9: Stormwater Infrastructure Precipitation Extremes and the Impacts of Climate Change on Stormwater Infrastructure in Washington State
Bibliographic record
Abstract
Stormwater management facilities are important elements of the civil infrastructure that can be sensitive to climate change, particularly to precipitation extremes that generate peak runoff flows. The design and anticipated performance of stormwater infrastructure is based on either the presumed characteristics of a “design rainstorm ” or the continuous simulation of streamflow driven by a time series of precipitation. Under either approach, a frequency distribution of precipitation is required, either directly or indirectly, together with an underlying assumption that the probability distribution of precipitation extremes is statistically stationary. This assumption, and hence both approaches, are called into question by climate change. We therefore examined both historical precipitation records and simulations of future rainfall to evaluate past and prospective changes in the probability distributions of precipitation extremes across Washington State. The historical analyses were based on hourly precipitation records for the time period 1949–2007 from weather stations surrounding three major metropolitan areas of the state: the Puget Sound region (including Seattle, Tacoma, and Olympia), the Vancouver (WA) region (including Portland, OR), and the Spokane region. Changes in future precipitation were simulated using two runs of the Weather Research and Forecast regional climate model (RCM) for the time periods 1970– 2000 and 2020–2050, statistically downscaled from the ECHAM5 and CCSM3 Global Climate Model and biascorrected
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".