A novel watershed scale Snow Water Equivalent (SWE) correction approach, using stream flow and remote sensing data
Bibliographic record
Abstract
Flood forecasting agencies and hydropower companies require cost effective approaches for accurate estimation of snow water equivalent (SWE) to improve spring flow forecast and to make informed decision about reservoir operation. The lack of accurate SWE estimation at the watershed scale is an issue in northern watersheds, as snow surveys are either absent, or sparsely distributed and infrequent (monthly to bi-weekly). Remotely sensed SWE data sets retrieved from passive microwave satellites, such as GlobSnow, offers the advantage of high frequent coverage of the Northern Hemisphere at the watershed scale. The main issue is that SWE is typically underestimated because of vegetation. Also, the signal saturates for deep snowpacks. An approach is therefore required to correct GlobSnow which does not resort to local SWE measurements. A correction factor approach which focuses on improving the Maximum Snow Water Equivalent (MSWE) estimate for a watershed produced by publicly available regional databases, such as GlobSnow, has been developed. The method does not require point SWE measurements and assumes that the spring runoff volume calculated from historical streamflow observations equals the total snow melt volume retrieved from GlobSnow’s MSWE, less infiltration into frozen ground. The latter is calculated from freely available hydro-meteorological information. The method presented below introduces a cost-effective approach which can bridge the temporal and spatial sparsity that is often associated with the snow survey programs. The results from applying this approach to the regional GlobSnow database to northern watersheds in Quebec show that the Corrected GlobSnow (C-Glob) more accurately correlates to the manual snow surveys, compared to the uncorrected GlobSnow data source. The corrected database may prove especially useful for watersheds where no SWE measurements are available, may serve as a supplementary source of information to better understand what takes place over the entire watershed by filling gaps of manual surveys.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".