The sedimentology, geomorphology, and hydrology of extreme floods on the Assiniboine River, southwestern Manitoba
Bibliographic record
Abstract
River flooding is the most common natural disaster in Canada, particularly in the prairie provinces. Floods occur when river discharge exceeds the volume a channel is capable of conveying. Extreme floods on meandering rivers can cause significant geomorphic change, however, the extent to which the sedimentary record of extreme floods differs from that of annual floods is unclear. In the past decade, the Assiniboine River in southwestern Manitoba has experienced three >200-year-recurrence interval floods. Recent studies of local and global climatic changes have suggested a potential for more extreme precipitation events that lead to increased flood risk. The objectives of this thesis are to: i) investigate the sediments deposited by these extreme floods, and ii) quantify the drivers of flood hazard on the Assiniboine River. The goal is to refine our understanding of the sedimentary record of multiple extreme floods and evaluate the relative contributions of in-channel sedimentation and climatic factors to recent flooding events. This thesis combines qualitative field-based investigations of point-bar deposits with quantitative stream-gauge data analysis to achieve these goals. Point bars in Spruce Woods Provincial Park have undergone extensive lateral migration over the past decade, with over 80% of migration occurring during three extreme flood events. The sedimentation associated with each flood event revealed that in some instances, extreme floods can generate an atypical coarsening upward profile of point-bar deposits. However, the deposits do not differ significantly from annual sedimentation, rather they are deposited more rapidly and in larger volumes. Data analysis of 60 years of stream-gauge data reveals that the greatest driver of flood hazard is an increase in flow frequency, however channel capacity changes have had a lesser but significant impact on decreasing the flood frequency. Climatic changes, specifically intense rainfall events, are a potential driver of the increased flow frequency, but further work is required to evaluate anthropogenic factors that also contribute to overland flow (e.g., urbanization, monoculture farming, etc.). The results of this thesis contribute to the refinement of point-bar models, specifically the recognition criteria for extreme floods, and offer insight into the drivers of flood hazard on the Assiniboine River.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".