Representation of the Effect of Agricultural Tile Drains on Streamflow simulated with GEM-Hydro in the Watershed of the Laurentian Great-Lakes.
Bibliographic record
Abstract
In agricultural areas, Tile Drains (TDs) are often installed by farmers in order to drain any excess of water accumulating in crop fields. This anthropogenic modification to the land surface can have strong effects on streamflow in these areas. Here, a simple technique was employed in order to partly account for the effect that TDs can have on streamflow simulated with the GEM-Hydro physically-based and distributed hydrologic model developed at Environment and Climate Change Canada (ECCC). The technique consists in significantly increasing the horizontal hydraulic conductivity of the soil layer generally containing the TDs, in the land-surface scheme of GEM-Hydro, for the part of the grid-cell that contains TDs. To do so, a multiplying coefficient obtained through automatic calibration was used to increase the appropriate soil layer’s horizontal hydraulic conductivity. The part of the grid-cell containing Tile Drains was obtained from different databases depending on the country (i.e., US or Canada) or the province (Ontario or Quebec). Moreover, a similar strategy was followed to represent the effect that agricultural ploughing practices can have on streamflow, by increasing the model’s vertical hydraulic conductivity for the superficial soil layers. The methodology employed allowed to significantly increase the performance of GEM-Hydro streamflow simulations in the watershed of the Laurentian Great-Lakes when compared to the default (current) version of the model, while maintaining similar performances for other hydrologic variables simulated with GEM-Hydro, such as evapotranspiration, and soil moisture and surface temperature simulations, when comparing for example to the recent Global Land Evaporation Amsterdam Model (GLEAM version 3.5b) reference dataset. These findings are promising in the view of developing land-surface schemes that can be applied both for two-way coupling with atmospheric models and for environmental and hydrologic applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".