Combined assimilation of reanalyzed SWE and observed streamflow to enhance spring flow forecasts in southern Quebec, Canada
Bibliographic record
Abstract
Incorporation of observed streamflow into different hydrological models has resulted in improved streamflow forecasting in many studies. This approach is currently used by different hydropower companies to maximize hydroelectric production and also to predict and mitigate flood damages. In addition, snow-related model states, such as SWE, snow depth, and snow wetness, also carry important information regarding both timing and volume of spring flow in snow-dominated regions. Consequently, the main objective of this study is to combine assimilation of observed streamflow and reanalyzed SWE to enhance spring flow forecasting. The reanalyzed SWE product investigated is SNODAS. SNODAS is a snow data assimilation system that improves outputs of a snow model by assimilating observed snow data provided by airborne platforms, satellites, and ground stations and generates snow-related data, such as SWE and snow depth at 1 km resolution. SNODAS is run each day so that the data product is available in near real-time. In this study, SNODAS SWE data has been assimilated into HYDROTEL, a physically-based distributed hydrological model equipped with a snow module based on a mixed energy budget – degree-day approach, along with observed streamflow during the spring flow season in order to enhance spring flow forecasts. The study site is Au Saumon watershed, located in southern Quebec, Canada. The Au Saumon watershed has an area of 1022 and is predominantly forested. The simulation period is the 2014-2015 water year. Preliminary results show that combined assimilation of SNODAS SWE and observed streamflow improve spring flow predictions, while SWE assimilation has a more delayed impact than streamflow assimilation. Keywords: Data Assimilation, SNODAS, SWE, Spring Flow Prediction
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".