Estimation of the summer-fall PMP and PMF of a northern watershed under a changed climate
Bibliographic record
Abstract
[1] This study is focused on assessing a summer-fall probable maximum precipitation (PMP) under recent climate conditions and then applying it under a future projected climate using output information from a regional climate model. The estimated PMPs are forced into a hydrological model to investigate potential changes in probable maximum flood (PMF) values. The PMP method is based on the moisture maximization method developed by the World Meteorological Organization. Precipitable water amounts are evaluated using data from the Canadian Regional Climate Model. The approach was tested on the Manic-5 River basin in Canada. Results show the PMP intensity could increase by 0.5–6% for 48 h and 72 h PMP values by the 2071–2100 horizon. These PMP values were used to assess projected PMF values. A lumped conceptual hydrological model was calibrated under recent climate values and model parameters were kept unchanged for modeling future hydrological regimes. Hydrological modeling results indicate modest changes of the PMF values for future climate projections, with no clearly identified upward or downward trends. The study also highlighted the need for a coherent approach to estimate PMF values under future climate conditions using projected PMP estimates, since the current practice of simulating the PMF value by inserting half of a PMP six days prior to the PMP event in a meteorological time series did not produce consistent results. An approach based on a random insertion of the PMP into a meteorological time series is a promising avenue to explore.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".