The Effects of Climate Change on Flood-Generating Mechanisms in the Assiniboine and Red River Basins
Bibliographic record
Abstract
Flood events are influenced by terrestrial factors including land cover, land and water management, watershed physiographic features, and hydro-climatic components including snowmelt and precipitation. In Canada, flooding is a frequent and prominent natural disaster, which is modulated by different flood-generating mechanisms. In this study, we assess the intensity and frequency of three flood-generating mechanisms including Rain on Snow (ROS), intense rainfall, and snowmelt-driven flood events over the Assiniboine-Red River basin, which is one of the most flood-prone regions in Canada and located in the Lake Winnipeg watershed. We downscale and bias correct seven Global Climate Models (GCMs) that participated in CMIP6 using two methods of Bias Correction/Constructed Analogues with Quantile mapping (BCCAQ) (BCCAQ) and Multivariate Bias Correction (MBC). The observed and downscaled climate variables (precipitation and temperature) are used to drive a process-based distributed snow model to evaluate the changes in flood-generation mechanisms in the historical and future periods. The projected future changes are analyzed under policy-relevant global mean temperature (GMT) increases from 1.0 °C to 3.0 °C above the pre-industrial period. Overall, all models project higher regional temperature increases compared to the global mean with warmer and wetter winters. The snow model results indicate future decreases in the snow cover duration, snowmelt rate, and snow water equivalent (SWE), and earlier shifts in the maximum SWE timing. Moreover, both the intensity and frequency of ROS events increase in all seasons except summers. However, the increases in the rain and snowmelt events are mostly projected to occur in the spring.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".