Advancing blue-green infrastructure design with synthetic 3D drainage channels: A scenario-based flood model in Nova Scotia, Canada
Bibliographic record
Abstract
Urbanized riverscapes are facing challenges due to hydrological changes. Adjusted flow regimes and imperviousness are contributing to increased flood risks resulting from gray infrastructure and strained subgrade drainage systems. Here a new methodology is presented for designing synthetic 3D drainage channels as blue-green infrastructure to enhance multidisciplinary decision-making for sustainable urban drainage systems planning and elements of nature-based stormwater management. LiDAR derived digital elevation models and River Builder software were used to generate four unique drainage channel scenarios with different surface geometries and vegetative cover types for flood modelling. Flood risks were assessed by fluvial simulation responses to specific channel elements and the design process may translate to real-world applications. Fluvial simulations were compared to evaluate how flood inundation patterns and flow velocities responded to morphology changes and roughness coefficients. Results suggest that incorporating geomorphic principles into open drainage channels can advance blue-green infrastructure design by reflecting more natural morphological elements and improve stakeholder engagement that is well suited for nature-based solutions. By combining high-resolution LiDAR data and process-based River Builder functions, the methodology presents a design tool for interactive investigation, adjustment, and communication of continuous 3D channel design scenarios. Although further site-specific studies are needed and additional metrics may be applied, this paper demonstrates a flexible framework to support sustainable urban drainage systems and nature-based stormwater management approaches in urbanized riverscapes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".