Crowdsourced Data Improves Flood Forecasting

Citizen science hydrologic data may be beneficial in enhancing models that forecast stream flow. Hydrogeologist Chris Lowry at the University of Buffalo founded the initiative CrowdHydrology in 2011 which uses crowdsourcing to gather hydrologic data. Lowry is now attempting to use the data gathered from CrowdHydrology into the National Water Model to possibly predict floods in the United States.The National Water Model simulates and forecasts stream flow in every stream in the USA, making it possible to use data which models the conditions of these streams to predict potential floods. Lowry’s work is also part of a larger project at Arizona State University which is attempting to improve forecasting by using information gathered from social media, traffic cameras and webcams with the end goal to use accurate real-time data to connect first responders and citizens (read more here).