Research

Water temperature in rivers is a key factor for the ecological status of aquatic ecosystems and faces growing pressures from climate change, but also from thermal discharges. Existing thermal load models, developed over a decade ago under the climatic and hydrological conditions of that time, raise the question of whether their core assumptions still hold, particularly with regard to shifting seasonal temperature and discharge patterns. Using the Salzach/Inn as a case study, the SENSI-SAI project examines how discharge and water temperature conditions have changed since the original modeling, and builds on this to develop a sensitivity analysis for critical combinations of low discharge and high water temperature. The existing thermal sensitivity between air and water temperature is reassessed using updated time series and scaled to current climate scenarios. In addition, the relevance of glacier retreat for the seasonal discharge dynamics of alpine catchments is contextualized through a literature review. The goal is a robust risk assessment of whether and under what conditions ecological temperature thresholds may be reached or exceeded in the future, along with a well-founded recommendation for action for the client.

The project investigates the influence of climate variability and climate change on nitrate dynamics in groundwater across Austria. Its objective is to quantify the interactions between climate, land use, agricultural management and hydrogeological conditions, and to determine the contribution of climatic drivers to groundwater nitrate contamination. Particular emphasis is placed on identifying delayed system responses (legacy effects) and assessing how droughts, heavy precipitation and compound climate extremes affect nitrate mobilisation and transport. The study is based on a unique nationwide dataset comprising long-term groundwater monitoring records, high-resolution climate data and spatial land-use information. An integrated methodological framework combining climate indices, statistical analyses and machine learning techniques will be developed to identify the relationships between climatic drivers and groundwater nitrate concentrations and to predict future changes under different climate scenarios. The project addresses a critical knowledge gap regarding the role of climate in controlling groundwater quality. It will develop predictive models, quantify time lags between land-surface drivers and groundwater responses, and classify groundwater bodies according to their sensitivity to climatic stressors. The results will be translated into spatial vulnerability maps identifying areas that are particularly susceptible to climate-induced changes in groundwater quality. The project will provide a scientific basis for assessing climate-related risks to groundwater quality and for prioritising groundwater monitoring and management measures. The main target groups are water authorities, water resource managers, drinking water suppliers and the scientific community, providing evidence-based decision support for the long-term protection and sustainable management of groundwater resources.

The UN Decade of Action on Cryospheric Sciences aims to reduce uncertainties regarding the causes and effects of changes in snow, ice, and permafrost. Snow is a major contributor to these uncertainties due to the incomplete understanding and representation of snow processes in climate models. SnowShifts seeks to significantly enhance our comprehension of snow volume and mass, their non-linear dynamics and properties and, specifically, snow regime shifts within the Earth and climate systems. This will be achieved through a novel combination of in-situ and remote sensing observations, along with high-end numeric model developments. Innovative technologies such as photon-counting space-borne laser altimeters, terrestrial superconducting gravimeters, and novel scale-bridging approaches and downscaling of satellite gravimetry in extreme snow environments will be integrated with the latest multi-sensor satellite data and modelling. This will enable the retrieval of snow volume, mass, extent, and other properties, such as snow albedo, at unprecedented spatio-temporal resolutions. The combined observational and modelling advancements will define and describe trends and shifts in snow climates ranging from high-latitude polar regions to mid-latitude high-mountain ranges. This project will provide future snow scenarios for at least three socio-economic pathways refining current global and regional climate models, exploring and defining potential non-linearities in snow changes. SnowShifts will not only demonstrate the accuracy of our new models in reproducing snow quantities and dynamics but will also generate extensive datasets characterising snow in remote and understudied regions. The new representations of snow processes will be a major outcome permitting the scientific community to incorporate the knowledge gained into climate and land surface models, and to replace in parts computationally expensive model components with machine learning emulators.