Modelling the effects of climate and landcover change on the hydrologic regime of a snowmelt-dominated montane catchment
Bibliographic record
Abstract
ABSTRACT Climate change poses risks to society through the potential to alter streamflow, and wildfires are projected to increase; however, little is known about their combined effects on hydrology. Using the Raven Hydrological Modelling Framework, we investigate the impacts of climate and landcover changes on the hydrology of a montane forested catchment in southern British Columbia, Canada. The combination of climate change and stand‐replacing landcover disturbance in middle and high elevations is predicted to advance the timing of peak flow by two to nine times (depending on climate projection) more than the advance from disturbance alone (7 days). The combined effects of climate and landcover disturbance on peak flow magnitude are predicted to be offsetting for frequent events, but additive for extreme events. There appears to be a dependency of extreme peak flows on the distribution of landcover. Extreme summer low flows are predicted to become commonplace by the 2050s. Low annual yield is predicted to become more prevalent by the 2050s, but then largely recover by the 2080s. The modelling suggests that landcover disturbance can have a mitigative influence on annual water yield, but minimally for summer low flow. The results highlight the importance of a multifaceted examination of complexity incorporating climate change, landcover change and a large range of hydrological indicators. Moreover, the results indicate that management strategies must assess the interplay of future climate, landcover condition and societal values on watershed risk.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".