Flood analysis and flood projections under climate change in New Brunswick
Bibliographic record
Abstract
Floods events are a key component in river engineering including the design and risk assessment of various projects. In this study, a flood frequency analysis was carried out to determine flood characteristics in New Brunswick under present and future climate. For current flood characteristics, an analysis was carried out using 56 hydrometric stations across the province using the Generalized Extreme Value (GEV) distribution and the three-parameter lognormal distribution function. A regional flood frequency analysis was also carried out using regression equations. Results showed that current regional flood equations were very consistent among distribution functions. Results were also consistent with previous studies. To study floods under climate change, seven catchments were selected within the province and these catchments were further analyzed using artificial neural network (ANN) models for two climate scenarios. As such, future climate data were extracted from the third-generation Coupled Climate Model (CGCM3.1) under the greenhouse gas emission scenarios B1 and A2. The climate variables (temperature and precipitation) were downscaled using the delta change approach, and future river discharges were predicted. A frequency analysis was then carried out on these seven stations using the GEV distribution function. Results showed that for the period 2010–2100, average temperatures are projected to increase between 2.9°C (B1) and 5.2°C (A2) in New Brunswick. As for precipitation, the mean annual precipitation showed an increase of 9 to 12% compared to current conditions. Results also showed an increase in flood flows. The increase in low-return floods (e.g. 2-year) was generally higher than the increase of higher return floods (e.g. 100-year). Depending on the scenario and the future time period, the increase in low-return floods was about 30%, and about 15% for higher return floods. A Regional Climate Index (RCI) was used to links floods to their frequency under future climate scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".