Application of variance decomposition approach in the uncertainty analysis of a hydrological model
Bibliographic record
Abstract
Decomposition of uncertainty into individual sources is important for understanding the risks of decisions made under the modelling uncertainty. This paper has applied the predictive uncertainty analysis and variance decomposition (VD) approach for quantifying hydrological modelling uncertainty. The VD analysis is used for quantifying the contribution of various sources of uncertainty to total modelling uncertainty. The goal is to increase the reliability of predictive uncertainty analysis by the inclusion of the results of VD analysis. The approach is evaluated by analyzing uncertainty of the Soil and Water Assessment Tool (SWAT) model for a watershed of Southwestern Ontario, Canada. Three uncertainty analyzing frameworks are employed for quantifying modelling uncertainty by both predictive uncertainty analysis and VD approach. The contribution of parameter uncertainty to total error variance is expressed by the percentage of total variance explained by parameter uncertainty and this contribution is quantified for three uncertainty analyzing frameworks. The contributions of other uncertainties excluding model parameters and precipitation uncertainties to total error variance are quantified by applying the VD approach. The results obtained from predictive uncertainty and VD analyses are observed to be consistent. The underlying hypotheses of each uncertainty framework are also verified for the reliability of the uncertainty analysis.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".