A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting over North America
Bibliographic record
Abstract
Seasonal streamflow forecasts are critical for many different sectors - e.g., water supply management, hydropower generation, and irrigation scheduling. Initial hydrological conditions (e.g., snow storage and soil moisture) are an important source of hydrological predictability on seasonal timescales. Snowmelt is the main source of runoff generation in high-latitude and/or high-altitude headwaters basins across North America, and the basins downstream. As a result, data-driven forecasting from snow observations is a well-established approach for operational seasonal streamflow forecasting in the USA and Canada.The aim of this work is to benchmark the skill of probabilistic seasonal streamflow forecasts across North America. To this end, we developed a reproducible data-driven workflow and implemented it for basins with a nival regime across North America. The workflow uses snow water equivalent measurements from the Canadian historical Snow Water Equivalent dataset (CanSWE), the Natural Resources Conservation Service (NRCS) manual snow surveys, and the SNOTEL automatic snow pillow in the USA. These datasets are gap filled using quantile mapping based on neighboring snow and precipitation stations. Principal Component Analysis is then used to define a small set of orthogonal predictor variables. These principal components are used as predictors in a regression model to generate ensemble hindcasts of streamflow volumes for basins across North America. Preliminary results for 93 nival basins and 17 glacial basins across Canada suggest that this forecasting method has the ability to provide skilful hindcasts (i.e., better than streamflow climatology) during the snowmelt season with up to 2-3 months lead. The results of this study provide a reference against which alternative forecasting methods (e.g., process-based forecasting models or machine learning approaches) can be assessed in the future.This work is a contribution of the recently launched Cooperative Institute for Research to Operations in Hydrology (CIROH) initiative that aims to develop next-generation water prediction capabilities. The CIROH program and the Global Water Futures (GWF) program are advancing capabilities for probabilistic streamflow forecasting over North America.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".