Mémoire

Abstract

Extreme precipitation events can have numerous consequences in the province of Québec, Canada; the Montréal deluge of 1987 or the 1996 Saguenay floods are two examples among many. These precipitation events can cause significant damage and lead to flash floods, sewer overflows, and landslides (Van de Vyver et al., 2021). In the context of climate change, it is anticipated that extreme rainfalls will become more frequent and intense in Québec (Zhang et al., 2019). This is also the case in many regions of the world, a direct consequence of the Clausius-Clapeyron relationship, which states that warmer air can hold more water vapor (Westra et al., 2014). While the need for a better understanding of extreme precipitation events is very present in the context of adaptation measures, the link between heavy rainfall and floods or flash floods is not always easy to assess. How to identify these events in past climate series, and what are the expected changes in the frequency and intensity of extreme rains in Québec? How to inform the public and decision-makers about the projected changes in extreme precipitation events to promote their adaptation and resilience? The present research project aims to address these questions by discussing the future evolution of the percentiles to different indicators of extreme precipitation, both on reanalysis data and for future scenarios. The objective is to bridge the gap between past events identified on a time series and future virtual events based on different simulations. The first step will therefore be to choose the best dataset among those available for Québec. The second step will be to define extreme precipitation indicators. In this analysis, the maximum quantities at 3 hours, 6 hours, 12 hours, and 24 hours on an annual frequency will be retained – thus one data per indicator, per grid point, per year. The results will then be compared to past extreme events through the resulting time series. Can extreme precipitation events be identified by the peaks of these time series? Which indicator(s) best identify extreme events? Once the extreme indicators allowing for the identification of floods are defined, it will be possible to evaluate their evolution in the future until the year 2100 using the percentiles method. Will they become more frequent or intense in the future, could changes be observed in the return periods of extreme events? Comparing the projected changes for various horizons and scenarios and applying different representation techniques will help to make the conclusions of the study accessible to the public. Based on the literature and the analysis of different climate information websites, a reflection will also be initiated on how to best integrate the conclusions of the study into the Ouranos Climate Portraits site, as well as different suggestions to improve the platform. Consideration will also be given to supporting users’ comprehension regarding projected changes in extreme precipitation events.