SUMMARY

In Mountain environments, natural hazards can recurrently affect infrastructures and human settlement. Besides, mountain environments are changing faster than ever due to concomitant changes in climate (CC), land use and society so that adapting and mitigating future impacts is urgently required. The REadapt proposes a paradigm shift toward Ecosystem-based disaster risk reduction (Eco-DRR) strategies. However, little is still known about the long-term performance of such Eco-DRR strategies under future CC scenarios. Focusing on the Spanish Pyrenees, REadapt aims at analyse past and future trajectories of the reliability of Eco-DRR implemented at the beginning of the 20th century to protect the international railway station of Canfranc. To this end, the REadapt project innovatively relays on the use stochastic life cycle cost assessment (LCCA), where the progressive deterioration of each measurements, the impact of extreme events under different scenarios, and the influence of maintenance and repair strategies will be combined to assess the performance of the Eco- DRR system during its life cycle. Moreover, to account for climate variability in our assessment, a long-term perspective analyses of natural hazard will be included based on historical and palaeo-records. The project is structured in 4 well established and interconnected workpackages (WP). WP1 aims creating a digital GIS database with relevant information from past technical reports and project. WP2 aims at reconstructing past extreme events and climate impacts. WP3 aims at understanding the progressive deterioration of each implemented measurements in the Eco-DRR scheme and WP4 aims at analyses their long-term performance using a LCCA. The Readapt project expects that the implemented approach allows a more strategic and sustainable allocation of economic resources under future CC scenarios, by optimizing the maintenance and repair of existing measurements to support decision-making process, which it is in line with the Transformation and Resilience Mechanism.