Doctor of Philosophy / Department of Environmental Design and Planning Program / Lee R. Skabelund / Despite the growing number of urban adaptation planning initiatives to climate change hazards, there exist significant barriers related to implementation uncertainties that hinder translation of adaptation plans into actions, resulting in a widely recognized ‘planning-implementation gap’ across scales and regions. Bridging the planning-implementation gap will require overcoming implementation uncertainties by better understanding the relationships between the primary factors driving adaptation planning initiatives and emerging adaptation options across spatial scales. The modified Driver-Pressure-State-Impact-Response model published by Rounsevell, Dawson, and Harrison in 2010 provided a robust framework for identifying the primary factors driving adaptation planning initiatives and the emerging adaptation options related to risk of changing climate and flooding events in the urban context. Drawing on evidence from the systematic review of 121 adaptation planning case studies across North America, this research derived qualitative and quantitative data, which was subsequently analyzed using binary logistic regression to generate objective and generalizable findings. The findings of binary logistic regression models suggest that the choice of specific adaptation options (namely enhancing adaptive capacity; management and conservation; and improving urban infrastructure, planning, and development) may be predicted based on the assessment of primary factors driving adaptation planning initiatives (namely, anticipation of economic benefits; perceived threats to management and conservation of urban natural resources; support of human and social systems; and improvement of policy and regulations) in relation to the risk of changing climate and urban flooding events. This does not imply that other primary factors (namely information and knowledge; perceived funding and economic opportunities; evidence of climate change effects; and general concerns) have no or insignificant relationships with the selection of adaptation options, only that the review did not find evidence to support such claims. These study findings may offer useful guidance to the design and further development of planning and decision support tools that could be used for assessment of adaptation plans and selection of robust adaptation options that take account of uncertainties surrounding implementation of effective climate adaptation actions. Study findings can also inform evidence-based policy and investment decision making, especially in regions where urban adaptation plans are weak or absent.