Modelling the impacts of projected climate-driven changes in return period rainfall on peak flows of the Upper Humber River, Newfoundland
Bibliographic record
Abstract
As climate change threatens the resilience and well-being of rural communities across Canada, this study seeks to fill gaps in knowledge by investigating the impacts of potential future hydrological intensification on peak flows of the Upper Humber River in rural Newfoundland. A basin model of the Humber River watershed was generated using the Hydrologic Engineering Center's Hydrological Modeling Software (HEC-HMS), utilizing available Digital Elevation Model (DEM), soil and land use data. Observed rainfall and streamflow data were used to calibrate and validate the model, which was found to simulate streamflow with statistical accuracy. Return period peak flows under current climate conditions were estimated using historical rainfall data, producing results consistent with previous studies. Estimated return period peak flows for the 2020s, 2050s, and 2080s utilized rainfall projections considering an intermediate greenhouse gas (GHG) emission scenario (RCP 4.5). This study projected a rise in the frequency, magnitude, and uncertainty of peak flows with the projected progression of climate change, with the most pronounced changes occurring later in the century. At Humber River inlet into Deer Lake, a 40% increase in the magnitude of median 100-year return period peak flows was projected from the baseline historical period (1966-2014) to the 2080s, emphasizing the urgent need for comprehensive planning, continued climate change research, and the implementation of targeted flood adaptation strategies within the Humber River watershed to reduce the potentially catastrophic impacts of projected future climate change.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".