Development of a Stepwise‐Clustered Multi‐Catchment Hydrological Model for Quantifying Interactions in Regional Climate‐Runoff Relationships
Bibliographic record
Abstract
Abstract The concurrent variations of multi‐catchment runoffs exist widely in natural hydrological systems. Approaching such variations requires integrated analyses of not only the climate‐runoff relationships within individual catchments but also the distributive interactions among multiple catchments. In this study, a stepwise‐clustered multi‐catchment hydrological model (SCMW) is proposed to tackle the interactive relationships among multi‐catchment runoffs and their concurrent variations within a watershed system. Through multivariate inference based on Wilks likelihood ratio criterions and F tests, the proposed model can deal with both continuous and discrete variables as well as nonlinear relations among multiple variables without the assumptions of functional relationships. The proposed SCMW is applied to the Iskut‐Stikine Watershed, Canada. The effects of multiple uncertain factors are traced through multilevel factorial analysis. Overall, the accuracies of SCMW‐simulated mean and interval flows demonstrate that the developed method can well reproduce the distributive and interactive relationships between climatic variables and multi‐catchment runoffs. At the same time, the contributions of climate variables can be quantified; for example, it is found that near‐surface minimum temperature (at Below Johnson Station) can explain 25.6% concurrent variations of multi‐catchment runoffs, and vapor pressure (at Below Johnson Station) and precipitation (at Telegraph Creek Station) can explain 17.5% and 4.6% of such variations, respectively. The results of the multilevel factorial analysis indicate that the uncertainties in the simulated runoff levels are mainly from the modeling approach (43.3%); also, significant effects exist from interactions among multiple impact factors. The molding of concurrent variations of multi‐catchment runoffs through SCMW is helpful for improving simulation accuracy for watersheds with spatially heterogeneous climate‐runoff relationships.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".