Trade-offs Between Discretization Approaches in Urban Stormwater Modeling: Accuracy, Interpretability, and Practical Implications
Bibliographic record
Abstract
Stormwater models are important tools for urban drainage design, planning, and analysis, but their performance and interpretation depend heavily on how spatial discretization is handled. This study evaluates the influence of two common discretization strategies – topography- and sewer geometry-based – on hydrological representation and simulation accuracy in the Storm Water Management Model (SWMM), using a mixed urban and peri-urban watershed in London, ON, Canada. Leveraging long-term flow data from multiple monitoring locations across the watershed, we systematically evaluated the effects of discretization strategy across different rainfall conditions and land use settings (e.g., urban vs. peri-urban) using continuous and event-based simulations, as well as a fixed-effects regression model. The two models with different discretization approaches showed no significant differences in simulating outlet flows, indicating that discretization choice had limited impact on outlet flow simulations. However, the topography-based model yielded parameter values with greater hydrological interpretability and, accordingly, performed better at simulating flows at locations within the watershed. In addition, model performance was strongly influenced by rainfall depth and land use characteristics, with significantly improved results observed during larger storm events and in the urban watershed. The strengths and limitations of the two discretization approaches are laid out based on the study findings. Ultimately, the study demonstrates that discretization choice can significantly influence model structure, parameter interpretation, and spatial simulation accuracy, particularly in watersheds with heterogeneous topography and mixed drainage systems, and should therefore be carefully considered in stormwater modeling and scenario planning.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".