Hydrological science is increasingly dependent on computational tools for modelling, prediction, uncertainty quantification and decision-making. At the same time, a growing community of researchers is developing open-source software, models, packages, graphical user interfaces and computational workflows that can make hydrological methods more accessible, transparent and reproducible. However, these tools are often developed independently and remain scattered across different programming languages, platforms and research communities.
This session aims to bring together researchers and developers of open-source tools for hydrology, providing a platform to move beyond conventional research outputs and showcase the software, models and workflows they have built for the hydrological community. Contributions are welcome across a broad range of approaches, including statistical and stochastic modelling, numerical and process-based models, hydrological extremes, machine learning and artificial intelligence, hybrid modelling, uncertainty quantification, data assimilation, forecasting and decision-support systems.
We particularly encourage contributions that introduce new open-source packages, software libraries, graphical user interfaces, modelling frameworks, APIs, workflows, benchmark datasets or computational platforms. Presentations may address the scientific problem the tool solves, its methodological framework, implementation, validation, interoperability and practical applications, encouraging collaboration, reuse, benchmarking and further development of open-source hydrological software.