3.1.2. Flood Risk for Vehicles

The flow variables (flow depths and flow velocity) provided by the 1D/2D USM were also used to generate flood hazard maps for vehicles for each district of the city and their evolution in the case of a climate change scenario. The results concerning the current scenario show that areas classified with high flood hazard conditions have reduced risk compared to the case of pedestrians; in particular, high hazard is null for T = 1 and is less than 5% for T = 10, with this last one being the designed return period for the sewer system of the city, which progressively increases for higher return periods. This notwithstanding, the results show that climate change scenarios could produce an average increase

of 30% for the whole city with a peak of 50% for specific districts. Additionally, for the simulation corresponding to return period T1 and the BAU scenario, the high flood hazard area is null for each district [28].

In order to assess the vehicles' vulnerability, three levels were also proposed based on a unique indicator: The vehicular flow intensity (VFI) expressed in veh/day. Depending on this value and defined thresholds, the vulnerability of each urban road was classified into three levels (low, medium and high) [28]. The final vulnerability map is shown in Figure 16.

Furthermore, for vehicles, flood risk was assessed through the elaboration of flood risk maps for all the considered return periods and scenarios (baseline and BAU). Figure 17 shows the flood risk maps related to a rainfall storm event with a return period of 10 years for both scenarios.

**Figure 16.** Vulnerability map for vehicles. Green, orange and red colors indicate low vulnerability (vehicular flow intensity (VFI) < 100), medium (100 < VFI < 1000) and high (VFI > 1000), respectively.

**Figure 17.** Example of flood risk maps for vehicles for synthetic 10 year return period projected storms related to (**a**) baseline and (**b**) BAU scenarios.

In this case, the assessment has also been broken down into districts in order to observe the riskiest districts in terms of vehicles' stability. Moreover, in order to highlight the effect of climate change in terms of the increase of high-risk areas in Barcelona, we present the variation of high-flood risk areas for vehicles in all of the districts (Figure 18). In this case, it is also possible to observe a major increase of high flood risk areas (from 20% to 40% for the whole city area) with respect to the climate change coefficients (from 12% to 16%) for the same return periods.

**Figure 18.** Expected increase of high-risk areas according to the future conditions.

#### *3.2. Assessment of Economic Impacts Produced by Pluvial Floods*

For the estimation of tangible direct damages caused by pluvial floods generated by urban floods, both properties and vehicles were considered in the economic assessment. According to the claims data provided by the Spanish re-insurance company (CSS), these two risk categories are the most significant.

According to the developed methodology to estimate property damage, flow depths on the streets provided by the 1D/2D USM were properly reduced to achieve flood depths for properties using specific sealing coefficients, which were collected for 14 land uses in Barcelona [35,36]. As a second step, flood damages suffered by the properties were evaluated on the basis of tailored flood depth damage curves for all the 14 land uses; a detailed flood damage model was developed and validated in previous studies [28,30]. Models considered different typologies of properties: without basements, with a basement and with up to two basements. On the other hand, configurations with or without parking access were considered [28,30].

Regarding the evaluation of vehicle damage, a novel methodology—also based on the concept of damage curves—was implemented. The methodology tried to reduce the uncertainty due to the mobility of vehicles, proposing heterogeneous vehicular occupation for several areas of the city based on the information provided by aerial photographs [28,37]. For this assessment, flood damage curves developed by the Army Corps of the United States of America [44] for five types of vehicles were adapted for the case study of Barcelona [28,37]. These curves were converted into a single damage curve weighted according to the percentage of vehicle types in Barcelona, also taking into account their depreciation according to statistical information concerning vehicle types and their age [28,30,37].

For both properties' and vehicles' flood damage assessment, damage maps were achieved for the return periods T1, T10, T50, 100 and T500 and current (baseline) and future (BAU) scenarios and aggregated for each district of the city (Figures 19 and 20). These figures show how future rainfall conditions for a projected storm of 10 years significantly worsen the situation in several districts of the city. Specifically, it can be observed that all the districts of the downtown would suffer high losses, and the better situation of several districts upstream would be exacerbated due to climate change.

**Figure 19.** Example of economic flood damage maps for properties for synthetic projected storms of 10 years related to baseline (**a**) and BAU scenarios (**b**) indicating aggregated damages for districts.

Moreover, for both scenarios, the expected annual damage (EAD) [29] for the whole city including flood damages related to properties and vehicles [30] was calculated. The results indicate that, due to climate change, the EAD would grow from € 39.8 M to € 54.7 M [28].

Finally, the methodology for the estimation of indirect damages produced by pluvial floods based on an econometric method of input–output (IO) tables indicated a linear relationship between direct and tangible losses. Specifically, according to the obtained results, indirect tangible damages produced by pluvial floods in Barcelona could represent around 29% of direct damages. This increase could be taken into account in the previously reported EAD [28].

**Figure 20.** Example of economic flood damage maps for vehicles for synthetic projected storms of 10 years related to baseline (**a**) and BAU scenarios (**b**) indicating aggregated damages for districts.

### *3.3. Assessment of the E*ff*ects of Pluvial Floods on the Surface Tra*ffi*c Service*

The climate-related resilience of a city depends on its capacity to maintain the correct functioning of the main urban services during extreme weather events such as pluvial floods. The results of the impacts produced by this kind of floods on the surface traffic system were analyzed according to the methodology presented in Section 2.7. In this case, flood hazard was assessed through flood hazard maps elaborated on the basis of flood depths provided by the 1D/2D USM and the specific hazard criteria previously presented. Hazard maps were elaborated for the return periods T1, T10, T50, T100 and T500 and current (baseline) and future (BAU) scenarios. Examples of flood hazard maps are shown in Figure 21. Comparing the results for both scenarios, it can be observed that, for the total amount of 1492 km, the increase of the road links that could be affected by speed reduction ranged between 3% and 30% depending on the return period, while the increase in terms of closed road links could be around 20% for all the considered return periods (Figure 22).

Finally, through the TransCAD mesoscalar traffic model, the increase in transit time for all the synthetic storm events was assessed and monetized following the methodology proposed by the Multi-Color Handbook [45]. The monetization of the increase of traveling time for the whole city allowed the estimation of a specific EAD for baseline (1.82 M€) and BAU (2.0 M€) [28,38].

**Figure 21.** Example of flood hazard maps for surface traffic for synthetic projected storms of 10 years related to baseline (**a**) and BAU scenarios (**b**).

**Figure 22.** Representation of the effects produced by pluvial flood on the surface transport system in Barcelona for current (baseline) and future (BAU) scenarios in terms of km of roads with reduced speed (**a**) and km of closed roads (**b**).
