Due to climate change, extreme weather events, such as localised flooding and heavy rainfall, are becoming increasingly common. Early warning systems for floods and heavy rainfall serve to minimise the effects of extreme weather events as a climate adaptation measure. A timely warning enables affected municipalities to take appropriate measures and thus protect life and property. The forecast enables early planning of evacuations and rescue measures. Local authorities currently use flood hazard maps and heavy rain hazard maps to analyse and assess risks. The problem is that the maps only provide information about potential hazards and therefore do not predict any specific hazards in real time. An early warning system, on the other hand, provides real-time information, warns of specific hazardous situations and takes weather and environmental conditions into account. New technologies in the field of the Internet of Things and AI-based analyses offer opportunities for the development of a real-time early warning system. To this end, a pilot project was launched in the small town of Steinheim/Westphalia in the district of Höxter to develop a flood information system: Seven water level sensors and one precipitation sensor were installed along the Emmer and Heubach rivers in Steinheim. Their measurement data is transmitted via gateways using the LoRaWAN standard to a server at the Fraunhofer Institute in Lemgo. There, the data is stored, processed and visualised web-based via a dashboard. This solution was developed in co-operation with the town of Steinheim, the municipal energy grid operator Westfalen Weser Netz and the Fraunhofer Institute in Lemgo.