FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America

Seasonal streamflow forecasts provide key information for decision-making in fields such as water supply management, hydropower generation, and irrigation scheduling. The predictability of streamflow on seasonal timescales relies heavily on initial hydrological conditions, such as the presence of snow and the availability of soil moisture. In high-latitude and high-altitude headwater basins in North America, snowmelt serves as the primary source of runoff generation. This study presents and evaluates a data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America (Canada and the USA). The workflow employs snow water equivalent (SWE) measurements as predictors and streamflow observations as predictands. Gap-filling of SWE datasets is accomplished using quantile mapping from neighboring SWE and precipitation stations, and principal component analysis is used to identify independent predictor components. These components are then utilized in a regression model to generate ensemble hindcasts of streamflow volumes for 75 nival basins with limited regulation from 1979 to 2021, encompassing diverse geographies and climates. Using a hindcast evaluation approach that is user-oriented provides key insights for snow-monitoring experts, forecasters, decision-makers, and workflow developers. The analysis presented here unveils a wide spectrum of predictability and offers a glimpse into potential future changes in predictability. Late-season snowpack emerges as a key factor in predicting spring and summer volumes, while high precipitation during the target period presents challenges to forecast skill and streamflow predictability. Notably, we can predict lower-than-normal and higher-than-normal streamflows during spring to early summer with lead times of up to 5 months in some basins. Our workflow is available on GitHub as a collection of Jupyter Notebooks, facilitating broader applications in cold regions and contributing to the ongoing advancement of methodologies.

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Related Links

Related Dataset #1 : GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow

Related Dataset #2 : Northern Hemisphere historical in-situ Snow Water Equivalent dataset (NorSWE, 1979-2021)

Related Dataset #3 : SCDNA: a serially complete precipitation and temperature dataset in North America from 1979 to 2018 (Version 1.1)

Related Dataset #4 : Canadian historical Snow Water Equivalent dataset (CanSWE, 1928-2022)

Related Software #1 : dataretrieval (python): a Python package for discovering and retrieving water data available from Federal hydrologic web services

Related Software #2 : FROSTBYTE: Forecasting River Outlooks from Snow Timeseries: Building Yearly Targeted Ensembles

Related Software #3 : FROSTBYTE: Forecasting River Outlooks from Snow Timeseries: Building Yearly Targeted Ensembles

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Author Arnal, L.
Clark, M. P.
Pietroniro, A.
Vionnet, V.
Casson, D. R.
Whitfield, P. H.
Fortin, V.
Wood, Andrew
Knoben, W. J. M.
Newton, B.
Walford, C.
Publisher UCAR/NCAR - Library
Publication Date 2024-09-01T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Topic Category geoscientificInformation
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Metadata Date 2025-07-10T19:58:57.100531
Metadata Record Identifier edu.ucar.opensky::articles:42542
Metadata Language eng; USA
Suggested Citation Arnal, L., Clark, M. P., Pietroniro, A., Vionnet, V., Casson, D. R., Whitfield, P. H., Fortin, V., Wood, Andrew, Knoben, W. J. M., Newton, B., Walford, C.. (2024). FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America. UCAR/NCAR - Library. https://n2t.net/ark:/85065/d7wm1jr8. Accessed 02 August 2025.

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