A method for mapping topsoil field-saturated hydraulic conductivity in the Cévennes-Vivarais region using infiltration tests conducted with different techniques
Bibliographic record
Abstract
Topsoil field-saturated hydraulic conductivity, Kfs, is a parameter that controls the partition of rainfall between infiltration and runoff. It is a key parameter in most distributed hydrological models. However, there is a mismatch between the scale of local in situ measurements and the scale at which the parameter is required in models. Therefore it is necessary to design methods to regionally map this parameter at the model scale. The paper propose a method for mapping Kfs in the Cévennes-Vivarais region, south-east France, using more easily available GIS data: geology and land cover. The mapping is based on a data set gathering infiltration tests performed in the area or close to it for more than ten years. The data set is composed of infiltration tests performed using various techniques: Guelph permeameter, double ring and single ring infiltration tests, infiltrometers with multiple suctions. The different methods lead to different orders of magnitude for Kfs rendering the pooling of all the data challenging. Therefore, a method is first proposed to pool the data from the different infiltration methods, leading to a homogenized set of Kfs, based on an equivalent double ring/tension disk infiltration value. Statistical tests showed significant differences in distributions among different geologies and land covers. Thus those variables were retained as proxy for mapping Kfs at the regional scale. This map was compared to a map based on the Rawls and Brakensiek (RB) pedo-transfer function (Manus et al., 2009, Vannier et al., 2016), showing very different patterns between both maps. In addition, RB values did not fit observed values at the plot scale, highlighting that soil texture only is not a good predictor of Kfs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".