Long‐term water budget imbalances and error sources for cold region drainage basins
Bibliographic record
Abstract
Abstract This study assessed the long‐term (1979–2008) water budget closures for 19 large cold region drainage basins in Canada using recently developed datasets for precipitation (P), land surface evapotranspiration and water surface evaporation, and observed streamflow. Total water storage (TWS) trends from the GRACE satellite observations were also used to assist the assessment. The objectives are to quantify the magnitudes and spatial patterns of the water budget imbalance (ε) and its source of errors for these cold region basins. Results showed that the water budget was closed within 10% of the P on average for all the basins. The ε showed a general pattern of positive values in the south and negative values in the north and mountainous regions over the country. Basins with large ε values were mostly found in the north. Uncertainties in the water budget variables, particularly P, were found to play a major role in the ε. Significant trends in TWS were found over 11 basins, which accounted for 31% of their ε on average. Improvements in the observation network, data quality assurance, and spatial models for P are critical for further improving the water budget closure for the cold region drainage basins. © 2014 Her Majesty the Queen in Right of Canada. Hydrological Processes. © John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".