Can isolated and riparian wetlands mitigate the impact of climate change on watershed hydrology? A case study approach
Bibliographic record
Abstract
The effects of wetlands on stream flows are well established, namely mitigating flow regimes through water storage and slow water release. However, their effectiveness in reducing flood peaks and sustaining low flows is mainly driven by climate conditions and wetland type with respect to their connectivity to the hydrographic network (i.e. isolated or riparian wetlands). While some studies have demonstrated these hydrological functions/services, few of them have focused on the benefits to the hydrological regimes and their evolution under climate change (CC) and, thus, some gaps persist. The objective of this study was to further advance our knowledge with that respect. The PHYSITEL/HYDROTEL modelling platform was used to assess current and future states of watershed hydrology of the Becancour and Yamaska watersheds, Quebec, Canada. Simulation results showed that CC will induce similar changes on mean seasonal flows, namely larger and earlier spring flows leading to decreases in summer and fall flows. These expected changes will have different effects on 20-year and 100-year peak flows with respect to the considered watershed. Nevertheless, conservation of current wetland states should: (i) for the Becancour watershed, mitigate the potential increase in 2-year, 20-year and 100-year peak flows; and (ii) for the Yamaska watershed, accentuate the potential decrease in the aforementioned indicators. However, any loss of existing wetlands would be detrimental for 7-day 2-year and 10-year as well as 30-day 5-year low flows.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".