A Comparison of Conceptual Rainfall-Runoff Modelling Structures and Approaches for Hydrologic Prediction in Ungauged Peatland Basins of the James Bay Lowlands
Bibliographic record
Abstract
James Bay Lowland peatlands are environments with unique hydrologic characteristics that challenge some basic assumptions embedded within many hydrology models, including topographically-driven lateral flows and hydrologic connectivity of all terrestrial landscape elements within the stream network.With increasing resource development in northern lowland regions of Canada, more rigorous and honest appraisal of modelling capabilities and deficiencies is warranted.This study was initiated with the following two objectives: (1) to compare the performance of two popular conceptual rainfall-runoff models, TOPMODEL and HBV, for rainfall-runoff simulation in a James Bay Lowland peatland complex in the James Bay Lowlands, and (2) to compare regionalization methods to maximize the predictive value of available landscape information to improve model calibration using HBV.HBV was found to outperform TOPMODEL, which was altogether unsuitable for this environment.Regionalization analyses and results favoured empirical methods such as artificial neural networks to improve predictive capabilities of the HBV model.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".