Uncertainty in modelling the hydrologic responses of a large watershed: a case study of the Athabasca River basin, Canada
Bibliographic record
Abstract
Abstract Large‐scale watershed modelling presents a unique challenge in terms of physiographic and climatological heterogeneity, and spatially varied hydrologic responses. In particular, the spatial variability in hydrologic processes may introduce a high degree of uncertainty in the modelling of a large watershed. This study assessed the uncertainties in annual/seasonal streamflow and annual peak flow simulations with respect to selection of climate data and model parameter sets for the variable infiltration capacity (VIC) model of the Athabasca River basin (ARB) in Alberta, Canada. Two high‐resolution gridded climate data sets over the 1979 to 2010 period, and six different model parameter sets calibrated corresponding to different time periods and various hydrologic patterns, were employed to quantify the uncertainty in VIC simulations. Moreover, the possibility of an ensemble approach to predict hydrologic responses in the ARB has been investigated. The results indicated that streamflow simulations near the headwater and along the Athabasca River mainstream have high uncertainty corresponding to selection of climate data mainly because of greater difference of precipitation between the two climate data sets, whereas sub‐basin stations at low elevations were more sensitive to the selection of parameter set for interflow‐dominated runoff cycle. All stations showed higher uncertainty corresponding to the selection of parameter set for annual peak flows. In addition, this study confirmed that the ensemble means can provide more accurate and consistent hydrologic information for the low‐elevation area where higher internal variability exists. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes © 2013 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".