Multiresponse modeling of variably saturated flow and isotope tracer transportfor a hillslope experiment at the Landscape Evolution Observatory
Bibliographic record
Abstract
Abstract. This paper explores the challenges of model parameterization and process representation when simulating multiple hydrologic responses from a highly controlled unsaturated flow and transport experiment with a physically based model. The experiment, conducted at the Landscape Evolution Observatory (LEO), involved alternate injections of water and deuterium-enriched water into an initially very dry hillslope. The multivariate observations included point measures of water content and tracer concentration in the soil, total storage within the hillslope, and integrated fluxes of water and tracer through the seepage face. The simulations were performed with a three-dimensional finite element model that solves the Richards and advection–dispersion equations. Integrated flow, integrated transport, distributed flow, and distributed transport responses were successively analyzed, with parameterization choices at each step supported by standard model performance metrics. In the first steps of our analysis, where seepage face flow, water storage, and average concentration at the seepage face were the target responses, an adequate match between measured and simulated variables was obtained using a simple parameterization consistent with that from a prior flow-only experiment at LEO. When passing to the distributed responses, it was necessary to introduce complexity to additional soil hydraulic parameters to obtain an adequate match for the point-scale flow response. This also improved the match against point measures of tracer concentration, although model performance here was considerably poorer. This suggests that still greater complexity is needed in the model parameterization, or that there may be gaps in process representation for simulating solute transport phenomena in very dry soils.
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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 | 0.001 | 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".