Effect of bedrock permeability on stream base flow mean transit time scaling relations: 1. A multiscale catchment intercomparison
Bibliographic record
Abstract
Abstract The effect of bedrock permeability and underlying catchment boundaries on stream base flow mean transit time (MTT) and MTT scaling relationships in headwater catchments is poorly understood. Here we examine the effect of bedrock permeability on MTT and MTT scaling relations by comparing 15 nested research catchments in western Oregon; half within the HJ Andrews Experimental Forest and half at the site of the Alsea Watershed Study. The two sites share remarkably similar vegetation, topography, and climate and differ only in bedrock permeability (one poorly permeable volcanic rock and the other more permeable sandstone). We found longer MTTs in the catchments with more permeable fractured and weathered sandstone bedrock than in the catchments with tight, volcanic bedrock (on average, 6.2 versus 1.8 years, respectively). At the permeable bedrock site, 67% of the variance in MTT across catchments scales was explained by drainage area, with no significant correlation to topographic characteristics. The poorly permeable site had opposite scaling relations, where MTT showed no correlation to drainage area but the ratio of median flow path length to median flow path gradient explained 91% of the variance in MTT across seven catchment scales. Despite these differences, hydrometric analyses, including flow duration and recession analysis, and storm response analysis, show that the two sites share relatively indistinguishable hydrodynamic behavior. These results show that similar catchment forms and hydrologic regimes hide different subsurface routing, storage, and scaling behavior—a major issue if only hydrometric data are used to define hydrological similarity for assessing land use or climate change response.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".