Hydrologic Connectivity and Flow Generation from California Vernal Pool, Swale, and Headwater Stream Complexes to Downstream Waters
Bibliographic record
Abstract
Abstract Many headwater wetlands are integrated into flowpath networks and can serve as sources of streamflow for downgradient waters. We demonstrate this with five years of data in vernal pool, swale, and headwater stream complexes in the Central Valley, California. Long-term United States Geological Survey data suggest that the mean flow duration from the smallest watersheds in this region, including those with vernal pool, swale, and headwater stream complexes, is ~ 85 days per year. Our data concur, indicating that the annual days of flow per year from our vernal pool, swale, and headwater stream complexes ranges from ~ 20–200, but is ~ 85 when annual precipitation is 100% of normal. Peak stages are evident first in vernal pools which then propagate sequentially downstream through swales, headwater streams, and to the Sacramento River at celerities of ~ 1-1.5 m/s, consistent with expected flood wave velocities. Geospatial analyses show that these vernal pool, swale, and headwater stream features cover > 4% of the study area. Our results suggest these systems can be significant sources of streamflow, and therefore play an important role in maintaining the chemical, physical, and biological integrity of downstream waters, which has important implications for the definition of waters of the United States subject to regulation under the Clean Water Act.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".