Analysis of parameter uncertainty in SWAT model using a Bayesian Box–Cox transformation three-level factorial analysis method: a case of Naryn River Basin
Bibliographic record
Abstract
Abstract Hydrological models are often plagued by substantial uncertainties in model parameters when analyzing water balance, predicting long-time streamflow, and investigating climate-change impact in watershed management. In this study, a Bayesian Box–Cox transformation three-level factorial analysis (BBC-TFA) method is developed for revealing the influence of parameter uncertainty on the runoff in the Naryn River Basin. BBC-TFA cannot only quantify the uncertainty through Bayesian inference but also investigate the individual and interactive effects of multiple parameters on model output. Main findings disclose that: (i) the contribution rate of runoff potential parameter during the non-melting period reaches 88.22%, indicating a flood risk in the rainy season; (ii) the contribution rate of snow temperature lag factor is the highest during the snow-melting period and the entire year (respectively occupying 76.69 and 53.70%), indicating that the glacier melting exists in the Naryn River Basin throughout the year; (iii) the Box–Cox transformation can successfully remove residual variance and enhance the correlation between input and output variables. These findings serve to revealing the presence of glacial resources in the study basin and the significant runoff during the rainy season. Policymakers can consider water storage during the rainy season while developing glacier resources to alleviate water scarcity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".