Empirical analyses of the hydrological influence of a riparian wetland in the Montmorency Forest, Quebec, Canada
Bibliographic record
Abstract
Riparian wetlands are strongly connected to water bodies and their capacity to provide hydrological services varies greatly over time. A better understanding of the temporal variability of this connectivity is necessary to improve our knowledge of how and when wetlands can influence floods. This work aims to empirically quantify the influence of a riparian wetland on floods in a small watershed located in the Montmorency Forest, Québec, Canada. To this end, 15-minute hydrometric data were retrieved from three Quebec government stations: one located upstream of a riparian wetland, one located downstream, and one located nearby in a control watershed with similar physical characteristics to the other two. With these data, a total of 229 flood events were identified between 1996 and 2022. The maximum flows for each event and the timing of each flood peak were isolated. Peak flow reductions and delays between the arrival of the flood peak for each flood event and between all catchments were calculated. Pre-flood flow, flood volume, total precipitation causing the flood, average water temperature during the flood and water level within the riparian wetland were used to explain the peak flow reduction patterns. The results show that the wetland reduces peak flow by a median of 27 % with a maximum reduction of 66 %. However, for some events there is an increase in peak flow after passing through the wetland. Delays in the arrival of the flood peak show a median of 135 minutes with a maximum value of 1300 minutes. Hysteresis patterns were observed between the river flow measured downstream of the riparian wetland and the water level measured in the wetland, indicating that the previous wetness of the riparian wetland influences the peak flow reduction capacity of the riparian wetland. Further hydrological and biogeochemical monitoring will be carried out at this site and will be used to improve our understanding of hydrological and biogeochemical processes in riparian wetlands, which are still poorly understood. Any future results will be compared with results from several sites in the Saint-Lawrence Lowlands, Québec, Canada, where an integrated wetland water and carbon cycle monitoring program is currently underway.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".