Parameterization and multi‐criteria calibration of a distributed storm flow model applied to a Mediterranean agricultural catchment
Bibliographic record
Abstract
Abstract The principal challenge in the parameterization of storm flow models for agricultural catchments with an artificial drainage network and fields with different degrees of tillage lies in the parsimonious definition of distributed model parameters in a way that reduces the number of calibration parameters to a justifiable minimum. This paper presents a comprehensive case study for the parameter estimation of a distributed storm flow model applied to an agricultural catchment (0.91 km2) in the Mediterranean region. Model parameterization was combined with procedures for multi‐criteria, multi‐storm calibration, where we automatically calibrated three parameters related to flow velocity and infiltration, and compared single and multi‐storm criteria that are based on discharge volume, peak flow, and the Nash–Sutcliffe coefficient. Multi‐storm calibration yielded a set of parameter values for the simulation batch with best multi‐storm overall performance, which are close to the median values in the pre‐calibration of individual storms. Our results suggest that flow velocities and proportionality of the channel infiltration rate do not vary significantly over the course of 11 years. Copyright © 2012 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".