Scale‐dependence of natural variability of flow regimes in a forested landscape
Bibliographic record
Abstract
The ecological integrity of riverine ecosystems is dependent upon the natural flow regime of the river system. Maintaining natural variability in the flow regime is critical for conserving the structure and function of riverine ecosystems. This research seeks to determine relations between natural variability in the flow regime and basin scale. A distributed hydrologic model was used to characterize the natural flow regime of basins from first to fifth order within tributaries of the Batchawana River in the Algoma Highlands of central Ontario using the range of variability approach (RVA). A 30‐year simulated flow record was used to calculate natural variability in the flow regime, defined by the S80 [(90th percentile – 10th percentile)/median]. Flow variability under wetter conditions was similar across all basins, regardless of scale. Conversely, flow variability under drier conditions was scale‐dependent, with smaller basins (<600 ha) showing a large range in variability and larger basins (>600 ha) showing a smaller range in variability that converged toward a constant with increasing area. The effect of basin area on flow variability suggested the existence of a representative elementary area (REA). Within the REA, morphometric sources of natural variability were determined through multivariate regression analyses. A combination of indices describing the near‐stream riparian area within a basin, median basin residence time, and basin curvature was significantly related to flow variability under drier conditions. These findings present a potential management template for establishing reference conditions against which impacts of disturbance on flows throughout a regional drainage basin may be measured.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".