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WaterBodiesTowardsCrisisManagement

“Water Bodies towards crisis management” is a project of IAAC, Institute for Advanced Architecture of Catalonia developed in the Master in City & Technology 01 - 2022-2023 by the student(s) Christos Grapas during the course MaCT01 22/23 Computational Urban Design I with Eugenio Bettucchi and Iacopo Neri.

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Water Bodies towards Crisis Management is a computational model predicting areas with high chances of dealing with floods based on open source data.Almost every day, we are confronted with dramatic images of communities and environments in crisis . Flash floods have recently become one of Greece’s most common natural occurrences after wildfires. Flooding occurs due to rapid rainfall and heavy storms, from rising river levels or melting snow. Deforestation, poor soil quality and rapid urbanization significantly contribute to the genesis of floods. Deforestation, also related to soil erosion, is a major issue in Greece. Nowadays, only 18% of the territory of Greece is covered by forests, whereas at the beginning of the 19th century it was more than 40%. This study uses computational design tools through open databases to locate potential conflicts related to flood phenomena within the agricultural and urbanized areas of Boeotia Region, Greece. The plain of Boeotia is one of the most productive agricultural regions of Greece.

The scope of this project is to identify highly urbanized and agricultural areas with potentialflooding phenomena in order to provide a general basis for urban and rural planning, aiming at reinforcing local communities’ resilience towards crisis management.

Citation: ●Jarvis, A., H.I. Reuter, A. Nelson, E. Guevara. 2008. Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database: https://srtm.csi.cgiar.org. ● Lehner, B., Verdin, K., Jarvis, A. (2008): New global hydrography derived from spaceborne elevation data. Eos, Transactions, AGU, 89(10): 93-94. ● Hengl, T., Miller, M.A.E., Križan, J., et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). doi:10.1038/s41598-021-85639-y