Multi-scale Calibration and Validation of MHYDAS-Erosion for A Small Mediterranean Vineyard Catchment: A Case Study
Bibliographic record
Abstract
In this work we present a case study of the multi-scale calibration and validation of MHYDAS-Erosion applied to a Mediterranean vineyard. The calibration was performed using expert knowledge in linking physical parameters to land uses with the automatic parameter estimation software PEST. MHYDAS-Erosion was calibrated and validated using spatially distributed observations on total discharge and soil loss. Calibration has been performed within six rainfall events; both hydrological and erosion parameters were calibrated using RMSE, R2and the modified version of the Nash-Sutcliffe model efficiency criteria. Calibration results indicate there was good agreement between simulated and observed total discharge and total soil loss at the seven observation points (modified Nash-Sutcliffe efficiency (mNSE) ranging between 0.89 and 0.95). Acceptable results were obtained in terms of parameter values, identification of their physical meaning and coherence. However, some limitations were also identified, and could be remedied in more detailed studies involving (i) spatially-distributed rainfall on the catchment, (ii) a description of groundwater exfiltration and (iii) spatially-distributed properties of the ditches over the catchment. Validation results were quite satisfactory for three of the four validation events. The results from this case study suggest that MHYDAS-Erosion may need a specific calibration when applied to another catchment, but once it is calibrated, it could be used for multi-scale soil loss forecasting.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".