Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
HS3.6 | Open and Transparent Hydrosystem Modeling for Decision Support: New Workflows and Tools
Open and Transparent Hydrosystem Modeling for Decision Support: New Workflows and Tools
Convener: Anneli GuthkeECSECS | Co-conveners: Dirk EilanderECSECS, Catherine Moore, Jeremy White, Michael Fienen
To provide support for resource management decision making, computational modeling workflows in hydrosystem simulation need to be efficient, reproducible, and transparent. Further, the hydrological community strives for new standards related to open and inclusive practices, e.g. following the FAIR principles for scientific data management and stewardship. Open and transparent workflows allow for detailed scrutiny of the techniques, assumptions, and interpretations of data, models, and their uncertainties. Such workflows also address issues of inclusion and diversity by providing all details of the path from data to results which can benefit all stakeholders, (more) independent of their background. Advancements in software engineering and data hosting provide sophisticated tools to improve transparency, reproducibility, and accessibility of all steps of the modeling chain.

For this session, we seek contributions of open-source tools and workflows striving for these goals. We aim to stimulate discussion based on lessons learned from challenges as well as success stories.

Software tools may include, but are not limited to:
• techniques to automate modeling workflow elements or increase efficiency, reproducibility, robustness of decision-support modeling elements.
• workflow managers (such as Snakemake) that help build open and transparent modeling pipelines.
• frameworks to build models from original data in flexible ways that may enable hypothesis testing in the form of changing discretization, process representation, and other modeling decisions.
• multi-model frameworks such as Bayesian-model selection/combination, as well as frameworks to accommodate model structural error.
• Methods for uncertainty analysis, data assimilation, and management optimization under uncertainty in the decision-support context.
• machine-learning approaches for decision support analyses.