On the relationship between flood and contributing area
Bibliographic record
Abstract
Abstract Although it is well known that the vast majority of the time only a portion of any watershed contributes run‐off to the outlet, this extent is rarely documented. Also, the power law form of the streamflow and contributing area (Q‐Ac) relationship has been known for a half century, but it is uncommon for it to be quantified, and time series of contributing area extensive enough to calculate its frequency distribution are almost non‐existent. Data from the Canadian Prairies, where there are extensive estimates of contributing area during the median annual flood, imply that the power law coefficient for any Q‐Ac curve is a function of flow magnitude and return period. These data also suggest that regional flood frequency curves are a construct of Q‐Ac curves from individual basins. This paper will discuss research that attempted to reproduce the Q‐Ac curves for the La Salle River Watershed with a semidistributed numerical hydrological model, MESH‐PDMROF. The model simulated streamflow reasonably well (Nash Sutcliffe values = 0.62) compared with published examples of comparable models applied in the region. Estimates of the coefficient and exponent of the Q‐Ac power law function ranged from 0.08–0.14 and 0.9–1.12, respectively. These exponent values were lower than those of regional flood frequency curves and support the theory that regional flood frequency curves are a construct of Q‐Ac curves. Simulations of the area contributing to the median annual flood were lower (0.3) than those derived from independent topographic analysis (0.9) described in earlier literature though there is uncertainty in both these estimates. This uncertainty was extended across the flood frequency distribution and may be too large to definitively verify the study hypothesis.
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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 | 0.017 |
| 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 | 0.001 |
| 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".