Incipient subsurface heterogeneity and its effect on overland flow generation – insight from a modeling study of the first experiment at the Biosphere 2 Landscape Evolution Observatory
Bibliographic record
Abstract
Abstract. Evolution of landscape heterogeneity is controlled by coupled Earth system dynamics, and the resulting process complexity is a major hurdle to cross towards a unified theory of catchment hydrology. The Biosphere 2 Landscape Evolution Observatory (LEO), a 334.5 m2 artificial hillslope built with homogeneous soil, may have evolved into heterogeneous soil during the first experiment driven by an intense rainfall event. The experiment produced predominantly seepage face water outflow, but also generated overland flow, causing superficial erosion and the formation of a small channel. In this paper, we explore the hypothesis of incipient heterogeneity development in LEO and its effect on overland flow generation by comparing the modeling results from a three-dimensional physically based hydrological model with measurements of total mass change and seepage face flow. Our null hypothesis is that the soil is hydraulically homogeneous, while the alternative hypothesis is that LEO developed downstream heterogeneity from transport of fine sediments driven by saturated subsurface flow. The heterogeneous case is modeled by assigning saturated hydraulic conductivity at the LEO seepage face (Ksat,sf) different from that of the rest (Ksat). A range of values for Ksat, Ksat,sf, soil porosity, and pore size distribution is used to account for uncertainties in estimating these parameters, resulting in more than 20 000 simulations. It is found that the best runs under the heterogeneous soil hypothesis produce smaller errors than those under the null hypothesis, and that the heterogeneous runs yield a higher probability of best model performance than the homogeneous runs. These results support the alternative hypothesis of localized incipient heterogeneity of the LEO soil, which facilitated generation of overland flow. This modeling study of the first LEO experiment suggests an important role of coupled water and sediment transport processes in the evolution of subsurface heterogeneity and on overland flow generation, highlighting the need of a coupled modeling system that integrates across disciplinary processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".