Transient potential groundwater recharge under surface irrigation in semiarid environment: An experimental and numerical study
Bibliographic record
Abstract
Abstract Groundwater recharge, a key component of the global water cycle, is critical in understanding water dynamics in managed and natural ecosystems. In this study, an experimental and numerical method was used to investigate potential groundwater recharge (PGR) in a semiarid region in Iran. A large soil column with four distinct layers was constructed using soil from an agricultural farm in the area. A 140‐day experiment with 10 applications of irrigation was carried out, and soil water was measured at different depths. Deep drainage started around 40 days after the first irrigation and increased with more water applications. The soil water dynamics for different irrigation tests were modelled using the HYDRUS‐1D software package, and the model was calibrated using laboratory collected data. Good agreement was achieved between the HYDRUS‐1D simulated and laboratory measurement data. The calibrated model was used to simulate PGR using meteorological station data between November 2008 and 2012. Three scenarios were applied, including fully cropped land with irrigation and precipitation water (R1), irrigation water (R2), and bare soil with precipitation water (R3) only. Results showed that the average annual PGR rates were 32.42%, 10.8%, and 4.26% for the three scenarios, respectively. Temporal variability of recharge for the R1 showed that recharge increased with a 2‐month delay corresponding to the increased water infiltration during harvest time. Recharge flux started after high precipitation values for R3. A clear relation between monthly precipitation and recharge rate was not found even after a 2‐month shifted recharge, but a relatively high correlation was observed between monthly 2‐month shifted recharge and precipitation after a threshold value cut‐off. Additionally, the results showed that the traditional recharge estimation in semiarid regions with episodic natural precipitation may not be as simplified as generally assumed.
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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) | 0.001 | 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 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".