Modeling subsurface drainage of agricultural fields in high time resolution using RZWQM2
Bibliographic record
Abstract
Mathematical models have been widely used in agricultural fields to simulate hydrologic process and to predict water transport in farmlands. The field-scale Root Zone Water Quality Model (RZWQM version 2.94.00) is proven to be satisfactory in modeling agricultural subsurface drainage in many studies. However, while focusing on long-term period drainage simulation and overall model performance, few study investigates simulations in short-term high drainage peak events, where models usually show relatively unacceptable performance. Alternative methods should be evaluated in improving drainage peak simulations, and high time resolution data should be utilized in these short-term period tests. Therefore, this study aims at: 1) modifying soil water redistribution process in RZWQM2 to solve drainage peak delay issue and improve the simulation in the timing of drainage peak, 2) testing transient state drainage equations (integrated-Hooghoudt Equation and van Schilfgaarde Equation) against the steady state equation (Hooghoudt Equation) on an hourly time scale, 3) evaluating macropore component in RZWQM2 on an hourly scale to test preferential flow effects on drainage peak simulations. Two sets of data collected from subsurface drainage sites were used in this study. One of the experiments was conducted at the Agricultural Drainage Water Quality – Research and Demonstration Site (ADWQ-RDS) in Iowa, USA. And the second experiment was conducted by Agriculture and Agrifood Canada (AAFC) at the north shore of Lake Erie in Harrow, Ontario. The results showed that, by modifying the model to allow soil water redistribution and drainage to occur simultaneously with the infiltration of rainfall, the model performance was significantly better than that in original RZWQM2, with the percent of bias (PBIAS) decreased while Nash-Sutcliffe efficiency (NSE) and Index of Agreement (IoA) increased in both scenarios. However, tile drainage computed using the transient equations didn't improve the model performance. No significant difference amongst those equations was observed in this study. By activating macropore component in RZWQM2, hourly drainage peak values were better simulated, but it didn't perform satisfactorily in predicting total drainage amount and timing in peak periods. Furthermore, the macroporosity and pore radius parameters in the macropore component were proved to be insensitive. In general, the modified version of RZWQM2 performed better in simulating the timing of hourly drainage peaks and the macropore component can increase simulated peak values which were closer to the observed peak values. More methods should be tested to improve RZWQM2 performance in simulating drainage peak distribution and amount on an hourly time scale.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".