Novel methods for estimating extreme design rainfalls at gauged and ungauged locations in a changing climate
Bibliographic record
Abstract
Information on the variability of extreme rainfalls in time and in space is of critical importance for many types of hydrologic studies. In addition, in recent years, climate change has been recognized as having a profound impact on the hydrologic cycle at different temporal and spatial scales. The present study is therefore was carried out to develop appropriate methods for improving the accuracy of design rainfall estimation at gauged and ungauged locations in the current climate as well as in the context of climate change. This study can be divided into five primary parts.The first part presents a general procedure for assessing systematically the performance of different commonly used probability distributions in extreme rainfall frequency analyses based on their descriptive as well as predictive abilities. To test the feasibility of the proposed procedure, an illustrative application was carried out using annual maximum rainfall data from a network of 21 raingages located in the Ontario region in Canada. Results have indicated that the Generalized Extreme Values (GEV), Generalized Normal (GNO), and Pearson Type 3 (PE3) models were the best models for describing the distribution of daily and sub-daily annual maximum rainfalls in this region.The second part introduces a new probability-weighted-moment-based scaling Generalized Extreme Value (GEV/PWM) distribution model for modeling rainfall extremes across a wide range of time scales. A comparative study was then carried out to asses the performance of the proposed model using the available extreme rainfall data from a network of 74 raingages located across Canada. Results of this comparative study have indicated the superior performance of the proposed GEV/PWM model as compared to the existing models based on an extensive set of graphical and numerical comparison criteria.The third part proposes an innovative spatio-temporal statistical downscaling approach for establishing the linkage between daily extreme rainfalls at regional scales and daily and sub-daily extreme rainfalls at a given local site. The performance of the proposed method was assessed for a case study in Ontario using observed extreme rainfall data from seven raingages and climate simulation outputs from 21 different Global Climate Models that have been downscaled to a regional 25-km scale. Results based on various graphical and numerical comparison criteria have indicated the feasibility and accuracy of the proposed downscaling approach. The fourth part introduces new scale-invariancce models for modeling rainfall extremes across a wide range of time scales. The present study presented some general mathematical frameworks for three commonly-used probability distributions in hydrologic frequency analyses such as the Generalized Logistic (GLO), GNO, and PE3 using both non-central moment (NCM) and PWM estimation methods. Results of an illustrative application using the observed IDF data from a network of 74 raingages located across Canada have indicated the feasibility and accuracy of these new scale-invariance models. Finally, the fifth part consists of developing a convenient decision-support tool for the construction of robust rainfall IDF relations in consideration of model uncertainty and potential climate change impacts for the design of urban water systems at a given location of interest. More specifically, this tool can readily be used to identify in an objective and systematic manner the most suitable probability models for accurate and robust estimation of design rainfalls. In addition, in the context of a changing climate, the proposed tool was able to establish the linkage between large-scale climate predictors given by GCMs and the daily and sub-daily extreme rainfalls at a given site
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".