Can we determine rainfall-runoff model parameters from vegetation catchment characteristics?
Bibliographic record
Abstract
The regionalisation issue is a real challenge for operational hydrology and has direct implications for the prediction on ungauged basins. Indeed, the first step of the development of a priori parameters estimation is to find the correlations between calibrated parameters and physical catchment descriptors. Our research investigates the relationships between the GR4J rainfall-runoff model parameters and catchment vegetation characteristics over a large sample of 221 French catchments. Besides, we also aim at improving the correlations between vegetation catchment characteristics and model parameters by refining the structure of the model. First, the links between GR4J calibrated parameters and catchment vegetation-type are investigated. Then, we try to improve these relations by introducing a description believed more physically sound in order to take into account vegetation-types, hence following the so-called downward approach. Results show that the GR4J model parameters cannot be obtained directly from vegetation characteristics. Moreover, the situation is not improved when using a more physically based approach to model evapotranspiration. We believe that these results, obtained over a large sample of catchment sample, are quite informative and are supported by previous findings (see for example Merz and Bloschl, 2004).
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".