FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".