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Climatic Change

, Volume 130, Issue 2, pp 115–129 | Cite as

Potential impact of climate change on intensity duration frequency curves of central Alberta

  • Chun-Chao Kuo
  • Thian Yew Gan
  • Mesgana Gizaw
Article

Abstract

Under the effect of climate change, warming likely means that there will be more water vapour in the atmosphere and extreme storms are expected to occur more frequently and with greater severity, resulting in municipal Intensity-Duration-Frequency (IDF) curves with higher intensities and shorter return periods. A regional climate model, MM5 (the Pennsylvania State University / National Center for Atmospheric Research numerical model), was set up in a one-way, three-domain nested framework to simulate future summer (May to August) precipitation of central Alberta. MM5 is forced with climate data of four Global Climate Models, CGCM3, ECHAM5, CCSM3, and MIROC3.2, for the baseline 1971–2000 and 2011–2100 based on the Special Report on Emissions Scenarios A2, A1B, and B1 of Intergovernmental Panel on Climate Change. Due to the bias of MM5’s simulations, a quantile-quantile bias correction method and a regional frequency analysis is applied to derive projected grid-based IDF curves for central Alberta. In addition, future trends of air temperature and precipitable water, which affect storm pattern and intensity, are investigated. Future IDF curves show a wide range of increased intensities especially for storms of short durations (≤1-h). Conversely, future IDF curves are expected to shift upward because of increased air temperature and precipitable water which are projected to be about 2.9 °C and 29 % in average by 2071–2100, respectively. Our results imply that the impact of climate change could increase the future risk of flooding in central Alberta.

Keywords

Return Period Regional Climate Model Couple Model Intercomparison Project Phase Precipitable Water Extreme Storm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We are grateful to Compute Canada’s WestGrid support staff for their assistance with technical issues of its supercomputers. This research was supported by the City of Edmonton and Natural Sciences and Engineering Research Council.

Supplementary material

10584_2015_1347_MOESM1_ESM.docx (343 kb)
ESM 1 (DOCX 342 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Civil & Environmental EngineeringUniversity of AlbertaEdmontonCanada

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