According to worldwide evidence of the last decades, the frequency and severity of extreme flooding events in urban areas are increasing [1
]. The characteristics of an urban environment, such as the high portion of impervious area and increased population density, raise the vulnerability to flooding [4
]. Traditional engineering measures face great challenges in providing sufficient flood protection when facing a more severe and frequent flooding condition [6
]. In response, current flood protection strategies move away from measures to increase flood proofing towards flood resilience [8
]. Various approaches to improve resilience to urban flooding have been proposed recently across different continents, such as the Best Management Practices (BMPs), Low Impact Development (LID), or Sustainable Urban Drainage Systems (SUDS). Furthermore, policies for improving public awareness of flood risk, advocating flood insurance, automated warning systems, etc. have also been advocated [9
]. These approaches aim to mitigate the flooding impacts in cities by maintaining a high level of system performance during flooding events and facilitating the recovery stage of the system after flooding, i.e., its resilience. This study aims to develop a novel methodology to assess the flood resilience level of households within an urban area with time during and after a flooding event by incorporating physical, social, and economic factors.
There are numerous studies evaluating the benefits of flood resilience-enhancing strategies. However, many of them focus on flood impact reduction instead of resilience. Indeed, various flood impact assessment techniques have been formulated according to a wide diversity of research purposes, availability of data, and accessibility of resources [10
]. On the contrary, the assessment of flood resilience faces many challenges, including its definition, dimensions used (e.g., social, economic, or physical aspects), and methods of quantification [11
]. Nevertheless, there is a growing number of research projects and studies aiming at quantifying flood resilience using integrated [13
] or multi-criteria [14
] approaches, assessing climate variability [15
] or the impact of infrastructure [16
] while considering socioeconomic aspects [18
]. Governance strategies for improving flood resilience have also been studied [19
The study of resilience was originated in the field of ecology [20
], where Holling defined it as the measure of the ability of an ecosystem to absorb changes and persist [21
]. Since then, variations of the resilience concept started to emerge in different research fields. In the context of flood risk and flood management, various definitions have been introduced recently [22
]. According to the literature, the definitions of flood resilience differ from each other. However, they generally comprise two major elements: 1. The coping capacity in the face of flooding, and 2. the recovery capacity after flooding. In this paper, these two major elements are adopted. Flood resilience is thus defined as “the capacity to withstand adverse effects following flooding events and the ability to quickly recover to the original system performance before the event”.
Resilience assessment can be used to evaluate flood risk management strategies at a city scale [28
]. However, there still exists no consensus on how to measure flood resilience [31
]. One commonly applied approach to quantify resilience is to utilize indicators that measure the characteristics of a system facing urban flooding. De Bruijn defined a set of indicators for flood resilience quantification, which covers three aspects: The amplitude of reaction, the graduality of the increase of the reaction with increasingly severe flood waves, and the recovery rate [32
]. These three aspects describe the state of system performance when facing flooding events. In addition, the value of indicators reflects the physical, social, and economic factors regarding flood risk management. Batica and Gourbesville developed an urban flood vulnerability and resilience assessment tool with indicators providing a comprehensive overview of vulnerability and resilience of a city and its community [33
]. An index is proposed to describe resilience level by assigning grades (0 to 5) to different indicators according to the availability levels to various urban services when facing a 100-year flooding event. Mugume et al. quantified the resilience of urban drainage systems in the UK by applying the utility performance function combined with the depth–damage data for residential properties that relates the overall performance of a drainage system to flood depths [25
]. Analogously, Lee and Kim proposed a resilience index for urban drainage systems in Korea based on flooding damage that resulted from damage functions calculated by multi-dimensional flood damage analysis [34
]. However, both studies on urban drainage systems lack socioeconomic aspects when estimating resilience. Bertilsson and Wiklund developed a spatialized index to measure and visualize flood resilience changes in an urban area of Rio [35
], incorporating five dimensions: Flood level, exposed population, susceptibility, material recovery, and flood duration.
Despite the already existing studies on flood resilience quantification, there is a lack of methods for assessing how a system’s resilience level is affected during and after flooding. As discussed, most existing studies are not time-dependent. Therefore, the aim of this study is to propose a time-dependent method for quantifying flood resilience of households in urban areas.
introduces the study area of Maxvorstadt in Munich city. In Section 3
, the structure of the Flood Resilience Index (FRI) and the computation of each parameter are explained in detail. In Section 4
and Section 5
, the inundation and FRI modelling results for Maxvorstadt as well as the sensitivity analysis of the applied reference parameters are provided and discussed. Finally, in Section 6
, the conclusion highlights the main advantage and limitation of the proposed FRI method and consideration for future work.
2. Study Area and Data
The study area of Maxvorstadt is one of the 25 boroughs within Munich city, located at the city center. The borough contains an area of 429.79 ha, and is composed of 69% of buildings, 7% of recreation area, and 24% of road surface [36
]. The geographical range of the study site is trimmed alongside the roads at the boundary of the administrative area of Maxvorstadt to exclude buildings crossing over multiple boroughs. Maxvorstadt consists of nine districts, which are Königsplatz, Augustenstraße, St. Benno, Marsfeld, Josephsplatz, Am alten nördlichen Friedhof, Universität, Schönfeldvorstadt, and Maßmannbergl (see Figure 1
a). Table 1
shows the number of buildings and area within each district. Figure 1
b illustrates the population and age distribution of each district. The population of Maxvorstadt lies mainly between 20 to 30 years old [36
]. Like other urban areas, the majority of the surface area within Maxvorstadt is sealed. However, there are parks, cemeteries, and lawns composing 7% of the total area as green spaces. Furthermore, some buildings are constructed with green roofs or roof-top gardens, making up more green surface areas. Figure 1
c shows the land use map of the study site.
The average yearly precipitation from 1981 to 2010 of Munich City was 944 mm [37
]. Regarding rainfall events, the German Meteorological Office (Deutscher Wetterdienst, DWD) provides a dataset storing grids of return periods of heavy rainfall over Germany (Koordinierte Starkniederschlagsregionalisierung und -auswertung des DWD, KOSTRA-DWD). The dataset contains statistical rainfall intensity values as a function of the duration and return period. It is often applied to assess damages caused by severe design rainfalls with regard to their return period [38
]. This paper applies the latest version of the dataset, the KOSTRA-DWD-2010R, which encompasses the time period from 1951 to 2010 and focuses on the 15 min duration rainfall events for various return periods (see Figure 1
In this paper, we developed an indicator-based flood resilience quantification method by introducing the time-varying Flood Resilience Index (FRI). The FRI is able to quantify the flood resilience level for households within an urban area, divided into event and recovery phases. Therefore, the new FRI embodies the definition of flood resilience as the capacity to withstand adverse effects following flooding events and the ability to quickly recover to the original system performance before the event. During the flooding event, the FRI is estimated based on physical indicators, namely the water depth, accumulated water depth, flooding duration, and water accumulation rate. During the recovery phase, the FRI is estimated based on social indicators, i.e., the percentage of households with children and that of elderly population, as well as an economic indicator, i.e., annual household income.
The sensitivity analysis of the parameters (and indicators) provided a useful tool to understand better how external influencing factors affect the FRI. The aggregated FRI results allow the identification of fragilities in the urban household as part of a system. It is easy to identify which households have a slow-recovering process or which are being hit severely by the event. Furthermore, the dispersiveness of the FRI curves also provides the information of how homogenously the urban components of the system react to a certain event.
The novel time-varying FRI therefore provides a novel insight into the indicator-based quantification method of flood resilience level for households in an urban area. The time-dependent characteristic of the proposed method contributes to advancing the research field by enabling a quantifiable characterization and visualization of how a system responds during and after a flooding event. Therefore, the introduced FRI could become a valuable tool for urban planning and public communication, and promote a better flood risk management plan. Future work will see the inclusion of the sewer network and possible extension of the urban area of Maxvorstadt, which is considered at the moment isolated from other boroughs in Munich City.