A Flood Risk Assessment of the LaHave River Watershed, Canada Using GIS Techniques and an Unstructured Grid Combined River-Coastal Hydrodynamic Model
Bibliographic record
Abstract
A flexible mesh hydrodynamic model was developed to simulate flooding of the LaHave River watershed in Nova Scotia, Canada, from the combined effects of fluvial discharge and ocean tide and surge conditions. The analysis incorporated high-resolution lidar elevation data, bathymetric river and coastal chart data, and river cross-section information. These data were merged to generate a seamless digital elevation model which was used, along with river discharge and tidal elevation data, to run a two-dimensional hydrodynamic model to produce flood risk predictions for the watershed. Fine resolution topography data were integrated seamlessly with coarse resolution bathymetry using a series of GIS tools. Model simulations were carried out using DHI Mike 21 Flexible Mesh under a variety of combinations of discharge events and storm surge levels. Discharge events were simulated for events that represent a typical annual maximum runoff and extreme events, while tide and storm surge events were simulated by using the predicted tidal time series and adding 2 and 3 m storm surge events to the ocean level seaward of the mouth of the river. Model output was examined and the maximum water level for the duration of each simulation was extracted and merged into one file that was used in a GIS to map the maximum flood extent and water depth. Upstream areas were most vulnerable to fluvial discharge events, the lower estuary was most vulnerable to the effect of storm surge and sea-level rise, and the Town of Bridgewater was influenced by the combined effects of discharge and storm surge. To facilitate the use of the results for planning officials, GIS flood risk layers were intersected with critical infrastructure, identifying the roads, buildings, and municipal sewage infrastructure at risk under each flood scenario. Roads were converted to points at 10 m spacing for inundated areas and appended with the flood depth calculated from the maximum water level subtracted from the lidar digital elevation model.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".