Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology

Abstract

Abstract. This paper presents Shyft, a novel hydrologic modeling software for streamflow forecasting targeted for use in hydropower production environments and research. The software enables rapid development and implementation in operational settings and the capability to perform distributed hydrologic modeling 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 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 (last access: 22 November 2020), facilitating effective cooperation between core developers, industry, and research institutions.