Risk-based quantification of the impact of climate change on storm water infrastructure
Bibliographic record
Abstract
Storm water detention ponds are usually designed to store-and-release the runoff of extreme rainfall events based on a selected return period, e.g., 100 years. The design storm is typically a recorded historical event or one that is extracted from historical intensity–duration–frequency (IDF) curves. In essence, the selected storm and the resulting design are deterministic. In this research, the inevitable natural weather variability and its impact on the uncertainty of extreme events are simulated and quantified. This study builds on the results of a previous study where a stochastic weather generator, LARS-WG, was used to generate an ensemble of series with a 30-year length of hourly rainfall in the city of Saskatoon, Canada, based on the statistical properties of historical rainfall. Here, the most critical day (24-h rainfall) of each of the series is identified as a possible realization of the design storm. The runoff of each realization of the storm events is routed to a storm water pond in Saskatoon using the XPSWMM model. The critical runoff volume collected in the pond throughout the 24-h duration is also identified. Empirical probability distributions are fitted to the critical values of runoff volumes collected in the pond and compared with the current design storage. Exceedance probabilities and expected flood risk are estimated from the probability distributions for the baseline period (1960–1990), as well as under three projected future (2014–2100) scenarios of climate change (RCP 2.6, 4.5, and 8.5). Along with the magnitude of expected risk, this method provides the probability of the infrastructure’s failure due to uncertainty. The proposed risk-based approach presented in this study provides a way for municipalities to quantify the risk associated with their selected design values and for tangible and meaningful interpretation of the risks that projected climate change might pose on storm water infrastructure. The main finding of this study is that the distribution of rain throughout the storm event may play a more important role than the total rainfall depth when water ponding/flooding is the major concern. It is further concluded that risk analysis must be tailored to the type of infrastructure under consideration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".