Statistical modeling of extreme rainfall processes in consideration of climate change
Bibliographic record
Abstract
Extreme rainfall events may have catastrophic impacts on the population and infrastructures, therefore it is essential to have accurate knowledge of extreme rainfall characteristics. Moreover, both the scientific community and policymakers have recently shown a growing interest in the potential impacts of climate change on water resources management. Indeed, changes in the intensity and frequency of occurrence of extreme rainfall events may have serious impacts. As such, it is important to understand not only the current patterns of extreme rainfalls but also how they are likely to change in the future. The objective of the present research is therefore to find the best method for estimating accurately extreme rainfalls for the current time period and future periods in the context of climate change. The analysis of extreme rainfall data from the province of Quebec (Canada) revealed that, according to L-moment ratio diagrams, the data may be well described by the Generalized-Extreme-Value (GEV) distribution. Results also showed that a simple scaling relationship between non-central moments (NCM) and duration can be established and that a scaling method based on NCMs and scaling exponents can be used to generate accurate estimates of extreme rainfalls at Dorval station (Quebec, Canada). Other results demonstrated that the method of NCMs can accurately estimate distribution parameters and can be used to construct accurate Intensity-Duration-Frequency (IDF) curves. Furthermore, a regional analysis was performed and homogenous regions of weather stations within Quebec were identified. A method for the estimation of missing data at ungauged sites based on regional NCMs was found to yield good estimates. In addition, the potential impacts of climate change on extreme rainfalls were assessed. Changes in the distribution of annual maximum (AM) precipitations were evaluated using simulations from two Global Climate Models (GCMs) under the A2 greenhouse gas emission scenario: the Coupled Global Climate Model version 2 (CGCM2A2) of the Canadian Centre for Climate Modelling and Analysis, and the Hadley Centre's Model version 3 (HadCM3A2). Simulations from these two models were downscaled spatially using the Statistical DownScaling Model (SDSM). A bias-correction method to adjust the downscaled AM daily precipitations for Dorval station was tested and results showed that after adjustments, the values fit the observed AM daily precipitations well. The analysis of future AM precipitations revealed that, after adjustments, AM precipitations downscaled from CGCM2A2 increase from current to future periods, while AM precipitations downscaled from HadCM3A2 show a mild decrease from current to future periods, for daily and sub-daily scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".