Flooding is one of the most destructive natural disasters which have rapidly been growing in frequency and intensity all over the world. In this view, assessment of the resilience of the city against such disturbances is of high necessity in order to significantly mitigate the disaster effects of flooding on the city structures and the human lives. The aim of this paper is to develop a method to assess the resilience of a river city (the city of Gothenburg in Sweden), which is prone to flood Hazard, against such disturbances. By simulating flood inundation with different return periods, in the first step, the areas of impact are determined. To assess the resilience, two different methods are followed. One is a syntactic method grounded in the foreground network in space syntax theory and the other is based on measuring accessibility to the essential amenities in the city. In the first method,
similarity and
sameness parameters are defined to quantitatively measure the syntactic resilience in the city. In the next step, accessibility to amenities and the minimum distance to amenities before and after each disturbance is measured. The results, in general, show that such disturbances affect the city structure and the resilience of the city differently. For instance, the city is more resilient after flooding according to accessibility measures. This clearly means that the answer to the question of resilience is mainly dependent on “resilience of what and for what.”
Flooding Resilience GIS Space Syntax Accessibility1. Introduction
Flooding as a natural disaster has great impacts on both individuals and communities and causes social, economic and environmental consequences is flooding. Flood is defined as a great overflow of water which especially submerges normally dry lands. Given the broad and intensive impacts on human loss, damaging of transportation, power plants and waste water systems, crops and farming and many other subsequences, people during the history put lots of effort to develop solutions to reduce flood impacts [1] . However, given the aberrant human interference in ecological system, urban sprawling, climate change, besides natural factors such as topography, geology of basins, land cover, amount of rainfall and etc. [2] [3] , the frequency and intensity of flooding is increasing [4] [5] [6] . Based on statistics from the Centre for Research on the Epidemiology of Disasters [7] in the period 1994-2013 floods were the most frequent type of disasters, accounted 43 percent of all disasters and affected 55 percent of total population of the world during the past two decades. The report also emphasizes that floods had the most economical damage (US$ 636 billion) after the storms and earthquakes during this period. Flood forecasting and flood warning, as European Union notices, is a “prerequisite for successful mitigation of flood damage” [8] . One of the frequently occurring flooding is river flooding which occurs when a river excesses the capacity of its canal because of rainfall, flash flooding, snowmelt and etc. and overflows its banks and the surrounding areas called flood plain. When the flood plain includes the urbanized areas, such flooding has great impacts on both population and infrastructures. Therefore, flood risk management in urban areas plays an essential role in preventing or reducing the impacts of flooding [9] . Flood simulation has a long history and from two centuries ago hydrologist and engineers have attempted to develop and improve prediction models of water flow in canals and watersheds [10] . The state-of-the-art in flood modelling is using GIS capability as a platform which enables researchers to combine physical and hydrological measurements with social and urban infrastructure data and estimate urban flood damage and flood vulnerability [11] [12] [13] [14] .
1Hydrologic Engineering Centers River Analysis System(HEC-RAS) is one of the most applicable software in flood modelling released by USACE (United States Army Corps of Engineers, http://www.hec.usace.army.mil/).
Demir and Kisi [12] , in a study on the Mert River in Turkey, integrated GIS and hydraulic model to represent a flood risk map. Creating a digital elevation model (DEM) of the study area and taking advantage of HEC-RAS1 software, they simulate flood flows of different return periods in a range of 5 to 10000 years. The result showed that the area in the downstream of the Mert River affected by a flood with a return period of 10-years was 30 percent which was comparable with the impacts of an occurred flood in 2012 in the same area. In another study in the central part of Gothenburg, Sweden, Filipova and collogues [15] using MIKE 21 as hydrologic simulation software developed a 2-dimen- sional simulation model of flooding. Data used in this research was DEM, temporal precipitation data and land use values. The result showed that different part of the city including streets, canals and residential area can be affected by flood flow in case of intense precipitation and overloading the drainage system. Some researchers go steps further and not only simulate flooding but try to also measure the vulnerability of a city to flooding and make a city more resilient against flood. In this way, Lhomme and collogues [1] , developing a Web-GIS for resistance capacity, absorption capacity and recovery capacity of different networks, tried to propose a framework to improve urban resilience in facing to flooding. In their research, the stress was on the urban networks and how the efficiency of such networks plays an important role in economic activity and other public facilities and infrastructures. Gil and Steinbach [16] used objective properties of the road network such as connectivity, closeness and between ness to show the indirect effect of flood Hazard on transport and socio-economic situations in London. In their paper, areas of impact were assessed based on two parameters, the level of separation from the rest of the city and the degree of accessibility to the rest of the city. The term resilience was coined in ecology by Holling [17, P.14] and defined as “a measure of the persistence of systems and of their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables”. Given the enhancing use of the term in many different fields such as economy, sociology, networking and engineering the definition and concept of resilience can vary and sometimes is malleable as it can be in contrast to its definition in other disciplines. The main difficulties occur when efforts are devoted to form the concept in order to take the term out of a purely abstract and general form and advance it toward a specific characteristic in a measurable and functional form [18] [4] [19] . However, there are common definitions of resilience in urban planning and disaster management. Wilbanks [20] defined urban resilience as “capability to prepare for, respond to, and recover from significant multi-hazard threats with minimum damage to public safety and health, the economy, and security” and Lhomme et al. [21] defined it as “the ability of a city to operate in a degraded mode (absorption capacity) and to recover its functions, despite the fact that some urban components are disrupted”. UNSIDR [22] defined resilience in disaster management as “the ability of a system, community or society exposed to hazards to resist, absorb, accommodate to and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions.” In this view, as Carpenter et al. [23] noticed the main questions are resilience of what or resilience to what rather than the terminology itself. In contrast to flood modeling and flood simulation with a long history, urban resilience against flood Hazard is still under development due to the complexity of the urban systems and urban drainage systems [24] . It has been proven that urban forms and urbanization patterns are in close relationships and interaction with natural phenomena and the balance between them might break or disrupt with changing or damaging the physical structure of the cities [25] [26] [27] [28] [29] . Flood Hazard and urban flooding thus as physical phenomena are influenced by the form of the cities and thus the magnitude of their impacts on urban infrastructures and human lives can be intensified by urban infrastructures such as street networks and buildings [24] . The aim of this research is to measure the resilience of a city against flooding according to the impacts of flooding on the street networks using two different methods; a syntactic measurement of the street networks discussed in space syntax research [27] [30] , as its combination with resilience has recently proposed different approaches in assessing resilience, covering a range of possibilities from theory to practice at different scales of built environment from building to urban scale [18] [31] [32] [33] ; and a metric measurement of accessibility of the street networks to amenities. In this view, comparing the syntactic properties of the street networks before and after flooding as well as exploring changes in the accessibility of the street networks after flooding are the ways conducted in this research in order to investigate the degree of resilience and vulnerability of the city against flooding.
The rest of this paper is organized as follows: in the next section, the study area and data used in this research are explained. The method to simulate flooding is briefly discussed and mostly referred to the work of the other researchers, since they are not central to this paper. Then, syntactic properties of the street networks are introduced and the way to calculate syntactic parameters of resilience is explained. Taking advantage of spatial analyses and statistics, the results of different analyses are illustrated and discussed in the result and discussion section. The paper ends up with the conclusions.
2. Study Area and Dataset2.1. Study Area
Gothenburg with a population of 544285 is the second largest city in Sweden after the capital Stockholm (SCB, 2016, www.scb.se). The city is located on the west coast, in southwestern Sweden and the average elevation of the city is about 12 meters from the sea level. Göta River originates from the Vanern Lake and after over a distance of 93 km and passing through the city of Gothenburg empties into the Kattegat sea area in Baltic Sea. Göta River is Sweden's largest and richest rivers and represents, for example, the water source for drinking water to approximately 700,000 people. The river is used for many different interests. Göta River is also of great importance for agriculture and absolutely necessary to industries and other businesses to function. With an average water discharge of 563 m3/s Göta River as a natural resource not only influences the flora and fauna around itself but it also has a great impact on the community and the city streaming in [34] . Figure 1 shows the city and the flow of Göta River.
The city of Gothenburg is located in the estuary of Göta River along the coast of Kattegat, placing the city at high risk of rising water level of both the sea and the river, causing flooding in the city. Following a global trend, the rate of flooding in Gothenburg is increasing in recent years [15] [35] . Thus, measuring and estimating the flood risk, mapping and assessing potential damage and eco-
nomical loss are not only of interest for researchers but are critical for city planners and other societies of the city dealing with flood Hazard and city infrastructures [34] .
This paper aims at estimating flood in the city with different return periods in order to map the area at high risk and from that assess the resilience of the city against different simulated floods. The study area, in this research, is limited to that part of the river streaming in the city (municipality border) and flood simulation and its potential impacts on the structure of the city are investigated in such area.
2.2. Dataset
Data required for this paper are obtained from the following sources:
- Digital elevation model (DEM) with a resolution of 2 meters which covers the whole study area. The data source used for DEM production is LAS dataset which is an industry-standard binary format for storing airborne LIDAR data, provided by the Lantmäteriet (Swedish National Land Survey: http://www.lantmateriet.se/ ) with an average spacing of 0.6 meter. All the analyses and converting LAS dataset to raster were done in ArcGIS and using LAS dataset toolbar embedded in this software.
It should be noted that in order for accuracy of the flooded area, in this research, both digital train model and digital surface model are produced. In this view, the real height of objects such as street networks and bridges can be determined, helping find out if the flooded area inundates such objects.
- Urban road network or road center line of Gothenburg derived from open street map (OSM, downloaded from https://www.geofabrik.de/ ).
- Urban infra structures including the location of amenities (Figure 2) collected from a project called “Dela[d] Stad” [36] funded by Boverket/MISTRA Urban Futures (Table 1).
- Hydrological data and measurements of the river flows for a period of 15 years collected from the Swedish Meteorological and Hydrological Institute (SMHI)
( http://vattenwebb.smhi.se/modelarea/ ).
The normal editing on street networks and other data are done to make them GIS-ready and usable for the next analyses.
Due to completely different but relative analyses done for this research, the methodology is divided into three parts. In the first part, the methods to simulate flood flow explained. In the second part, the method used for defining and measuring the resilience in the city based on syntactic properties is discussed
Different amenities used in this study
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