Impact of urban drainage system malfunctions on pluvial flooding – Peri-urban study site in Austria
Bibliographic record
Abstract
Study region The study site is the small city of Feldbach, Austria (1.3 km²), recently affected by urban flooding. Study focus Urban drainage system (UDS) infrastructures are prone to malfunctions, which can reduce flow capacity and lead to increased sewer flooding during rainfall events. These malfunctions can cause urban flooding during moderate events where flooding might not otherwise occur or exacerbate flood hazards during heavy rainfall. New hydrological insights for the region This study investigates the impact of malfunctions in central “grey” UDS infrastructures and decentralized “blue-green” infrastructures on urban flood hazard using an integrated 1D/2D urban flood model (PCSWMM). A total of 25 scenarios, including reference and malfunction cases, were analyzed under five rainfall events of varying return periods. The results reveal that malfunctions in central grey infrastructures significantly increase flooded areas by up to 85 %, thereby intensifying urban flood risk. In contrast, malfunctions in decentralized “blue-green” infrastructures have negligible impact (change in flooded area <1 %), primarily due to their limited implementation in the study area. The average increase in flooded area due to drainage system malfunctions decreases with increasing rainfall return periods, from 25.3 % to 4.75 %. This indicates that malfunctions have a greater importance during smaller-scale events. These findings underscore the critical role of proactive sewer asset management in preventing malfunctions and reducing the risk of urban pluvial flooding.
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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) | 0.001 | 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".