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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-47
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-47
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 04 May 2020

Submitted as: model description paper | 04 May 2020

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This preprint is currently under review for the journal GMD.

Shyft v4.8: A Framework for Uncertainty Assessment and Distributed Hydrologic Modelling for Operational Hydrology

John F. Burkhart1,2, Felix N. Matt1,2, Sigbjørn Helset2, Yisak Sultan Abdella2, Ola Skavhaug3, and Olga Silantyeva1 John F. Burkhart et al.
  • 1Department of Geosciences, University of Oslo, Oslo, Norway
  • 2Statkraft AS, Lysaker, Norway
  • 3Expert Analytics AS, Oslo, Norway

Abstract. This paper presents Shyft, a novel hydrologic modelling software for streamflow forecasting targeted for use in hydropower production environments and research. The software enables the rapid development and implementation in operational settings, the capability to perform distributed hydrologic modelling with multiple model and forcing configurations. Multiple models may be built up through the creation of hydrologic algorithms from a library of well known routines or through the creation of new routines, each defined for processes such as: evapotranspiration, snow accumulation and melt, and soil water response. Key to the design of Shyft is an Application Programming Interface (api) that provides access to all components of the framework (including the individual hydrologic routines) via Python, while maintaining high computational performance as the algorithms are implemented in modern C++. The api allows for rapid exploration of different model configurations and selection of an optimal forecast model. Several different methods may be aggregated and composed, allowing direct intercomparison of models and algorithms. In order to provide an enterprise level software, strong focus is given to computational efficiency, code quality, documentation and test coverage. Shyft is released Open Source under the GNU Lesser General Public License v3.0 and available at https://gitlab.com/shyft-os, facilitating effective cooperation between core developers, industry, and research institutions.

John F. Burkhart et al.

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John F. Burkhart et al.

Model code and software

Shyft v4.8 J. F. Burkhart, F. N. Matt, S. Helset, Y. S. Abdella, O. Skavhaug, and O. Silantyeva https://doi.org/10.5281/zenodo.3782737

John F. Burkhart et al.

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Latest update: 28 Sep 2020
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Short summary
We present a new hydrologic modeling framework for interactive development of inflow forecasts for hydropower production planning and other operational environments (e.g. flood forecasting, etc.). The software provides a Python user interface that gives an end user extensive control over the model configuration in realtime, during a simulation. This functionality allows extensive exploration of alternative weather scenarios or model setup options.
We present a new hydrologic modeling framework for interactive development of inflow forecasts...
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