EGU2020-11943
https://doi.org/10.5194/egusphere-egu2020-11943
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Global Assessment of Non-Stationarity in Extreme Precipitation

Razi Sheikholeslami1, Simon Michael Papalexiou2, and Martyn Clark3
Razi Sheikholeslami et al.
  • 1Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada (razi.sheikholeslami@usask.ca)
  • 2Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatoon, Canada (sm.papalexiou@usask.ca)
  • 3Department of Geography & Planning, University of Saskatchewan, Saskatoon, Canada (martyn.clark@usask.ca)

Rapid urban development, along with human modifications in river discharge (both frequency and magnitude) increase the need to design safe and resilient infrastructure. In addition, continental-domain studies show that there are significant changes in the intensity and frequency of the extreme rainfall events. Importantly, Earth System Models predict that these changes will continue to grow in the future. Consequently, flood frequency from heavy precipitation events is expected to increase, thereby threatening human society and the environment. Therefore, the stationary climate assumption — the idea that the future variability of the system will remain within the limits observed in the past record — may not be valid and should be carefully examined. Despite the existing awareness of potential non-stationarity, there has been a limited research on analysis of non-stationary of extreme precipitation at the global scale. This motivated us to conduct a comprehensive global study to compare the performance of non-stationary and stationary models in describing precipitation extremes.

How to cite: Sheikholeslami, R., Papalexiou, S. M., and Clark, M.: A Global Assessment of Non-Stationarity in Extreme Precipitation , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11943, https://doi.org/10.5194/egusphere-egu2020-11943, 2020