The influences of catchment geomorphology and scale on runoff generation in a northern peatland complex
Bibliographic record
Abstract
Abstract We computed daily discharge (Q) versus gross drainage area (GDA) regression analyses for the 2009 and 2010 growing seasons for six small to medium headwater catchments at a northern peatland complex in the James/Hudson Bay lowlands. Temporal dynamics of the daily goodness of fits (R2) between Q and GDA were then examined to identify the most relevant conceptual model of runoff generation in this landscape. We observed high R2 values during low flow conditions (mean R2 = 0.93 for 2009 and 2010). During wetter periods and in particular during large runoff events, the relationship degraded rapidly and consistently, suggesting differences in quickflow response among the gauged catchments. At low flows, the six catchments generated equivalent amounts of runoff (mm), leading to a strong Q–GDA relationship. During high flows, total growing season runoff increased systematically with GDA between 8 and 50 km2 and then decreased with further increases in GDA. These differences were responsible for the observed breakdown in the daily Q–GDA relationships and also resulted in significant differences in total runoff among the six catchments during the wetter year. Quantitative landscape analysis using a 5‐m resolution Light Detection and Ranging (LiDAR) digital elevation model revealed that near‐stream zone characteristics vary systematically with scale in a manner that is consistent with the observed patterns of quickflow runoff response. In this northern peatland complex, fast‐responding flowpaths in the spatially discrete near‐stream zones may be the key determinant of catchment runoff efficiency at the small to medium (~10 to ~200 km2) headwater catchment scales analysed here. Moreover, the relatively organized drainage patterns observed in this study are consistent with our understanding of ecohydrological feedbacks driving geomorphic evolution of northern peatlands. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".