A national‐scale assessment of long‐term water budget closures for Canada's watersheds
Bibliographic record
Abstract
Abstract This study examined the long‐term water budget closures for 370 watersheds over Canada's landmass by using 30 years' (1981–2010) data products recently produced for precipitation (P) gridded using climate station measurements, land surface evapotranspiration (ET), and water surface evaporation (E0) obtained by the Ecological Assimilation of Land and Climate Observations (EALCO) model, and observed streamflow (Q). The results show that 29%, 58%, and 83% of the watersheds were closed within 5%, 10%, and 20% of P, respectively. The positive and negative imbalances among the 370 watersheds are largely offset and the national scale average is −24 mm yr −1 , or 4.2% of P. Water budget closures have large variation across the landmass. Regions with sparse or less accurate monitoring of P such as the mountainous region and the Arctic exhibit the largest water imbalances. Further efforts on enhancing the climate observation networks, improving spatial models for P and ET estimates, and streamflow measurements are all likely critical for a better understanding of Canada's water budgets.
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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 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".