Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Master of Engineering Science

Program

Civil and Environmental Engineering

Supervisor

Najafi, Reza, M.

Abstract

Changes in the regional characteristics of temperature and precipitation can intensify the occurrence and severity of extreme events such as rain-on-snow induced flooding, droughts and wildfires. Analyzing these climate variables in isolation without considering their interdependencies might result in severe underestimation of their combined effects. In this study, copula functions are used to describe the joint behaviour of temperature and precipitation across Canada over a historical period of 1910-present using observations and further till the end of the 21st century using three large ensembles of regional climate models. Moreover, given the lack of observation data over Canada, gridded datasets are also evaluated under both univariate and bivariate settings. The importance of preserving the dependence structure is shown through a hydrologic model forced with multivariate bias-corrected data. Climate projections are evaluated against observations using a hierarchical Bayesian framework followed by calculation of extreme climate indices over four future warming scenarios corresponding to +1.5, +2.0, +3.0 and +4.0°C mean temperature rise above the pre-industrial period. Finally, an ensemble pooling approach is applied to calculate non-stationary return periods of compound extreme events.

Results show clear signs of accelerated warming and wetting over northern Canada and strong evidence of hot and dry conditions in Prairie Provinces while non-stationary analyses reveal shifts towards warm and wet climate conditions for the rest of southern Canada. Results from the comparison of multiple gridded datasets show that while they represent temperature and precipitation well, their performance in simulating the joint behaviour is relatively weak. Hydrological modelling results indicate that the multivariate bias correction of the input datasets can improve streamflow simulations particularly extreme events compared to the univariate approach. Climate projections show an increase in warm spells in the future accompanied by an increase in extreme precipitation as well for most regions in Canada. The importance of considering the dependence of temperature and precipitation extremes in calculating joint return periods reveals potential future changes in the frequency of compound extremes. Overall, this study provides a comprehensive characterization of the joint behaviour of temperature and precipitation over Canada under a changing climate.

Summary for Lay Audience

Temperature and precipitation are two important climate variables that directly affect societies. Whereas temperature extremes in the form of very hot summers or very cold winters can cause socioeconomic disruptions, precipitation extremes cause floods, snowstorms and droughts. These events are not independent from each other and are almost always jointly caused by both temperature and precipitation exceeding some thresholds. Floods caused by rain falling over frozen ground, heatwaves adding to the intensity of droughts and wildfire risk increasing due to extended hot-dry periods are some examples of compound events caused by temperature and precipitation jointly. To study this behaviour comprehensively for historical and future periods over Canada, state-of-the-art statistical methods are applied, which are able to capture the complete dependence structure of both variables. The analysis of multiple gridded historical data shows that they are not very reliable in capturing this dependency. The potential impact of this drawback is revealed by running a hydrologic model, which produces unreliable outputs in comparison to the same model if run with data from which biases have been removed. Analysis of future temperature and precipitation dependence under climate change is conducted using three large ensembles of regional climate model simulations that project these two variables until the end of the 21st century. The accuracy of projections is validated against observations revealing that methods which correct the dependence structure of simulated temperature and precipitation should be preferred over methods that adjust these variables in isolation. Next, we determine how frequent extreme compound events would occur when the dependence between the variables is considered and how this frequency can change in the future. The study provides comprehensive information on the relationship between temperature and precipitation and its potential impacts in the future under a changing climate.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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