Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
Bibliographic record
Abstract
Systematic tile drainage is used extensively in poorly drained agricultural lands to remove excess water and improve crop growth; however, tiles can also transfer nutrients from farmlands to downstream surface water bodies, leading to water quality problems. Thus, there is a need to simulate the hydrological behaviour of tile drains to understand the impacts of climate or land management change on agricultural surface and subsurface runoff. The Cold Regions Hydrological Model (CRHM) is a physically based, modular modelling system developed for cold regions. Here, a tile drainage module is developed for CRHM. A multi-variable, multi-criteria model performance evaluation strategy was deployed to examine the ability of the module to capture tile discharge under both winter and summer conditions (NSE > 0.29, RSR < 0.84 and PBias < 20 for tile flow and saturated storage simulations). Initial model simulations run at a 15 min interval did not satisfactorily represent tile discharge; however, model simulations improved when the time step was lengthened to hourly but also with the explicit representation of capillary rise for moisture interactions between the rooting zone and groundwater, demonstrating the significance of capillary rise above the saturated storage layer in the hydrology of tile drains in loam soils. Novel aspects of this module include the sub-daily time step, which is shorter than most existing models, and the use of field capacity and its corresponding pressure head to provide estimates of drainable water and the thickness of the capillary fringe, rather than using detailed soil retention curves that may not always be available. An additional novel aspect is the demonstration that flows in some tile drain systems can be better represented and simulated when related to shallow saturated storage dynamics.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".