Saturation-excess overland flow in the European loess belt: An underestimated process?
Bibliographic record
Abstract
A major challenge in runoff and soil erosion modelling is the adequate representation of the most relevant processes in models while avoiding over parameterization. In the European loess belt, progressive soil crusting during rainfall events, resulting in infiltration-excess runoff, is usually considered the dominant process generating runoff on catchments covered with silty soils. Saturation-excess may also occur and affect their runoff and erosion behavior. However, saturation-excess runoff occurrence and quantification have rarely been performed and is usually not taken into account when modelling runoff and erosion in these environments. Accordingly, a continuous simulation of the Austreberthe catchment (214 km2), located in the European loess belt (Normandy, France), was conducted with the new Water and Sediment (WaterSed) model over 12 years, corresponding to more than 780 individual rainfall events, at a 25 m spatial resolution. The inter-annual variability of runoff and erosion was closely linked to the number of intense events per year and their distribution through the year. The model was properly calibrated over a representative set of 35 rainfall events, considering either infiltration-excess and/or saturation-excess runoff. It was also able to reproduce the measured runoff volume for most of the monitoring period. However, the three years with most rainfall were adequately modelled only including saturation-excess runoff. An analysis performed at the seasonal scale revealed that saturation was modelled in the catchment during almost all of the modelling period, suggesting the importance of this often overlooked process in current modelling attempts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".