Water resources management modelling for analyzing flood mitigation projects along the Elgin Creek
Bibliographic record
Abstract
Flooding is a significant natural hazard in Manitoba that has caused severe damage to properties and infrastructure. One of the flood mitigation projects in Southern Manitoba is to install new detention dams along the stream to flood regions that have minimal impact on the infrastructure but can hold a relatively large amount of water compared to the total flow volumes in the basin. Water resources management models, such as MODSIM-DSS can be used to identify the location of such control points because they can accurately simulate flows and consider the existing infrastructure in the basin, where the potential damage could occur. In this research, MODSIM-DSS is configured for simulating the movement of water along the Elgin Creek and for identifying the location of potential dams that can be installed in the future to mitigate flood impacts in the region. Elgin Creek has 24 intersections with provincial and municipal roads, some of which were washed out during the floods over the past two decades. The developed model simulates the stage-storage-discharge (i.e., the relationship between the flow rate and the corresponding water level) at the intersection of the stream network and roads where road culverts pass the water from upstream to downstream. To run MODSIM-DSS, the physical characteristics of the seasonal reservoirs created upstream of each water control structure, such as water elevation, flooded areas and volumes, and hydraulic capacity, need to be analyzed using LiDAR data in GIS software. The inflow scenarios for MODSIM-DSS are the estimated 2-, 5-, 10-, and 100-year events, each of which are 1-month long with a 5-minute timestep. In addition, MODSIM-DSS is applied to evaluate the effectiveness of potential detention dams in mitigating flood damages, using risk analysis as a conceptual framework. Results show that, under the 100-year flood event, MODSIM-DSS could accurately predict the 8 roads in the basin that were historically washed out during an 83-year flood. Moreover, results show that the proposed detention dams could reduce the peak flow of water during a flood event by up to 85%, which can reduce the risk of damage of downstream road infrastructure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".