Impact of using different ET models in HYDRUS-1D on soil water dynamics and potato crop ET.
Bibliographic record
Abstract
Soil water content (SWC) plays a critical role in crop yield, irrigation scheduling, and water resources management. In the Canadian Prairies, the SWC in the rootzone from rainfall is rarely sufficient to satisfy crop water requirements. Thus, an understanding of the soil water dynamics is important for effective water management. Hydrologic modelling helps us to understand the underlying processes controlling and affecting soil water movement and distribution. The reference evapotranspiration (ETref) is a key input in most hydrologic models; thus, the estimation method could affect simulation results and inferences. The FAO Penman-Monteith (FAO PM) is recommended as a standard model. However, it is limited by requiring too many weather variables that are not readily available. Thus, simple empirical ETref models have been developed as an alternative. Soil moisture sensors were installed at 0.2, 0.4, 0.6, 0.8, and 1 m depths to measure SWC. SWC was first modelled in a rainfed potato farm in Winkler, Manitoba, using the FAO PM equation as input in the HYDRUS-1D model. Statistical and graphical results showed that the HYDRUS model performed well in simulating SWC with R2 ranging from 0.6 to 0.9, RMSE from 0.003 to 0.03 m3/m3, MAE varying between 0.00932 and 0.0197 m3/m3 and MPE from -1.91 to 1.67%. The impacts of different ETref equations with varying weather inputs on soil water dynamics and seasonal potato crop evapotranspiration (ETc) were further investigated. The results showed that measured SWC and SWC predicted using Irmak, Priestly-Taylor, and the FAO PM equations were not statistically different. Similar results were also obtained for ETc. Hence, under limited data, the Irmak and Priestly – Taylor ETref equations are suitable alternatives that could provide accurate and reliable results for water management in southern Manitoba.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".