Quantile-Based Downscaling of Precipitation Using Genetic Programming: Application to IDF Curves in Saskatoon
Bibliographic record
Abstract
Intensity-duration-frequency (IDF) curves are commonly used in engineering planning and design. Considering the possible effects of climate change on extreme precipitation, it is crucial to analyze potential variations in IDF curves. This paper presents a quantile-based downscaling framework to update IDF curves using the projections of future precipitation obtained from general circulation models (GCMs). Genetic programming is applied to extract duration-variant and duration-invariant mathematical equations to map from daily extreme rainfall quantiles at the GCM scale to corresponding daily and subdaily extreme rainfall quantiles at the local scale. The proposed approach is applied to extract downscaling relationships and to investigate possible changes in the IDF curves for the City of Saskatoon, Canada. The results show that genetic programming is a promising tool for extracting mathematical mappings between extreme rainfall quantiles at the GCM and local scales. The duration-variant mappings were found to be more accurate than the duration-invariant relationships. Using the extracted relationships, future changes in IDF curves in the City of Saskatoon are estimated using projections obtained from the CGCM3.1 based on A1B, A2, and B1 emission scenarios. The results show that future IDF curves in the City of Saskatoon are subject to change, but the sign, magnitude, and uncertainty in the estimates of possible changes depend on the emission scenario, storm duration, return period, and mapping equations. Regardless of the emission scenario and/or the mapping relationships, the results of this study show increases in short-duration extreme rainfall with short return periods in Saskatoon. This study shows that the downscaling of extreme precipitation quantiles directly from the corresponding large-scale estimates can be an efficient approach when estimating the design precipitation values under climate change are sought.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".