Disy (Germany)

Abstract. To address the increasing threat of heavy rainfall events the FloReST (Urban Flood Resilience – Smart Tools) project focuses on the identification and prediction of emergency flow paths in urban areas and enhancing risk management and communication. The project developed a Citizen Science app to document and report pluvial flood risks in urban areas due to the fact that conventional flood protection has so far focused mainly on fluvial hazards. The population is involved in sustainable risk minimisation through the active use of the app and local risk awareness. Additionally, the app includes educational functions through a user guidance on categorizing pluvial hazards and taking preventive measures. The app is complemented by a Geo Data Warehouse, which enables authorities to analyse and visualise the data transmitted by users via customisable dashboards. Although there are still some technical limitations, such as limited offline functionality and inaccuracies in the use of Global Navigation Satellite Systems, these may be addressed in future research. Thanks to its open-source design, the system remains scalable and can be adapted to other regions worldwide. Overall, the Citizen Science app and Geo Data Warehouse form an innovative, participatory tool that improves the resilience of cities to climate change through inclusive, data-driven risk management.

Crop nitrogen (N) demand is defined by various physiological, management and environmental factors that interact with each other and involve temporal and spatial dynamics. Variable rate N application (VRNA) intends to address this but the underlying algorithms often remain rather rigid and deterministic, are partly subject to high uncertainties and largely leave the agronomic expert’s knowledge and experience unnoticed. A novel generic system architecture was conceptualized to overcome these limitations and respond to the complexity of N management in a straightforward and both, reactive and proactive manner. Having a fuzzy expert system as methodical core, the approach mainly relies on human input to grasp the circumstances at a specific N application and address the required parameter interactions. As a holistic concept, it further aims at a high versatility in terms of considered input data and utilization with different sensor and application technology, as well as a digitized VRNA process chain involving graphical user interfaces to simplify procedures for data presentation, decision making, application and documentation. Bringing the presented concept into a prototypic implementation considering real-time crop N sensor and mapped soil data, its consistency was verified. At the same time, potential functionalities, as well as limitations were illustrated and technical requirements on specific subsystems were clarified by the prototype. The possible risks that stand in the way of high benefits due to a strong focus on expert knowledge can be countered by using digital tools and heading towards hybridization with other VRNA approaches.