Building-scale flood loss estimation through vulnerability pattern characterization: application to an urban flood in Milan, Italy

Abstract

Abstract. The vulnerability of flood-prone areas is determined by the susceptibility of the exposed assets to the hazard. It is a crucial component in risk assessment studies, both for climate change adaptation and disaster risk reduction. In this study, we analyse patterns of vulnerability for the residential sector in a frequently hit urban area of Milan, Italy. The conceptual foundation for a quantitative assessment of the structural dimensions of vulnerability is based on the modified source–pathway–receptor–consequence model. This conceptual model is used to improve the parameterization of the flood risk analysis, describing (i) hazard scenario definitions performed by hydraulic modelling based on past event data (source estimation) and morphological features and land-use evaluation (pathway estimation) and (ii) the exposure and vulnerability assessment which consists of recognizing elements potentially at risk (receptor estimation) and event losses (consequence estimation). We characterized flood hazard intensity on the basis of variability in water depth during a recent event and spatial exposure also as a function of a building's surroundings and buildings' intrinsic characteristics as a determinant vulnerability indicator of the elements at risk. In this sense the use of a geographic scale sufficient to depict spatial differences in vulnerability allowed us to identify structural vulnerability patterns to inform depth–damage curves and calculate potential losses from mesoscale (land-use level) to microscale (building level). Results produces accurate estimates of the flood characteristics, with mean error in flood depth estimation in the range 0.2–0.3 m and provide a basis to obtain site-specific damage curves and damage mapping. Findings show that the nature of flood pathways varies spatially, is influenced by landscape characteristics and alters vulnerability spatial distribution and hazard propagation. At the mesoscale, the “continuous urban fabric” Urban Atlas 2018 land-use class with the occurrence of at least 80 % of soil sealing shows higher absolute damage values. At microscale, evidence demonstrated that even events with moderate magnitude in terms of flood depth in a complex urbanized area may cause more damage than one would expect.

The value of ultra-detailed survey data for an improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0

“…Obtained results also indicate that, in addition to the standard hazard variables, an accurate description of the building features is essential to derive reliable estimations of flood damage (Schröter et al, 2018;Molinari et al, 2020;Taramelli et al, 2022). While data retrieval on large-scale for some of the vulnerability variables can be costly (Ruggieri et al, 2021), the use of the proposed probabilistic missing data filling procedure, based on representative datasets of the local building stock, can be employed as an option.…”

Integrated Flood Impact and Vulnerability Assessment Using a Multi-Sensor Earth Observation Mission with the Perspective of an Operational Service in Lombardy, Italy

“…To determine flood depth, a Floodwater Depth Estimation Tool (FwDET-GEE), produced by the University of Alabama [63,64] and implemented in Google Earth Engine [27,65] was applied. This tool uses DTM as input data.…”

“…Heuristic models are commonly applied using simple and equal weights [25]. These approaches regularly refer to a weight assignment for different indicators to determine a final level of vulnerability [26,27]; however, the weighting process could be applied using different perspectives. For instance, there are examples of the development of vulnerability indexes [23,28].…”

Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines

“…Impact models based on FDCs were preferred due to their straightforward implementation and state-of-the-art approach. More 205 complex models (e.g., Dottori et al, 2016;Nofal et al, 2020;Taramelli et al, 2022) were not used due to the lack of required detailed input data at the large scale. Simpler models (e.g., Manselli et al, 2022) do not have the level of details required for this study.…”