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 sectors such as water supply management, hydropower generation, and irrigation scheduling. Principal component regression (PCR) stands as a well-established and widely used data-driven method for seasonal streamflow forecasting, offering advantages over more complex methods, including intuitive use of local data to represent key hydrological processes and low computational resource requirements.

We will present FROSTBYTE, a systematic and reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins. FROSTBYTE is available on GitHub as a collection of Jupyter Notebooks, facilitating broader applications in cold regions and contributing to the ongoing advancement of methodologies. This structured workflow consists of five essential steps: 1) Regime classification and basins selection, 2) Streamflow pre-processing, 3) Snow Water Equivalent (SWE) pre-processing, 4) Forecasting using PCR, and 5) Hindcast verification. It was applied to 75 basins characterized by a snowmelt-driven regime and limited regulation across diverse North American geographies and climates. Ensemble hindcasts of winter to summer streamflow volumes were generated from 1979 to 2021, with initialization dates ranging from January 1st to September 1st. The hindcasts were evaluated with a user-oriented approach, tailored to offer insights for snow monitoring experts, forecasters, decision-makers, and workflow developers. Join us to learn more about FROSBYTE, and explore ways in which you can actively contribute to its development.