale data are needed, and therefore impede these systems' ability to accurately monitor fine-scale environmental conditions [8; 10]. In recent years, the combination of youth who are increasingly globally connected to the internet, and a growing population of retired professionals, poses an opportunity to create a wide-ranging and diverse network of citizen scientists with the capacity to span multiple societal themes [11; 12]. Citizen science is public participation in conducting scientific research by non-professional scientists, typically following some form of informal training on data collection. While not a panacea for all inundation monitoring needs, citizen scientists can augment and enhance traditional research and monitoring. Their interest and engagement in flooding resiliency issues can markedly increase spatial and temporal frequency along with an effective duration of sampling. This can reduce time and labor costs, provide hands-on science, technology, engineering, and mathematics (STEM) learning related to real-world issues, and increase their public awareness and support for the scientific process. Naturally, a lack of sufficient professional oversight in citizen science endeavors can introduce caveats to overcome before wide-scale inclusion in an established coastal observing system, yet progress in this underutilized resource is promising [4]. First seeing significant adoption in the US in the aftermath of 2012 Hurricane Sandy, citizen science flood monitoring efforts first became useful through mobile phone pictures capturing inundation with a time-indexing landmark in view, such as a clock tower or local bank clock [13; 14]. These pictures gave credence to the digital medium with the advent of enhanced-GPS, which leverages the Global Positioning System's (GPS) satellite constellation along with nearby cell towers to better triangulate a user's position on the ground. These tools have now begun to rival the utility of government-sponsored post-event flood monitoring efforts, such as the USGS' high water marks [15]. While the latter approach affords confidence for model validation through a trusted agency for superior accuracy, the former possesses a greater capacity to document everywhere flooding occurs, with the inherent risk of potentially less accurate validation data. Regardless, collection of data at the local scale in public spaces where flooding is prevalent, such as streets, public right-of-way access spaces, and parks, can improve model prediction by properly resolving flow around small-scale features in the built environment [12].