The Comparison of GEV, Log-Pearson Type 3 and Gumbel Distributions in the Upper Thames River Watershed under Global Climate Models
Bibliographic record
Abstract
The increase in greenhouse gas emissions has had a severe impact on global temperature, and is affecting weather patterns worldwide. With this global climate change, precipitation levels are changing, and in many places are drastically increasing. The need to be able to accurately predict extreme precipitation events is imperative in designing for not only the safety of infrastructure, but also people’s lives. To predict these events, the use of historical data is necessary, along with statistical distributions that are used to fit the data.\nIn this study, historical data from the London International Airport station has been used, along with 11 different Atmosphere Ocean Global Climate Models (AOGCMs), which are used to predict future climate variables. These models produced a total of 27 different data sets of annual maximum precipitation over a period of 117 years, for storm durations of 1, 2, 6, 12 and 24 hours.\nThe current Environment Canada recommended distribution is the Gumbel (EV1) distribution, and the current United States distribution is the Log-Pearson type 3 (LP3). This report investigates a third distribution, the Generalized Extreme Value (GEV) distribution, in the context of the Upper Thames River Watershed.\nThe historical data set and the data sets derived from AOGCMs were used with the GEV, LP3 and EV1 distributions, and the goodness of fit tests were performed to select which was most appropriate distribution. L-Moment Ratio diagrams were also constructed to help establish the most suitable distribution. All results showed that GEV was very appropriate to the Upper Thames River Watershed data, and it was often the favored distribution.\nThis report shows the need for more studies to be carried out on the GEV distribution, to ensure we are using the most appropriate methods for predicting these extreme precipitation events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".