The Digital Waters Consortium is a Finnish flagship and PhD pilot uniting scientists, engineers, and policymakers to co-develop architectures, models, and methods that enable a comprehensive digital twin of the hydrosphere for data driven, sustainable water management.
Our mission is to make water science and management reproducible, scalable, and generalizable through open-source technologies, transparent data practices, and stakeholder-driven innovation.
- Learn Digital Waters architecture and models
Visit the training page to learn about our:
- Data models and storing your data in the DIWA Data Lake
- Developing models that are interoperable with Digital Waters models and data pipelines.
- Standards for reproducible repository management.
- And more!
- Access the DIWA Data Lab
In development Digital Waters Flagship and Pilot researchers can access our Virtual Research Lab
- Share and publish data on the DIWA Data Lake
In development, email diwa-vre@lists.oulu.fi to register Register new sensors or add research data to the DIWA Data Lake
- Explore our repositories
Visit our public repositories to discover ongoing projects.
- Get assistance with finding, processing, or modelling data
Participate in our GitHub Discussions to get community support navigating technical issues in research computation. This board is moderated by staff computer scientists and environmental data/software specialists.
- Contribute
- Review our Contributing Guidelines
- Open issues, propose features, or submit pull requests
- Collaborate on digital water management use cases
We welcome collaborations on regional and thematic digital twin demonstrators.
- Report issues or bugs with DIWA platforms and software
Report an issue, bug, or error with DIWA software, platforms (like the VRE), and models.
1. Co-develop architectures and models to support a comprehensive, scalable, source-to-sea digital twin of the hydrosphere
- Build modular, interoperable, generalizable architectures for data-driven hydrologic digital twins.
- Integrate real-time sensor networks and 4D (3D + time) observations with physical models using data assimilation pipelines and AI-driven components.
- Promote FAIR (Findable, Accessible, Interoperable, Reusable) and open science principles in water-resources management.
- Utilize OGC Standards
- Adhere to the European Interoperability Framework
- Foster cloud-native, containerized, and distributed modeling frameworks
- Develop reusable digital assets (data, models, APIs, visualization tools)
- Encourage collaboration across disciplines — from IoT devices to networks to hydrology to policy
We build upon open architectures, including:
- Data and metadata interoperability (OGC standards)
- Data & model interoperability (pygeoapi, STAC, simpl, WaterML2.0, OpenMI)
- Interoperability with EU platforms (STAC, Simpl, Kubernetes)
- AI for science (PyTorch, TensorFlow, Scikit-learn)
- FAIR workflows (Docker, conda, pixi, git/GitHub, JupyterHub)
If you use our frameworks, models, or data in your research, please cite the appropriate repository or DOI via Zenodo. To learn more about core concepts in the Digital Waters Flagship, view our publications page.
We welcome questions, suggestions, and ideas for collaboration. Please contact us at diwa-vre@lists.oulu.fi