Evaluation of the Impact of Rainfall Increases on Runoff in Urban Watersheds
Bibliographic record
Abstract
Existing drainage systems, which were already susceptible to degradation, are now facing even greater challenges due to the projected increase of rainfall extremes related to urbanisation and climate change. This could compromise their overall effectiveness. This project aims to develop a novel methodology for assessing the impacts of rainfall increases on urban runoff. The proposed methodology relies on modeling a significant number of artificial urban watersheds using the United States Environmental Protection Agency's (US EPA) Storm Water Management Model (SWMM) software, and the use of rainfall data from different meteorological stations with a variety of durations and frequencies. Results showed that using a simple increase of 18% in simulated rainfall led to larger relative increases in computed peak flow, with a median variation of 24.7%, and median variations in computed runoff volumes of 18.0%. The dispersion of obtained results depends on the intensity (0.05% to 2.58%), frequency (0.05% to 3.22%), and duration (0.16% to 1.49%) of the rainfall event. This suggests that there is no direct link between the rainfall increase factor and the amount of urban runoff. A sensitivity analysis (ANOVA) was conducted to evaluate how watershed characteristics influence runoff caused by changing rainfall regimes. It was found that among all the artificial watershed criteria tested, impermeability, area, and slope have a greater influence on calculated peak flow changes under the tested rainfall increases. Additionally, impermeability emerged as the most critical characteristic affecting calculated runoff volume across all stations, although certain stations exhibited variations where soil type played a more prominent role. This shows that the impacts of climate change could affect watersheds differently depending on their characteristics. This study emphasizes the importance of reducing soil impermeability in the urban water cycle, notably using blue-green infrastructures, to mitigate the impact of climate change on existing urban drainage systems.
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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 | 0.003 | 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 | 0.000 |
| 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".