Halifax Harbour extreme water levels in the context of climate change: scenarios for a 100-year planning horizon
Bibliographic record
Abstract
A Halifax Harbour Plan is being developed within the context of the 25-year Regional Municipal Planning Strategy in the Halifax region of Nova Scotia. This strategy recognizes the importance of climate change and the need for a precautionary approach to minimize negative impacts of rising sea level. Airborne LiDAR data were acquired in 2007 to produce a high-resolution digital elevation model (DEM) as a basis for mapping flood limits. The selection of flood levels for adaptation planning required an understanding of present and future sea-level rise (SLR), vertical land motion, extreme water levels (combined tide and surge), harbour seiche and wave runup. Relative sea level in Halifax Harbour has risen at 3.2±0.13 mm/a since 1920 through a combination of regional subsidence (1.6±0.3 mm/a) and local SLR (~1.6 mm/a). Scenarios of future extreme water levels were developed using (1) current local SLR, (2) the upper limit of the 2007 IPCC projections, and (3) a higher projection based on a growing scientific consensus that the IPCC upper limit may have been overly conservative. These projections of SLR (0.16, 0.59, and 1.3 m over ~100 years) were combined with regional subsidence and extreme water levels for 2-, 10-, and 50-year events derived from a generalized extreme values distribution of annual extreme water levels in Halifax Harbour. The resulting water levels were applied to the LiDAR DEM to visualise the extent and depth of flooding for each event. A plausible upper limit with very low probability was developed by superimposing the record storm surge on the highest tide with a sea-level rise of 1.3 m. A buffer ranging from 1 to 2 m was considered to account for observed values of seiche and wave runup in various parts of the harbour. This study provides the scientific basis for a set of plausible scenarios for a 100-year planning horizon, but the choice of water level for planning purposes is a policy decision.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".