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

Burkhart, John F.; Matt, Felix; Helset, Sigbjørn; Abdella, Yisak Sultan; Skavhaug, Ola; Silantyeva, Olga

doi:10.5194/gmd-2020-47

Cited by 2 publications

(3 citation statements)

References 73 publications

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“…The Statkraft Hydrological Forecasting Toolbox (Shyft, https://gitlab.com/shyft-os) was used in this study. Shyft provides an optimized platform for the implementation of well known hydrological models [6]. The high-performance generic time series framework in Shyft allows for rapid calculations of hydrologic response at the regional to a cell scale.…”

Section: Hydrologic Model Frameworkmentioning

confidence: 99%

“…In the past, several hydrological models have been developed for use in various applications. Some of these models include SRM [3], HEC-HMS [4], J2000 [5], Statkraft's Hydrological Forecasting Toolbox (Shyft) [6], etc. Unlike other hydrological models, Shyft provides an optimized platform for the implementation of many well-known hydrological models from conceptual to physically based distributed hydrological models.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment

Bhattarai

Silantyeva

Teweldebrhan

et al. 2020

Water

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Distributed and semi-distributed hydrological modeling approaches commonly involve the discretization of a catchment into several modeling elements. Although some modeling studies were conducted using triangulated irregular networks (TINs) previously, little attention has been given to assess the impact of TINs as compared to the standard catchment discretization techniques. Here, we examine how different catchment discretization approaches and radiation forcings influence hydrological simulation results. Three catchment discretization methods, i.e., elevation zones (Hypsograph) (HYP), regular square grid (SqGrid), and TIN, were evaluated in a highly steep and glacierized Marsyangdi-2 river catchment, central Himalaya, Nepal. To evaluate the impact of radiation on model response, shortwave radiation was converted using two approaches: one with the measured solar radiation assuming a horizontal surface and another with a translation to slopes. The results indicate that the catchment discretization has a great impact on simulation results. Evaluation of the simulated streamflow value using Nash–Sutcliffe efficiency (NSE) and log-transformed Nash–Sutcliffe efficiency (LnNSE) shows that highest model performance was obtained when using TIN followed by HYP (during the high flow condition) and SqGrid (during the low flow condition). Similar order of precedence in relative model performance was obtained both during the calibration and validation periods. Snow simulated from the TIN-based discretized models was validated with Moderate Resolution Imaging Spectroradiometer (MODIS) snow products. Critical Success Indexes (CSI) between TIN-based discretized model snow simulation and MODIS snow were found satisfactory. Bias in catchment average snow cover area from the models with and without using imputed radiation is less than two percent, but implementation of imputed radiation into the Statkraft Hydrological Forecasting Toolbox (Shyft) gives better CSI with MODIS snow.

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Section: Hydrologic Model Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment

Bhattarai

Silantyeva

Teweldebrhan

et al. 2020

Water

Self Cite

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“…com/shyft-os/dockers (Shyft, 2021c). A Zenodo archive with the exact version of Shyft described and used in this paper is available at https://doi.org/10.5281/zenodo.3782737 (Burkhart et al, 2020).…”

Section: Multiple Model Configurationmentioning

confidence: 99%

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

et al. 2021

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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.

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

Cited by 2 publications

References 73 publications

Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment

Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment

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

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