Hydrologic and Hydraulic Impact of Climate Change on Lake Ontario Tributary
Bibliographic record
Abstract
Climate model projections indicate that the frequency and magnitude of hydrological extremes will increase in a future climate due to increasing concentration of greenhouse gases. Increase in precipitation depth will lead to higher peak flows, and will bring floods with higher inundation depths and larger extends. This study involves the climate change impact analysis of design storms, peak flows and flooding scenario for the Clearview Creek drainage area located in Southern Ontario, Canada. First, the storm depths for different return periods and durations were calculated from the observed rainfall data and the North American Regional Climate Change Assessment Program (NARCCAP) climate simulations. The storm depths were calculated by using the best fitted distribution among twenty seven distributions. The design storm depths calculated from the observed and climate model simulated data are used as input into an existing Visual OTTHYMO model of the study area for flow simulation. The simulated peak flows for 24hr Storm of different return periods are used as input in the HEC-RAS model for hydraulic analyses. Frequency analysis results show that the storm depths are predicted to increase significantly under future climate. Simulated flow results show an increase of peak flows ranging from about 26 % to 64% for 2yr and 100yr return periods at the outlet of the Creek. Finally, the analyses of flooding scenario revealed an average increase of water surface elevation and extents by 30 cm and 37.1 m, respectively, for a 100 year return period flood. It is also revealed that the variability of flow simulated by hydrologic model and flow area simulated by the hydraulic analyses tool are much higher than the variability of the storm depths under future climate condition.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".