Comparing alternative conceptual models for tile drains and soil heterogeneity for the simulation of tile drainage in agricultural catchments
Bibliographic record
Abstract
Tile drains are important water flow paths in agricultural catchments and must be included in hydrological models. However, their locations are rarely known and the explicit incorporation of tile drains in hydrological models requires refined meshes around the drains, which can significantly increase computational times. Although seepage nodes have been used to represent tile drains with satisfactory performance, they have never been applied to represent all tile drainage systems in a catchment. The goal of this study is to compare different conceptual models for tile drains and soil heterogeneity for the numerical simulation of tile drainage in an agricultural catchment in Denmark. The first conceptual model for tile drains uses seepage nodes to represent only the main collector drains in the catchment and the second model uses seepage nodes distributed over all the agricultural areas, without considering the specific locations of tile drains. A third conceptual model, labelled the Benchmark Model, represent all tile drains at their known locations with seepage nodes and a fourth conceptual model implicitly represents tile drains as a high-permeability layer. The four models performed satisfactorily to simulate the observed outlet stream discharge and could be recommended almost interchangeably. The simulation of the water table depth was very satisfactory compared to modeling studies with similar mesh resolution (∼50 m). Results indicated that the three models using seepage nodes i) simulated similar monthly discharges and cumulative discharge volumes for most of the studied tile-drained areas, and ii) simulated surface water flow in tile-drained fields without runoff or ponding water. The shorter simulation times (around 35% faster) of the model representing the main drains and the distributed seepage node model suggest that they are suitable for model calibration, compared to the Benchmark Model. Whenever the location of tile drains is unavailable, using seepage node to represent drains in agricultural areas may satisfactorily simulate catchment-scale stream and drainage discharges. Four alternative soil models were developed to evaluate the effect of soil heterogeneity on the simulations. Our results suggest that at smaller scales (drainage area) soil heterogeneity is more relevant than the drainage conceptualization to improve model results. However, at the subcatchment scale, the opposite was observed and, at the catchment scale, both criteria had a comparable effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".