Village-based flood prediction with WATERVERSE - Localising digitalisation to empower the residents of Etteln.

<p dir="ltr">We live in a time of increasing weather unpredictability. Weather events are becoming both more extreme and more frequent. In our area of expertise, flooding, we’re increasingly talking to regions and towns that used to have grainy black and white photos of a single extreme flood from the last 50 years to now having videos of the last bad flood of the season. Whilst weather prediction and associated flood prediction technologies have improved greatly, they still tend operate on a macro-spatial level, providing broad warnings for relatively large areas. Conversely, the University of Exeter’s (UNEXE) ‘CA Flood Pro’ application ​[1]​ has been developed to provide highly detailed, on a street-by-street basis, flooding simulations, but they are time consuming to run. For communities at risk of flooding, accuracy and timeliness of prediction are key. Therefore, for a CA Flood Pro based flooding simulation to be of value, it must provide quality predictions quickly. The WATERVERSE ​[2]​ project has given us the opportunity to integrate CA Flood Pro flood prediction application into the Water Data Management Ecosystem (WDME), along with sources of historic, current, and predicted precipitation, and ground condition data, to provide Etteln, a small town in Germany, with highly localised and up-to-date flood predictions.</p><p dir="ltr">This paper was presented at the 21st Computing and Control in the Water Industry Conference (CCWI 2025) at the University of Sheffield (1st - 3rd September 2025).</p>