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.
Highlights
Operational hydrologic modeling is fundamental to several critical domains within our society
The World Meteorological Organization gives an overview of the responsibilities of these services and the products they provide to society, including monitoring of hydrologic processes, provision of data, water-related information including seasonal trends and forecasts, and, importantly, decision support services (World Meteorological Organization, 2006)
While Shyft provides some mechanisms for such investigation, we have further extended the paradigm to enable efficient evaluation of multiple forcing datasets in addition to model configurations, as this is found to drive a significant component of the variability
Summary
Operational hydrologic modeling is fundamental to several critical domains within our society. For the purposes of flood prediction and water resource planning, the societal benefits are clear. Many nations have hydrological services that provide water-related data and information in a routine manner. The World Meteorological Organization gives an overview of the responsibilities of these services and the products they provide to society, including monitoring of hydrologic processes, provision of data, water-related information including seasonal trends and forecasts, and, importantly, decision support services (World Meteorological Organization, 2006). Despite the abundantly clear importance of such operational systems, implementation of robust systems that are able to fully incorporate recent advances in remote sensing, distributed data acquisition technologies, high-resolution weather model inputs, and ensembles of forecasts remains a challenge. The Hydrologic Ensemble Prediction EXperiment (https://hepex.irstea. fr/, last access: 22 November 2020) is an activity that has been ongoing since 2004, and there is extensive research on the importance of the role of ensemble forecasting to reduce uncertainty in operational environments (e.g., Pappenberger et al, 2016; Wu et al, 2020)
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