Abstract:
Climate change is causing serious threats on natural and human systems worldwide. In particular, climate-related impacts will be especially relevant in coastal areas, where a dense interaction between terrestrial and marine systems occur. Located at the land-sea interface, coastal areas, are dynamic environments where natural and anthropogenic forcing interact at diverse temporal and spatial scales modifying their geomorphological, physical and biological characteristics. Against this complex interplay, coastal managers and policy makers are increasingly asking for new integrated approaches supporting a multi-scenario analysis of environmental risks arising from natural and anthropic stressors.
In the frame of this thesis, a GIS-based Bayesian Network (BN) approach was developed, exploiting functionalities offered by both methods to evaluate the probability (and related uncertainty) of coastal erosion risks, and connected water quality variation, against multiple ‘what-if’ scenarios, including different climate conditions (e.g. sea level rise). Resulting output of its application to the testing case of the shoreline of the municipality of Ugento (Apulia Region, Italy), represents valuable information to support robust decision-making and to provide the means for adaptive policy pathways in the context ICZM implementation and disaster risk reduction.
