Research

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.

Droughts and low-flows are significant hydrological and environmental hazards that threaten a wide range of water-related sectors, such as navigation, hydropower production, and water management in general. Under climate change scenarios, the increasing risk of severe and persistent low-flow events will lead to rising costs for economy and society. Improved forecasting of low-flows for lead times of 1 to 6 months would be vital for many sectors, as it would allow for a more proactive water management. Streamflow forecasting is common in areas as North-America, Africa or Australia, but less frequent in Europe. Here the links to atmospheric modes are weaker, which poses a particular challenge. Seasonal forecasting of low-flow is even rarer, although hydrological drought has the virtue of being a slowly evolving process that is not substantially influenced by short-term precipitation events and is therefore likely easier to predict. For Austria, a seasonal low flow forecasting is completely missing. Our proposed project aims to fill this gap, by developing a probabilistic seasonal low-flow forecasting framework for five main river basins in Austria (Danube, Inn, Salzach, Drau, Mur). The study area was selected in initial communication with stakeholders to increase the societal impact of the study. The approach is innovative in many aspects: • It evaluates different spatial and temporal aggregates of the predictor variables (e.g. soil moisture, groundwater) and their value for forecast accuracy. • It develops bias-adjusted and downscaled climate forecasts and assess their relative performances with respect to meteorological variables and the added value for low-flow forecasting over various lead times. • It assesses the value of simpler single-site data-driven models compared with complex multi-site data-driven space-time models. • It combines process-based with data-driven models to explore relative merits that help to improve the models. • It evaluates the user value for stakeholders with particular emphasis on navigation and hydropower production. The results of the proposed project will directly feed into the emerging needs of the water sector due to climate change for critical infrastructure such as hydropower production, transport, navigation or water quality-related issues. A probabilistic forecasting framework for low-flow would enable water managers to act with foresight, increase society’s resilience to droughts and reduce the economic costs of this hydrological hazard. The outcome of this study will not only be beneficial for society and economy, but will have a significant impact to advance scientific knowledge, by: i. Improving seasonal climate forecasts for Austria. ii. Quantifying the user value of seasonal low-flow forecasting for Austria. iii. Advancing methods and knowledge about complex space-time statistical models and their relative merits compared to single-site forecasting schemes.

This research project is a contribution to the project: Adaptation strategies to climate change for Austria's water management: Follow-up study 2024. The contribution consists of trend analyses for floods, surface water supply and low flows, and includes literature studies, method comparison and development, and applications for approx. 800 gauging stations in Austria. Different trend analyses, which take into account serial autocorrelation of discharges, are to be evaluated comparatively and an overall statement is to be synthesised. The research question is to what extent the discharge in Austria has changed in the course of climate change and direct anthropogenic influence, and a quantification of the changes by region, season and catchment size.