Frequency of floods in a changing climate: a case study from the Red River in Manitoba, Canada
Bibliographic record
Abstract
Abstract. Spring flooding in the Red River basin is a recurrent issue in the Province of Manitoba, Canada. There have been a number of flood events in recent years and climate change has been suggested as a potential cause. This paper employs a relatively simple model for predicting changes in the frequency distribution of annual spring peak discharge of the Red River as a response to increased GHG concentrations. A regression model is used to predict spring peak flow from antecedent precipitation in the previous fall, winter snow accumulation, and spring precipitation. Data from the Coupled Model Intercomparison Project – Phase 5 (CMIP5) are used to estimate changes in the predictor variables and this information is then employed to derive flood distributions for future climate conditions. Most climate models predict increased precipitation during winter months but this trend is partly offset by a shorter snow accumulation period and higher winter evaporation rates. The means and medians of an ensemble of 16 climate models do not suggest a particular trend toward more or less frequent floods of the Red River. However, the ensemble range is relatively large, highlighting the difficulties involved in estimating changes in extreme events.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".