Understanding the Factors That Influence Headwater Stream Flows in Response to Storm Events1
Bibliographic record
Abstract
Stanfield, Les W. and Don A. Jackson, 2011. Understanding the Factors That Influence Headwater Stream Flows in Response to Storm Events. Journal of the American Water Resources Association (JAWRA) 1-22. DOI: 10.1111/j.1752-1688.2010.00518.x Abstract: Headwater drainage features (first- to second-order streams) are the capillaries of the landscape that, among other things, moderate the timing and volumes of water available to the riparian and aquatic ecosystems. How these features respond to summer rainfall is poorly understood. We studied how geology and an index of land use/land cover influenced peak flows following rainfall events in 110 headwater stream sites that were studied over a four-month period during a drought year. Highest peak flows were observed in the most urbanized catchments and in poorly drained soils, but specific responses were variable depending on both geology and land disturbance. Redundancy analysis indicated that both surficial geology and land disturbance were important factors influencing peak flows under drought conditions. We conclude that responses of these headwater streams to individual storms during drought conditions are unpredictable from data collected using our methods, but increased peak flows were associated with increased urban and agricultural development, but mitigated by surficial geology. These findings demonstrate the challenges to accurately predict flow conditions in headwater streams during periods of extreme weather that concurrently have the greatest potential effect on biota. The combination of these challenges and importance of such events indicates the need to develop new approaches to study and manage these resources.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".