Hydro‐sedimentary Dynamics of a Drained Agricultural Headwater Catchment: A Nested Monitoring Approach
Bibliographic record
Abstract
Core Ideas Discharge and sediment concentrations were measured in a drained catchment. The impact of tile drainage at the catchment scale was analyzed with a nested approach. The major part of sediment fluxes occurred during winter floods. Sediment transfer in the tile drain was episodic although significant. Soil erosion and sediment transfer are extensive in lowland agricultural catchments. In these environments, high‐frequency datasets of both water discharge and suspended sediment concentrations are often lacking. In particular, the impact of tile drainage networks on sediment fluxes in these catchments is poorly understood. Our research quantified sediment fluxes between 2013 and 2016 at five nested stations including a tile drain outlet across a small (25‐km 2 ) agricultural catchment (located in the Loire River basin, France) representative of lowland cultivated environments of the middle part of this basin. Sediment fluxes varied from 1 to 38 t km −2 yr −1 across the monitored subcatchments. Most of sediment fluxes (79 ± 9%) were measured during flood events ( n = 44), mostly occurring in winter (75%) and spring (20%). Seasonality controlled most of the variations of sediment fluxes. Sediment transfers in tile drains occurred episodically. Flows were measured during 11.4% of the monitoring time. A mean time lag of 80 min was recorded between the peak discharge at the drain outlet and the downstream river station. At the event scale, sediment fluxes exported from the tile drain varied between 1.1 × 10 −4 and 2.5 t km −2 and remained of the same order of magnitude as those measured at the downstream river station, although the latter were on average 53% lower. Results suggest significant storage of sediment material in the river channel and its export during the most intense flood events. Our research emphasizes the need for continuous monitoring of water and sediment transfers in agricultural catchments equipped with tile drains.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".