Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations
Bibliographic record
Abstract
Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Qpeak) for the Mackenzie River, located in northwest Canada, is modelled using the Gravity Recovery and Climate Experiment (GRACE) satellite observations. Compared with the observed Qpeak at a downstream hydrometric station, the model results have a correlation coefficient of 0.83 (p < 0.001) and a mean absolute error of 6.5% of the mean observed value of 28,400 m3·s−1 for the 12 study years (2003–2014). The results are compared with those for other basins to examine the difference in the major factors controlling the Qpeak. It was found that the temperature variations in the snowmelt season are the principal driver for the Qpeak in the Mackenzie River. In contrast, the variations in snow accumulation play a more important role in the Qpeak for warmer southern basins in Canada. The study provides a GRACE-based approach for basin-scale snow mass estimation, which is largely independent of in situ observations and eliminates the limitations and uncertainties with traditional snow measurements. Snow mass estimated from the GRACE data was about 20% higher than that from the Global Land Data Assimilation System (GLDAS) datasets. The model is relatively simple and only needs GRACE and temperature data for flood forecasting. It 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".