Project Details
Description
Worldwide freshwater resources are under increasing pressures of rapidly intensifying climate change effects putting the availability
and quality of water resources and socio-economic developments at risk. River basin organisations need to be prepared.
STARS4Water aims at improving the understanding of climate change impacts on water resources availability and the vulnerabilities
for ecosystems, society and economic sectors at river basin scale. STARS4Water will develop and deliver new data services and datadriven
models for better supporting the decision making on planning on actions for adaptative, resilient and sustainable
management of fresh water resources. The project team will work with seven river basin organisations through a co-creation, living
lab approach. The new services and models will be co-designed with stakeholders to meet their needs on data and information,
ensuring relevance and uptake for use beyond the lifetime of the project.
The STARS4Water project includes two distinctive elements: first, the need for an international stakeholder community to address the
stakeholders’ needs and requirements and second, the development and application of innovative data and model concepts. New
datasets and models offer possibilities for improved projections on water resources availability, and the new insights on links
between water, nature, society ask for a broader set of indicators to be considered in decision-making on water management. These
novel datasets, models and indicators are not yet fully matured and integrated in current river basin management information tools
and decision-making processes. We acknowledge that these elements are of a different nature, being a stakeholder-driven approach
and rather science-(data-)driven in the application of novel data and models, respectively. It is the consortium’s firm conviction that
for substantial progress in climate change adaptation the two elements need to be combined.
| Status | Active |
|---|---|
| Effective start/end date | 01.10.2022 → 30.09.2026 |
Fields of science
- 101031 Approximation theory
- 102 Computer Sciences
- 305901 Computer-aided diagnosis and therapy
- 102033 Data mining
- 101029 Mathematical statistics
- 102032 Computational intelligence
- 101028 Mathematical modelling
- 102013 Human-computer interaction
- 305905 Medical informatics
- 101027 Dynamical systems
- 101004 Biomathematics
- 101026 Time series analysis
- 202017 Embedded systems
- 101024 Probability theory
- 305907 Medical statistics
- 102019 Machine learning
- 202037 Signal processing
- 202036 Sensor systems
- 102018 Artificial neural networks
- 103029 Statistical physics
- 202035 Robotics
- 106005 Bioinformatics
- 106007 Biostatistics
- 101019 Stochastics
- 101018 Statistics
- 101017 Game theory
- 101016 Optimisation
- 102001 Artificial intelligence
- 101015 Operations research
- 102004 Bioinformatics
- 101014 Numerical mathematics
- 102003 Image processing
JKU Focus areas
- Digital Transformation
-
Antic: Adaptive Neural Temporal In-situ Compressor
Cranganore, S. S., Bodnar, A., Galleti, G., Paischer, F. & Brandstetter, J., 10 Apr 2026, arXiv, 31 p. (arXiv.org; no. 2604.09543).Research output: Working paper and reports › Preprint
Open Access -
Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples
Sanchez-Fernandez, A., Pinetz, T., Zellinger, W. & Klambauer, G., 22 Apr 2026, arxiv.org, 24 p. (arXiv.org; no. 2604.20824).Research output: Working paper and reports › Preprint
Open Access -
gyaradax: Local Gyrokinetics JAX Code
Galletti, G., Volkmann, E. & Brandstetter, J., 07 Apr 2026, arXiv, 20 p. (arXiv.org; no. 2604.06085).Research output: Working paper and reports › Preprint
Open Access