Supporting robust and climate-sensitive adaptation strategies for infrastructure networks: A multi-hazard case study on Mozambique's healthcare sector
Citation:
Mühlhofer, E., Stalhandske, Z., Sarcinella, M., Schlumberger, J., Bresch, D. N., & Koks, E. E. (2023). Supporting robust and climate-sensitive adaptation strategies for infrastructure networks: A multi-hazard case study on Mozambique's healthcare sector. 14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14), Dublin, Ireland. https://doi.org/10.25546/103336Download Item:

Abstract:
As climate change causes more intense and frequent natural hazard events, decision makers are tasked to climate-proof vital infrastructure systems against these challenges. Adaptation studies often evaluate benefits of different options in face of single types of natural hazards, and on their damage aversion potential to individual infrastructure components. In a proof of concept, we use the healthcare sector in Mozambique, which is highly affected by tropical cyclone winds and concurrent flooding, to showcase how packages of adaptation measures may be evaluated in their effectiveness on a systemic level, to mitigate basic service disruptions from multiple hazards, across various interdependent infrastructure networks. Using the open-source risk modeling platform CLIMADA on 2019�s tropical cyclone Idai, we simulate five stylized adaptation strategies and their effects in reducing direct damages from wind and flooding to roads, power lines and healthcare facilities, their overall aversion of people�s healthcare access losses, and synergies or trade-offs with other basic service supplies. Results illustrate the importance of considering multi-hazard phenomena and interdependencies between infrastructure systems in adaptation appraisals. We further provide an outlook on how to integrate probabilistic and climate-scenario driven hazard modeling into robust adaptation planning.
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Author: ICASP14
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14th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP14)Type of material:
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https://doi.org/10.25546/103336Licences: