Impact of groundwater depth on crop coefficient: An improved evapotranspiration model
Bibliographic record
Abstract
Understanding the impact of groundwater depth on actual crop evapotranspiration (ET c act ) is essential for agricultural water management in shallow water table regions. A four-year field experiment (2019–2023) was conducted at the Wudaogou Hydrological Experimental Station to monitor the ET c act of winter wheat under different groundwater depth conditions, while simultaneously recording key meteorological variables, including air temperature, precipitation, wind speed, net radiation, relative humidity, sunshine duration, soil heat flux, and soil temperature. Based on the observed data, we analyzed the influence of groundwater depth on winter wheat ET c act and actual crop coefficients ( K c act ). The results revealed that both ET c act and K c act exhibited a clear exponential relationship with groundwater depth, showing a significant decreasing trend as the depth increased. Building upon this finding, we developed a Groundwater–Meteorology-Based Actual Crop Evapotranspiration Model (GW–M model) that incorporates both groundwater depth and meteorological factors. In the model, these variables affect ET c act by influencing the K c act . The selection of meteorological factors was guided by the top three variables identified through geographical detector analysis. The model effectively reproduced the ET c act process of winter wheat in shallow groundwater areas. Comparative analysis with other evapotranspiration models demonstrated that the proposed model achieved higher accuracy. This study underscores the pivotal role of groundwater depth in regulating ET c act in shallow water table areas. The proposed modeling approach offers a flexible and scalable framework for simulating ET c act under variable groundwater conditions, thereby providing theoretical support for regional-scale agricultural water management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".