Inter‐comparison of high‐resolution gridded climate data sets and their implication on hydrological model simulation over the Athabasca Watershed, Canada
Bibliographic record
Abstract
Abstract Several different gridded climate data sets have recently been made available with the purpose of providing a consistent set of climatic data for many hydro‐climatic studies. Recent advances in land‐surface schemes and their implementation in fully distributed processes‐based hydrologic models have demanded even higher‐resolution gridded data. It remains, however, a challenge to identify the most reliable gridded climate data for hydrologic modelling, especially in mountainous headwater regions where there is significant spatial variability but few observing stations. Moreover, the accuracy of such climate forcing data applied to alpine headwaters directly affects the modelled hydrologic responses of the lower, downstream portions of river basins. This study evaluates the spatial and temporal differences in precipitation and temperature fields among three high‐resolution climate data sets available in Canada, namely, the North American Regional Reanalysis, the Canadian Precipitation Analysis and the thin‐plate smoothing splines (ANUSPLIN). Inter‐comparison of the quality of these data sets was undertaken for the Athabasca River basin in western Canada. The hydrologic responses of this watershed with respect to each of the three gridded climate data sets were also evaluated using the Variable Infiltration Capacity model. Results indicate that the data sets have systematic differences, which vary with regional characteristics – the largest differences being for mountainous regions. The hydrologic model simulations corresponding to those three forcing data sets also show significant differences and more with North American Regional Reanalysis than those between Canadian Precipitation Analysis and ANUSPLIN. © 2014 Her Majesty the Queen in Right of Canada. Hydrological Processes © 2014 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".