Evaluation of Winter Hydrology Performance of Three Field-Scale Models
Bibliographic record
Abstract
Highlights EPIC, SHAW, and DRAINMOD models were evaluated for the simulation of winter hydrology. Energy-based models can better simulate late-winter and early-spring hydrology under winter conditions. Effective simulation of soil temperature and soil hydraulics in winters were identified as potential areas of development in temperature-based models. Abstract. The deterioration of Lake Erie's water quality is one of the major concerns in North America. A considerable percentage of annual phosphorus runoff occurs during the non-growing season in cold agricultural regions such as those in the Great Lakes region. Consequently, without accurate simulation of water flow during cold periods, reliable modeling of sediment and nutrient loads to surface water bodies is not achievable. Three hydrological models (EPIC, SHAW, and DRAINMOD) were evaluated for their capacity to predict winter tile flow and to highlight the significant processes that have a larger effect on runoff simulation at a field site in Southern Ontario, Canada. The SHAW model adequately predicted both soil temperature at 10 cm depth (R 2 = 0.95; 2013-2014) and winter tile flow (2012-2014, Nov-Apr; R 2 = 0.52; PBIAS = 7; NSE = 0.49). In the case of tile flow, DRAINMOD exhibited comparable results to the SHAW model for the same period (R 2 =0.55, PBIAS = -28, NSE = 0.58). EPIC was not able to perform satisfactorily in simulating the tile flow during winter conditions, which was attributed to the model’s erroneous prediction of soil temperature from air temperature. It was determined that energy-based models like DRAINMOD and SHAW can better simulate late-winter and early-spring hydrological conditions. Keywords: Agricultural runoff, Canadian winter hydrology, Hydrological models, Soil temperature, Tile flow.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".