Constraining a Flow Model with Field Measurements to Assess Water Transit Time Through a Vadose Zone
Bibliographic record
Abstract
The modeling of thick vadose zones is particularly challenging because of difficulties in collecting a variety of measured sediment properties, which are required for parameterizing the model. Some models rely on synthetic data, whereas others are simplified by running as homogeneous sediment domains and relying on a single set of sediment properties. Few studies have simulated flow processes through a thick vadose zone using real and comprehensive data sets comprising multiple measurements. Here, we develop a flow model for a 7-m-thick vadose zone. This model, combining the numerical codes CTRAN/W with SEEP/W, includes the measured sediment hydraulic properties of the investigated vadose zone and incorporates the actual climate and subsurface conditions of the study site (precipitations, water-table elevations, and stable isotope data). The model is calibrated by fitting the simulated and measured vertical profiles of water content. Our flow model calculates a transit time of 1 year for the travel of water through the 7-m vadose zone; this estimate matches stable isotope-based results obtained previously for this site. A homogeneous sediment domain flow model, which considers only a single set of sediment properties, produces a transit time that is approximately half the duration of that of the heterogeneous flow model. This difference highlights the importance of assuming heterogeneous material within models of thick vadose zones and testifies to the advantage gained when using real sediment hydraulic properties to parametrize a flow model.
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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) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".