In synergy with other socio-economic risks, the effects of climate change pose contemporary structural challenges that can not be considered only as an environmental issue. They affect the general development and therefore make the adaptive capacity of a population uncertain in the following decades. The subject of this dissertation comprises the development of a new decision support model for the selection of local level climate change adaptation measures. Considering the nature of management issues in climate policies, which involves decision-making under the conditions of uncertainty, the model employs adaptive management principles. It was designed to help decision-makers in selection of adequate adaptation measures, and to enable monitoring of the implementation process. The key objective of the research is fulfilled by developing a model for the selection of priority adaptation measures. The model is based on scenarios of the synergistic influence of diverse sets of measures on the observed system vulnerability. It takes into account climate projections and relevant biophysical and anthropogenic factors. The model relies on a combination of several methodological approaches. The scenario method was used for the selection of adaptation measures. It is based on the assessment of the simultaneous contribution of a group of measures to the reduction of vulnerability of the observed climate impact, by forming a conditional probability diagram using Bayesian networks. Through the analysis of the likelihood of diverse states of the observed group of criteria, it is possible to examine the effect of individual measures (or sets of measures) adaptation capacity, as a result of the joint probability distribution of all criteria in the network. The analytical hierarchical process (AHP) was used to quantify the distinct qualitative relationships between the risk criteria of the observed climate impact and the adaptation measures. A GIS is used to calculate the specific values of the criteria on the network, to profile the vulnerability, sensitivity, adaptation capacity and exposure index, as well as for data integration. The model can improve the decision-making in adaptation planning process. As the results are expressed as a probability distribution for each alternative, the model can help decision makers predict the chances of achieving desired effects of selected measures, and develop detailed programs at the local level to increase their efficiency. The model is also capable to transparently monitor the application process and facilitate the development of appropriate capacities for the purpose in local communities. In this respect, the developed model also provides a methodological contribution for improving the planning framework for the local adaptation project management.