MHYDAS-Erosion: a distributed single-storm water erosion model for agricultural catchments
Bibliographic record
Abstract
In this paper, we present MHYDAS-Erosion, a dynamic and distributed single-storm water erosion model developed as a module of the existing hydrological MHYDAS model. As with many catchment erosion models, MHYDAS-Erosion is able to simulate sediment transport, erosion and deposition by rill and interrill processes. Its originality stems from its capacity to integrate the impact of land management practices (LMP) as key elements controlling the sedimentological connectivity in agricultural catchments. To this end, the water-sediment pathways are first determined by a specific process-oriented procedure defined and controlled by the user, which makes the integration of LMP easier. The LMP dynamic behaviours are then integrated into the model as a time-dependent function of hydrological variables and LMP characteristics. The first version of the model was implemented for vegetative filters and tested using water and sediment discharge measurements at three nested scales of a densely instrumented catchment (Roujan, OMERE Observatory, southern France). The results of discharge and soil loss for simulated rainfall events have been found to acceptably compare with available data. The average R2 values for water and sediment discharge are 0·82 and 0·83, respectively. The sensitivity of the model to changes in the proportion of LMP was assessed for a single rain event by considering three scenarios of the Roujan catchment management with vegetative filters: 0% (Scenario 1), 18% (Scenario 2, real case) and 100% (Scenario 3). Compared to Scenario 2 (real case), soil losses decreased for Scenario 3 by 65% on the agricultural plot scale, 62% on the sub-catchment scale and 45% at the outlet of the catchment and increased for Scenario 1 by 0% on the plot scale, 26% on the sub-catchment scale and 18% at the outlet of the catchment. Copyright © 2010 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".