Reconstructing snowmelt runoff in the Yukon River basin using the SWEHydro model and AMSR‐E observations
Bibliographic record
Abstract
Abstract Snowmelt timing and snow water equivalent (SWE) from the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) are used as inputs to the SWEHydro model to simulate spring snowmelt runoff in high‐latitude, snow‐dominated drainages. AMSR‐E data from 2003 to 2010 are used to determine the timing of melt onset and snow saturation on the basis of changes in brightness temperature ( T b ) and diurnal amplitude variations (DAV). Pre‐melt SWE data are combined with terrain information and melt rate estimates to calculate runoff. After melt onset, there is a ‘melt transition period’ with daytime melt and nocturnal refreeze. The melt transition is characterized by high T b oscillations (high DAV). At the end of high DAV, the snowpack is melting at a higher rate. The model uses four parameters: snowmelt rate during and after melt transition (defined by T b and DAV thresholds) and flow timing during and after melt transition. The model effectively simulates spring freshet, peak timing and magnitude, and volume (between days 50 and 180) in basins lacking sufficient meteorological measurements for conventional models. We compare the model response in the Pelly and Stewart Rivers, tributaries to the Yukon River, to evaluate model parameters in broadly similar basins under varying conditions. Simulated freshet timing is strongly related to snowmelt timing, and the modeled hydrograph is most sensitive to the flow timing parameter. This observationally based model has potential as a module for quantifying spring snowmelt runoff and timing in physically based models. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 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".