Assessment of Macropore Component of RZWQM2 in Simulating Hourly Subsurface Drainage and Peaks
Bibliographic record
Abstract
Highlights The macropore component of RZWQM2 was evaluated using hourly drainage and rainfall data. Activating macropore components improved hourly drainage peak simulation. Macropore flow simulated by RZWQM2 was insensitive to the macroporosity and pore radius. Abstract. Understanding preferential flow through soil macropores is critical to effectively managing subsurface drainage water quantity and quality. This study aims to assess the macropore component of the Root Zone Water Quality Model (RZWQM2) in simulating subsurface tile flow with a high time resolution. Observed hourly tile flow rates from two experimental sites in Ontario, Canada (2008-2011) and Iowa, USA (2007-2008) were used to evaluate the importance of including a macropore flow component in subsurface drainage simulation. Activating the macropore component in the model improved the simulation of hourly drainage peaks, especially peak amplitude. Still, it did not improve the simulation of the total drainage amount for each rainfall event. Simulation of the drainage peak recession varied from peak to peak, suggesting that further studies are warranted for drainage flow in the model. In general, the macropore component in the RZWQM2 model improved subsurface peak subsurface simulation at the hourly resolution. However, further investigation and model modifications are needed to improve the drainage simulation’s timing and quality for RZWQM2’s hydrologic simulation of macropore flow and subsurface drainage. Keywords: Macropores, RZWQM2, Subsurface drainage modelling, Preferential flow simulation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".