Peak river flows in cold regions – Drivers and modelling using GRACE satellite observations and temperature data
Bibliographic record
Abstract
Abstract. The peak river flow for the Mackenzie River is modelled using GRACE satellite observations and temperature data, which advances the applications of space-based time-variable gravity measurements in cold region flood forecasting. The model estimates peak river flow by simulating peak surface runoff from snowmelt and the corresponding baseflow. The modelled results compared fairly well with the observed values at a downstream hydrometric station. The results also revealed an average 22-day travel time for the snowmelt water to reach the hydrometric station. The major driver for determining the peak flow was found to be the temperature variations. Compared with the Red River basin, the results showed that the Mackenzie River basin has relatively high water storage and water discharge capability, and low snowmelt efficiency per unit temperature. The study also provides a GRACE-based approach for basin-scale snowfall estimation, which is independent of in situ measurements and largely eliminates the limitations and uncertainties with traditional approaches. The model is relatively simple and only needs GRACE and temperature observations for peak flow or flood forecasting. The model can be readily applied to other cold region basins, and could be particularly useful for regions with minimal data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".