Synoptic Drought Monitoring: A Multivariable Framework for Water Cycle and Impact Integration

Early warning systems for extreme events, particularly droughts, are essential for protecting the economy, society, and the environment. Recent droughts have caused economic losses, social challenges such as unemployment and migration, and food shortages, especially in Europe, highlighting the urgent need for effective monitoring and forecasting. Despite advances in drought monitoring, most research continues to treat drought primarily as a hazard, without sufficiently considering its impacts or the underlying water cycle components that drive them. Moreover, droughts often interact with other extremes, such as heatwaves, amplifying their effects and further challenging environmental sustainability. The SynoDryMFW project addresses this gap by developing a framework that integrates drought impacts, main drivers of water cycle components, and interactions with other extreme events. The framework will forecast droughts based on impacts and main drivers in a spatiotemporal context in diverse study area (Austria catchments), with potential applicability worldwide. A hybrid data-driven modeling approach combining statistical methods with machine learning and deep learning will overcome the “black box” limitations of AI models. By shifting from hazard-based to impact-based drought monitoring, this project will deliver an innovative early warning system that links drivers, impacts, and risks. The result will support stakeholders and decision-making on adopting to drought events mitigating the risk associated with many severe weather events.