Quantifying the climate change-induced variations in Saskatoon's Intensity-Duration-Frequency curves using stochastic rainfall generators and K-nearest neighbors
Bibliographic record
Abstract
Intensity-Duration-Frequency (IDF) curves are among standard design criteria for various engineering applications, such as storm water management systems. Warming climate, however, changes the extreme rainfall quantiles represented by the IDF curves. This study attempts to construct the future IDF curves under possible climate change scenarios. For this purpose, a stochastic rainfall generator is used to spatially downscale the daily projections of Global Climate Models (GCMs) from coarse grid resolution to the point scale. The stochastically downscaled daily rainfall realizations can be further disaggregated to hourly and sub-hourly rainfall series using a deterministic disaggregation scheme developed based on the K-Nearest Neighbor (K-NN) method. We applied this framework for constructing the future IDF curves in the city of Saskatoon, Canada. As a model development step, the sensitivity of the K-NN disaggregation model to the number of nearest neighbors (i.e. window size) is evaluated during the baseline periods. The optimum window size is assigned based on the performance in reproducing the historical IDF curves. The optimum windows identified for 1-hour and 5-min temporal resolutions are then used to produce the future hourly and consequently, 5-min resolution rainfall based on the K-NN simulations. By using the simulated hourly and sub-hourly rainfall series and the Generalized Extreme Value (GEV) distribution future changes in IDF curves and associated uncertainties are quantified using a large ensemble of projections obtained for the CGCM3.1 and HadCM3 based on A1B, A2 and B1 emission scenarios in case of CMIP3 and RCP2.6, RCP4.5, and RCP8.5 in case of CMIP5 datasets. The constructed IDF curves for the city of Saskatoon are then compared with corresponding historical relationships at various durations and/or return periods and are discussed based on different models, emission scenarios and/or simulation release (i.e. CMIP3 vs. CMIP5).
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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 | 0.001 | 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) | 0.001 | 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".