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Articles | Volume XLII-3/W4
https://doi.org/10.5194/isprs-archives-XLII-3-W4-221-2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-221-2018
06 Mar 2018
 | 06 Mar 2018

ADAPTATION TO FLOODING EVENTS THROUGH VULNERABILITY MAPPING IN HISTORIC URBAN AREAS

A. Gandini, I. Prieto, L. Garmendia, J.-T. San-José, and A. Egusquiza

Keywords: Cultural heritage, flooding, climate change, urban data model, vulnerability assessment

Abstract. Historic urban areas are complex and inter-reliant systems, vulnerable to natural hazards. Over the recent years, the increase frequency in extreme precipitation events and sea-level rise, have impacted on a large number of historic areas, growing concern over disaster mitigation related to climate change.
Most of the changes in the climatological indicators may have adverse impacts on historic areas, leading to physical, social and cultural consequences and should be included in urban planning practice. The importance of addressing cultural heritage in disaster risk has also been included in The Sendai Framework, considering the dimensions of vulnerability, adaptive capacity and exposure through systematic evaluation.
Urban planning decisions involve an understanding of complex interactions between different aspects of the city, in its constructive, social, economic, environmental and cultural system. The analysis of these interactions requires a systemic approach as the components operate on different spatial and temporal scales and generate a large amount of data. This information can be used to determine the vulnerability of historic areas by assessing it at the building level, through the creation of typologies representing the building stock, often characterized by similarities and common constructive elements.
The comprehension of the information can be supported and homogenized by a multi-scale urban model, to facilitate the understanding of interactions and the link among the different disciplines involved. This paper describes the methodology proposed for vulnerability mapping in historic urban areas, by using a categorization method supported by an information strategy and a multi-scale urban model.