Analysis of an extreme rainfall-runoff event at the Landscape Evolution Observatory by means of a three-dimensional physically-based hydrologic model
Bibliographic record
Abstract
Abstract. We present a detailed analysis, by means of a three-dimensional physically-based hydrological model, of the first experiment conducted at the Biosphere 2 Landscape Evolution Observatory (LEO). The experiment was driven by an intense rainfall event and produced a hydrological response characterized predominantly by water outflow along the lower lateral boundary (seepage face) of LEO, together with overland flow that began 15 h after the start of rainfall and caused erosion of the superficial soil and formation of a small channel. The analysis is designed to test the null hypothesis that the soil is hydraulically homogenous, and an alternative hypothesis that the soil has developed some hydraulic heterogeneity in the downstream direction due to saturated soil compaction near the seepage face. More than 20 000 sensitivity simulations were run in a systematic search for optimal parameters to reproduce measurements of seepage face outflow and hillslope water storage. We varied the saturated hydraulic conductivity (Ksat) of the seepage face (18 values), Ksat in the rest of the LEO soil (30 values), and soil porosity (21 values), and we considered two values of the pore size distribution parameter (n) in the water retention characteristics, obtained from a particle size distribution analysis and from laboratory experiments on LEO soil samples. For both n values, the best simulations under the heterogeneous soil hypothesis produced smaller errors than the best runs under the null hypothesis. Moreover the heterogeneous runs yielded a higher probability of best realizations than the homogenous runs. These results support the hypothesis of localized incipient heterogeneity of the LEO soil.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".