Assessment of soil redistribution at catchment scale by coupling a soil erosion model and a sediment connectivity index (central spanish pre-pyrenees)
Bibliographic record
Abstract
The study and quantification of soil redistribution is a complex and difficult task and even a non-solved question at catchment scale both in field and numerical simulation studies. In this study we tackle this topic by coupling two different predicting models and a sound field-based dataset to assess the potential soil redistribution in a Mediterranean rain-fed agricultural and mountainous catchment (La Reina gully catchment, Cinco Villas region, NE Spain): the enhanced Modified-RMMF-2014 version of the “Modified Revised Morgan, Morgan and Finney” model (Morgan, 2001; López-Vicente and Navas, 2010) of soil erosion and the IC (Index of Connectivity; Borselli et al., 2008) model of sediment connectivity. In a cereal experimental plot (1.9 ha; 1 x 1 m of cell size), located in the lowlands of the La Reina gully catchment, we firstly ran the IC model under six different scenarios of runoff pathways and results were compared with field observations of soil redistribution. The best performance was obtained with the IC model when the map of geomorphic features (rills, ephemeral gullies and fan deposits) was used in the simulation. Predicted rates of both models were correlated at 613 control points and three areas where identified at the plot: erosive-, stable- and depositional- prone areas, affecting 30%, 22% and 48% of the soil surface, respectively. The average erosion rates in each area were 3.3, 1.5 and 1.1 Mg ha-1 yr-1 with standard deviation values of 20.3, 19.0 and 8.3 Mg ha-1 yr-1. Then, the IC model was run at La Reina gully catchment (231 ha; 5 x 5 m) and the IC values were analysed following the first approach. Stable areas and those mainly affected by processes of soil loss and deposition were identified. Results showed clear differences in the index of connectivity along the catchment though the extension of the areas with predominant processes of soil loss was under predicted (11% of the catchment area). Further research should be focused on the adjustment of the IC model to catchment scale. Our approach offers a simple and alternative method to assess spatially distributed processes of soil redistribution at catchment scale that can be of interest in ungauged catchments where calibration task of numerical models is difficult to be done.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".