A New Approach for Seasonality Characterization of Extreme Rainfall
Nirajan Dhakal (1) and Shaleen Jain (1, 2)
(1) Sustainability Solutions Initiative, University of Maine, Orono, Maine 04469 USA email@example.com (2) Department of Civil and Environmental Engineering, University of Maine, Orono, Maine 04469 USA
Climate variability and change has the potential to cause significant impacts on our economic, ecological, social, and cultural resources. In a changing climate, civil infrastructures (such as dams, bridges, and culverts) are increasingly compromised during extreme precipitation events. Infrastructure maintenance and potential impacts are often sensitive to the timing of extreme events (rain, rain-on-snow, ice events). The conventional approach of assessing the seasonality is based on the estimation of the mean date and variability of the occurrence of the extreme events. Such approach doesn't capture the uncertainty of the results due to randomness of the data. A more robust way of characterizing the seasonality is to use the non-parametric kernel circular distribution. Our study focuses on analyzing the seasonality of the extreme precipitation events for 12 USHCN stations across the state of Maine. The seasonality was evaluated based on both the conventional approach (using the mean date and variability of the occurrence of the extreme events) as well as the robust approach (using the circular distribution). A majority of stations show shifts in the timing of the extreme events. Historically, empirical distributions of extreme events were largely concentrated in the July—January period; recent events show increased proclivity towards late spring and early summer. Our ongoing work with coastal communities in Maine towards development of relevant solutions to confront climate-related vulnerabilities has shown that the information regarding the seasonality of extreme precipitation events factors into routine inspections, maintenance as well as funding for repair and replacement of infrastructures like culverts, sewers etc.