Investigations of uncertainty in SWAT hydrologic simulations: a case study of a Canadian Shield catchment
Bibliographic record
Abstract
Abstract Uncertainty analysis is an integral part of hydrological modelling. We investigated uncertainty from model structure and parameters in the soil and water assessment tool (SWAT) hydrologic simulations for a Canadian Shield catchment (5.4 km2) in south central Ontario. We investigated influences of model structure on parameter identifiability and prediction uncertainty by comparing model performance of SWAT (version 2009.10.1 Beta3) and SWAT‐CS (a version representing hydrological processes in Canadian Shield catchments). Equifinality was found to exist for many parameters in process modules of snow water equivalent (SWE), streamflow and lake outflow. For SWAT, there are clearly identifiable parameters from process modules of snowmelt, overland flow, lateral flow and groundwater flow, while snowmelt parameters and lateral flow travel time are the only clearly identifiable parameters in SWAT‐CS. Model structure also notably influenced the optimum parameter values. Only 50–55% of observed SWE and 27–43% of observed streamflow were bracketed by the corresponding 95% confidence interval. The prediction uncertainty for SWE was mainly caused by the inaccuracy in timing of simulated snowmelt. This, along with use of a daily time step that is not able to capture the subdaily rainfall on snow pack, the limited capability of simulating groundwater by SWAT and the close‐to‐zero streamflow during dry seasons at the study catchment, could all contribute to the prediction uncertainty in streamflow. Comparatively, more reasonable model structure (SWAT‐CS) was shown to reduce prediction uncertainty. Copyright © 2015 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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".