Development of rainfall intensity duration frequency curves for the City of London under the changing climate
Bibliographic record
Abstract
The main focus of this study is the analysis of short duration high intensity rainfall for London, Ontario under the conditions of the changed climate. Predicted future climate change impacts for Southwestern Ontario include higher temperatures and increases in precipitation, leading to an intensification of the hydrologic cycle. One of the expected consequences of change is an increase in the magnitude and frequency of extreme events (e.g. high intensity rainfall, flash flooding, severe droughts, etc.). Changes in extreme events are of particular importance to the design, operation and maintenance of municipal water management infrastructure.\nMunicipal water management infrastructure (sewers, storm water management ponds or detention basins, street curbs and gutters, catchbasins, swales, etc) designs are typically based on the use of local rainfall Intensity Duration Frequency (IDF) curves. IDF curves are developed using historical rainfall time series data. Annual extreme rainfall is fitted to a theoretical probability distribution from which rainfall intensities, corresponding to particular durations, are obtained. In the use of this procedure an assumption is made that historic extremes can be used to characterize extremes of the future (i.e., the historic record is assumed to be stationary). This assumption is not valid under changing climatic conditions that may bring shifts in the magnitude and frequency of extreme rainfall. Such shifts in extreme rainfall at the local level demand new regulations for water infrastructure management as well as changes in design practices. The objective of this report is to assess the change in IDF curves for use by the City of London under changing climatic conditions.\nThe methodology implemented to assess changes in rainfall magnitude resulting from climate change includes the following components: (a) Development and use of a daily weather generator model for synthetic generation of rainfall under current and future climates; (b) Disaggregation of daily rainfall into hourly; (c) Statistical analysis of rainfall of various durations, and development of IDF curves under changed climatic conditions; (d) Comparative analysis of IDF curves; and (e) Recommendation for possible modification of municipal infrastructure design standards.\nThe two IDF curves currently used by the City of London (i.e., MacLauren IDF curve for design of conveyance systems, and Atmospheric Environment Service IDF curve for storm water management facilities) could have not be reproduced in this research using the data currently available from Meteorological Service of Canada. The IDF curves in use by the City are based on data sets that are no longer available. In addition, methods used by either MacLauren or Meteorological Service of Canada to estimate rainfall quantiles for durations shorter than one hour are not available. Therefore, comparing the IDF curves generated in this research to those currently used by the City of London is not appropriate. More confidence is placed in the relative difference between the three scenarios generated in this research: simulated historic climate (no change), and wet and dry climates (change guided by outputs of global circulation model outputs).\nThe results of simulations in this research indicate that rainfall magnitude (as well as intensity) will be different than historically observed. The climate change scenario recommended for use in the evaluation of storm water management design standards (i.e., the wet scenario) reveals a significant increase in rainfall magnitude (and intensity) for a range of durations and return periods. This increase has major implications on the ways in which current (and future) municipal water management infrastructure is designed, operated, and maintained. The main recommendation from this work is that the design standards and guidelines currently employed by the City of London be reviewed and/or revised in light of the information presented in this report.
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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 | 0.003 | 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 | 0.000 |
| 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".