Impacts of Recharge Estimation on Groundwater Modeling for Arid Basins
Bibliographic record
Abstract
Abstract Recharge is important for evaluating groundwater sustainability, and efficiently managing water supplies. In arid areas, especially in areas where the amount of aquifer production outweighs aquifer replenishment, it can be extremely important to quantify recharge and to spatially identify recharge distribution. Field sampling with a Guelph Permeameter helped identify specific recharge areas and the use of the Soil Water Assessment Tool (SWAT) provided meaningful recharge rates for an arid basin in southwest Texas. SWAT modeling for Wild Horse Basin generated an annual amount of recharge into the basin aquifer that was subsequently used in two transient MODFLOW simulations, one with the recharge distributed according to the sediment unit location (distributed zonation), and one with the recharge concentrated in cells adjacent to the front of the mountain chains surrounding the basin (mountain-front zonation). When comparing the results of the two recharge distributions on individual well hydrographs to historic data, mountain-front recharge appeared to improve model calibration efforts. This study indicates that recharge in arid basins cannot be determined solely by calibrating numerical models because it is so small that other simulation errors overwhelm reasonable differences. However, the location of the distribution of recharge appeared significant when calibrating individual well hydrographs. A deterministic analytical model like SWAT is a good way to estimate recharge in arid basins and create meaningful input parameters for numerical models like MODFLOW. MODFLOW was in turn able to evaluate the SWAT recharge estimations for Wild Horse Basin with Calibration and sensitivity analyses.
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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) | 0.000 | 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 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".