Browsing by Author "Farjad, Babak"
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- ItemOpen AccessA Modeling Framework to Investigate the Impact of Climate and Land-Use/Cover Change on Hydrological Processes in the Elbow River Watershed in Southern Alberta(2015-09-28) Farjad, Babak; Marceau, DanielleComplex dynamical and physical interactions exist between climate, land use/cover (LULC), and hydrology. In fact, each of these systems is considered complex because they possess the following characteristics. They consist of a large number of components that interact in a non-linear way. They interchange information with their surroundings and constantly modify their self-organized structure. They are far-from-equilibrium and display instability, sensitivity to initial conditions, sudden changes, and a behavior that cannot be captured by simple models. Understanding how hydrological processes respond to climate and LULC change requires knowledge about how these complex systems interact in the present and how they might in the future. The objective of this research is to understand the responses of hydrological processes to climate and LULC change in the Elbow River watershed using an integrated modeling framework that can address the complexity of these interrelated systems. To achieve this goal, the physically-based, distributed MIKE SHE/MIKE 11 model was coupled with a LULC cellular automata to simulate hydrological processes up to the year 2070 under five GCM-scenarios (NCARPCM-A1B, CGCM2-B2(3), HadCM3-A2(a), CCSRNIES-A1FI, and HadCM3-B2(b)). Results reveal that most scenarios generate an increase in overland flow, baseflow, and evapotranspiration in the winter/spring, and a decrease in the summer/fall. The highest increase in streamflow occurs in mid-late spring due to an increase in snowmelt and rain-on-snow events that may enhance the risk of flooding. In addition, LULC change substantially modifies the river regime in the east sub-catchment, where urbanization occurs. The separated impacts of climate and LULC change on streamflow are positively correlated in winter and spring, which intensifies their influence and leads to a rise in streamflow, which in turn increases the vulnerability of the watershed to floods, particularly in spring. Flow duration curves indicate that LULC change has a greater contribution to peak flows than climate change in both the 2020s and 2050s. The integrated modeling framework used in this research is a powerful analytical tool that can help scientists and decision makers for the planning of sustainable water resources and infrastructure management.
- ItemOpen AccessIntegrated Environmental Modelling Framework for Cumulative Effects Assessment(University of Calgary Press, 2021-01) Gupta, Anil; Farjad, Babak; Wang, George; Eum, Hyung; Dubé, MoniqueA thorough and detailed examination of integrated environmental modelling and integrated environmental modelling frameworks for cumulative effects assessment of complex environmental problems. Global warming and population growth have resulted in an increase in the intensity of natural and anthropogenic stressors. Investigating the complex nature of environmental problems requires the integration of different environmental processes across major components of the environment, including water, climate, ecology, air, and land. Cumulative effects assessment (CEA) not only includes analyzing and modeling environmental changes, but also supports planning alternatives that promote environmental monitoring and management. Disjointed and narrowly focused environmental management approaches have proved dissatisfactory. The adoption of integrated modelling approaches has sparked interests in the development of frameworks which may be used to investigate the processes of individual environmental component and the ways they interact with each other. Integrated modelling systems and frameworks are often the only way to take into account the important environmental processes and interactions, relevant spatial and temporal scales, and feedback mechanisms of complex systems for CEA. This book examines the ways in which interactions and relationships between environmental components are understood, paying special attention to climate, land, water quantity and quality, and both anthropogenic and natural stressors. It reviews modelling approaches for each component and reviews existing integrated modelling systems for CEA. Finally, it proposes an integrated modelling framework and provides perspectives on future research avenues for cumulative effects assessment.
- ItemOpen AccessMultiscale Spatial and Temporal Modelling of Fine Particulate Matter (PM2.5) from Wildfire Smoke Using Remote Sensing and Statistical Methods(2020-04-22) Mirzaei, Mojgan; Bertazzon, Stefania; Yackel, John J.; Farjad, BabakWildfire smoke exposure is increasingly recognized as a critical public health problem due to the increase in the frequency and severity of wildfires in recent decades. Wildfire smoke-generated PM2.5 is considered the most concerning particulate, as it can be inhaled deep into the lungs, penetrate the human respiratory system, and enter the bloodstream. Studies assessing wildfire PM2.5 exposure and population health have traditionally employed three main approaches: (1) in situ measurements (2) satellite information of atmospheric aerosol, and (3) atmospheric models. The objective of the present study was to identify models to accurately predict PM2.5 concentration over space and time. Spatiotemporal models were built to perform a comprehensive analysis of wildfire PM2.5 concentrations, for each recent year over the study region: Land Use Regression (LUR), Linear Mixed Effect (LME), and Artificial Neural Network (ANN). Predictor variables were MODIS AOD images, ground PM2.5 measurements, and ancillary land use and meteorological data. LUR models were used to predict PM2.5 in three distinct periods: before, during, and after a wildfire. Results showed a major difference in predictors between the during-fire and the other models, due to the different contribution of traffic and industrial emissions. Daily estimation of PM2.5 concentration was derived by incorporating nested period-zone-specific random effects of the AOD-PM2.5 relationship over the province of Alberta, Canada using LME models. The LME model’s predictions also improved when additional variables were integrated with AOD measures in a multivariate framework. ANN models were used as a multivariate and non-parametric approach to empirically predict wildfire smoke using AOD along with other predictors. Daily PM2.5 concentrations were predicted using temporal and spatial ANN for the 2014 to 2017 fire seasons and each airshed zone in Alberta. The study demonstrated how MODIS AOD could be incorporated within statistical models to provide reliable predictions of daily PM2.5 concentrations over wildfire events, to feed health and epidemiological studies. The results demonstrated that mixed effect models outperformed the LUR models owing to their ability to adjust the varying relationship of AOD-PM2.5. ANN models also outperformed them owing to their advantage in modelling non-parametric and non-linear behaviours.