Research

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.

The aim of the study is a consistent estimation of a wide range of low-flow characteristics at observed and unobserved river sites in Austria. For this purpose, novel regionalisation models are to be developed and evaluated with regard to their predictive performance at unobserved sites. A nested model is proposed as the model structure, which takes into account low flow (NQ) of different time scales (year, season, month, minimum observed value) in hierarchical form, in order to obtain consistent estimates of derived mean characteristic values and extreme value statistics. The regionalisation aims at a consistent estimation of natural low flows and uses a data set of at most slightly anthropogenically influenced daily flow series with more than 40 years of observations.