Urban micro-scale flood risk estimation with parsimonious hydraulic modelling and census data

Abstract

The adoption of 2007/60/EC Directive requires European countries to implement flood hazard and flood risk maps by the end of 2013. Flood risk is the product of flood hazard, vulnerability and exposure, all three to be estimated with comparable level of accuracy. The route to flood risk assessment is consequently much more than hydraulic modelling of inundation, that is hazard mapping. While hazard maps have already been implemented in many countries, quantitative damage and risk maps are still at a preliminary level. A parsimonious quasi-2-D hydraulic model is here adopted, having many advantages in terms of easy set-up. It is here evaluated as being accurate in flood depth estimation in urban areas with a high-resolution and up-to-date Digital Surface Model (DSM). The accuracy, estimated by comparison with marble-plate records of a historic flood in the city of Florence, is characterized in the downtown's most flooded area by a bias of a very few centimetres and a determination coefficient of 0.73. The average risk is found to be about 14 € m⁻² yr⁻¹, corresponding to about 8.3% of residents' income. The spatial distribution of estimated risk highlights a complex interaction between the flood pattern and the building characteristics. As a final example application, the estimated risk values have been used to compare different retrofitting measures. Proceeding through the risk estimation steps, a new micro-scale potential damage assessment method is proposed. This is based on the georeferenced census system as the optimal compromise between spatial detail and open availability of socio-economic data. The results of flood risk assessment at the census section scale resolve most of the risk spatial variability, and they can be easily aggregated to whatever upper scale is needed given that they are geographically defined as contiguous polygons. Damage is calculated through stage–damage curves, starting from census data on building type and function, for the main categories in the study area: structures, household contents and commercial contents. This method is tested in the area of the St. Croce district in Florence, one of the most seriously affected in the famous 1966 flood

Quantification of flood risk mitigation benefits: A building-scale damage assessment through the RASOR platform

“…historic district) was considered the most suitable scale of analysis for the availability of open socio-economic data and for the possibility of upscaling flood representative parameters (Arrighi et al, 2013). The present 145 study strongly increases the level of detail to the single-building level.…”

“…Flood consequences in case of tangible damages are expressed in terms of economic (Sterlacchini et al, 2016). Vulnerability in urban areas is often assessed 40 at micro-scale (Arrighi et al, 2013;Apel et al, 2009;Dottori et al, 2016;Prahl et al, 2016), e.g. at single-building level in order to capture the variability of built-up area in terms of building characteristics (e.g.…”

“…However, such a detail requires high-resolution geographic data and attributes. over, major uncertainties still remain in replacement/recovery cost assessment (Meyer et al, 2013) which on one hand may rely on insurance data (PenningRowsell and Pardoe, 2012;Rojas et al, 2013;Alfieri et al, 2016), on the other on socio-economic proxies (Arrighi et al, 2013;Marin and Modica, 2017).…”

“…2 ) are undertaken at catchment scale in order to produce design rainfall and associated statistical flood scenarios (Campo et al, 2006). River hydraulics is performed through a standard solver of the 1D general For further details on the hydraulic model see the method described in Arrighi et al (2013), which is adopted by the Arno River Catchment Authority for hazard mapping. …”

“…With an exposure analysis at building scale it is possible to state for those exposed buildings (whose flood depth is extracted by high resolution hazard maps), the number of floors, the surface area, the building use, the presence of cellar etc. This step is crucial to make then the association with damage Proxies of economic values can be used to estimate the replacement and recovery costs (Arrighi et al, 2013;Marin and Modica, 2017) for structures and contents. The sensitivity analysis carried out in a previous work (Arrighi et al, 2016a) has shown that exposure values, from which the recovery/replacement 225 costs were assessed, is the most sensitive parameter (elasticity=0.9).…”