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A new GIS-based tsunami risk evaluation: MeTHuVA (METU tsunami human vulnerability assessment) at Yenikapı, Istanbul
© The Author(s) 2016
- Received: 28 December 2015
- Accepted: 6 July 2016
- Published: 22 July 2016
Istanbul is a mega city with various coastal utilities located on the northern coast of the Sea of Marmara. At Yenikapı, there are critical vulnerable coastal utilities, structures, and active metropolitan life. Fishery ports, commercial ports, small craft harbors, passenger terminals of intercity maritime transportation, waterfront commercial and/or recreational structures with residential/commercial areas and public utility areas are some examples of coastal utilization that are vulnerable to marine disasters. Therefore, the tsunami risk in the Yenikapı region is an important issue for Istanbul. In this study, a new methodology for tsunami vulnerability assessment for areas susceptible to tsunami is proposed, in which the Yenikapı region is chosen as a case study. Available datasets from the Istanbul Metropolitan Municipality and Turkish Navy are used as inputs for high-resolution GIS-based multi-criteria decision analysis (MCDA) evaluation of tsunami risk in Yenikapı. Bathymetry and topography database is used for high-resolution tsunami numerical modeling where the tsunami hazard, in terms of coastal inundation, is deterministically computed using the NAMI DANCE numerical code, considering earthquake worst case scenarios. In order to define the tsunami human vulnerability of the region, two different aspects, vulnerability at location and evacuation resilience maps were created using the analytical hierarchical process (AHP) method of MCDA. A vulnerability at location map is composed of metropolitan use, geology, elevation, and distance from shoreline layers, whereas an evacuation resilience map is formed by slope, distance within flat areas, distance to buildings, and distance to road networks layers. The tsunami risk map is then computed by the proposed new relationship which uses flow depth maps, vulnerability at location maps, and evacuation resilience maps.
- METU tsunami human vulnerability assessment (MeTHuVA)
- Tsunami risk analysis
- Geographic information systems (GIS)
- Multi-criteria decision analysis (MCDA)
- Analytical hierarchical process (AHP)
Tsunamis are the giant waves mostly triggered by earthquakes and/or submarine landslides. Despite the rare occurrence of tsunamis, they have been of interest to worldwide media since the early twenty first century with the repetition of mega earthquakes (Cartwright and Nakamura 2008; Mas et al. 2014). There are many associations researching and developing models to forecast tsunamis and create tsunami inundation and evacuation maps all over the world. The scientific and technical approaches for tsunami risk assessment are still in development despite the progress made after the Indian Ocean tsunami of 2004. In the last two decades, there have been considerably more studies related to hazard, risk, and vulnerability (Alexander 2000; Wisner et al. 2004). Many models have been developed to understand, assess, and map these three concepts (Fischer et al. 2002; Gambolati et al. 2002; Cheung et al. 2003). Validation of these models is required in order to make an accurate estimation of the real effects of natural disasters. The requisite of validation of hazard, risk, and vulnerability models is crucial, as the models based on these will form the foundation of the outputs used to define land use zoning and planning, emergency response actions, disaster planning, and insurance premiums (Tüfekci 1995; Jenkins 2000; Dominey-Howes and Papathoma 2007). Geographic Information Systems (GIS) are used in vulnerability assessment models for many types of natural or industrial hazards. Starting with the 2004 Indian Ocean tsunami, different methodologies have been developed to evaluate vulnerability to various types of natural hazards, including tsunamis (Papathoma et al. 2003; Ghobarah et al. 2006; Dominey-Howes and Papathoma 2007; Reese et al. 2007; Taubenböck et al. 2008; Dall’Osso et al. 2009a, b; Koshimura et al. 2009a, b; Wood 2009; Omira et al. 2010; Pendleton et al. 2010; Atillah et al. 2011; Leone et al. 2011; Murthy et al. 2011; Sinaga et al. 2011; Valencia et al. 2011; Eckert et al. 2012; Ismail et al. 2012; Suppasri et al. 2012a, b; Tarbotton et al. 2012; Usha et al. 2012; Suppasri et al. 2013; Santos et al. 2014; Benchekroun et al. 2015). In order to create appropriate models for hazard assessments, GIS tools are required for analyzing large amounts of data while generating thematic maps. The integration of various data sources can be performed, and the results obtained from the models can be presented as integrated with spatial and thematic data of selected region. In coherence with this approach, GIS tools are used in this study for further generating vulnerability assessment models while analyzing and integrating the results of numerical models.
The aim of this study is to further develop existing approaches, yielding a new methodology for GIS-based tsunami risk analysis, and thereby (i) to use high resolution (1 m) GIS-based data in tsunami numerical modeling and inundation analysis (hazard assessment), (ii) propose a new human vulnerability assessment method by further improving known vulnerability assessment aspects (locational vulnerability) and introducing new resilience assessment features (evacuation resilience), and (iii) propose a further developed tsunami risk evaluation equation by integrating the result of meter-size gridded high-resolution tsunami numerical models of different scenarios in the Yenikapı region in Istanbul, in order to obtain human vulnerability assessments.
Mitigation of tsunami impact can be achieved by providing faster evacuation for humans and by increasing the resistivity and performance of structures against tsunamis. Tsunami modeling is one of the important phases of tsunami hazard assessment. The source mechanisms, bathymetric, and topographical data in adequate resolution, as well as selection of probable tsunami scenarios, are used in tsunami numerical modeling (Aytöre et al. 2014; Aytöre 2015; Aytöre et al. 2015).
Estimated rupture parameters and initial wave amplitudes for tsunami sources PIN and YAN (Ayca 2012)
Depth from sea bottom
Initial wave amplitude
Tsunami source PIN is the normal component of the first four oblique-normal segments of tsunami source PI. In the simulations, it is assumed that four segments of PIN rupture have been broken entirely and generated the tsunami source. Tsunami source YAN consists of eight segments, of which three are oblique-normal and five are normal faults. In the simulations, it is assumed that eight segments of YAN have been broken entirely and generated the tsunami source.
Tsunami numerical simulations are performed using the NAMI DANCE code (NAMI DANCE 2015). The model solves nonlinear forms of shallow water equations with a friction term. The duration of simulations used is 90 min, and the time step is set as 0.005 s. Since the largest tsunami waves hit the study area in the first 50 min, the 90 min simulation duration is sufficient to obtain the major tsunami impact in Yenikapi, including additional reflections from all boundaries. In this study, the bottom friction coefficient is taken as zero in order to be on the safe side in the vulnerability analysis, which computes further inundation.
The nested domains are created to be high resolution, covering the Yenikapı region and surroundings. Three nested domains (from large to small B, C, and D) are selected with different resolutions. The corner coordinates of each domain is given as 40.210°–41.260°N, 26.542°–30.020°E; 40.971°–41.041°N, 28.920°–29.045°E; and 40.9949°–41.0050°N, 28.9520°–29.9794°E for B, C, and D, respectively. The largest domain (B) covers the whole Marmara with a resolution of about 90 m, the medium domain (C) covers the Yenikapı region with a resolution of 30 m, and the smaller domain (D) with a resolution of 10 m.
Since the tsunami computational tool NAMI DANCE allows a grid nesting ratio of three, at least five domains should be created for the high-resolution analysis (90 > 30 > 10 > 3.3 > 1.1 m). This process takes a very long computational time (up to a few months) even with a 64 processor computer. In order to save time, it is preferred to run the tsunami numerical model once for the nested domains (B, C, and D) and to obtain the water level change at the border of the smallest domain (Domain D). Afterwards, a very high resolution (1 m grid size) Domain D is developed using GIS implementation considering buildings, transportation networks, and infrastructure. At the final stage, a single domain (Domain D with 1 m grid size) simulation using the wave input from the border (computed from nested simulations) is applied.
In order to perform higher-resolution numerical modeling, necessary implementations for the dataset are performed. Two different types of data are used in this study. They are: (i) the topographical data in XYZ (longitude, latitude, and elevation) format made of 5 m spaced points to construct the natural topography of the Istanbul region and (ii) the building and infrastructure vector data with elevations to construct the metropolitan topography. A very high-resolution metropolitan digital elevation model with 1 m grid size is constructed by combining these two data sources, which are then joined with high-resolution bathymetry data to produce a seamless bathymetry and land topography input for detailed tsunami numerical model runs.
As seen in Fig. 5a, maximum flow depth exceeds 6 m near the shoreline east of the Yenikapı Fishery Port; it is represented in purple according to the simulation of the PIN source. Figure 5b shows the distribution of flow depth computed by simulation of the YAN source. In this simulation, the flow depth exceeds 6 m not only near the shore, but also in front of the high historical city wall which prevented water flow and caused accumulation of water volume in front. Hence, higher flow depths are observed in front of the historical city wall.
When the results of simulations of PIN and YAN sources are compared, it is observed that tsunami source YAN causes relatively longer inundation distances and higher flow depths at Yenikapı than tsunami source PIN. Therefore, results of the simulation of the YAN source (Fig. 5b) are reliable for use in the tsunami vulnerability analysis. These results are used as hazard intensity input in the next steps of this study.
Datasets used to compute vulnerability at location and evacuation resilience
Assumptions for vulnerability at location and evacuation resilience assessments
The earthquake is assumed to be a precursor to a tsunami, which warns people to consider the imminent arrival of a tsunami (with enough time to move to safer locations).
Buildings are considered rigid and undamaged by tsunamis.
It is assumed that vertical evacuation is possible in every building, and the number of floors are greater than one, except prefabricated buildings.
Day and night populations are assumed to be constant.
It is supposed that tsunami waves arrive at the same time at all locations on the Yenikapı shoreline in the study area (an approximately 2 km segment of the Yenikapı shoreline).
The duration of inundation is governed by the period of the tsunami wave.
According to the results of simulations using a critical scenario in a deterministic approach, the period of tsunami waves is estimated to be approximately 10–15 min. In the vulnerability analysis, the duration of the tsunami inundation is sufficiently long.
Vulnerability analysis at location
The tsunami vulnerability at location in Yenikapı is assessed by integration of metropolitan use, geology, elevation and distance from shoreline layers. These layers are produced from their raw data and combined in the previously decided MCDA framework. The input data and their relevant parameter maps/layers are as follows:
a) Metropolitan use layer
All the attributes of metropolitan use vector data are analyzed and grouped into meaningful units by gathering similar metropolitan use polygons. Here, 23 descriptive units are aggregated in an attribute table, which includes all buildings and structures, grouped into five main metropolitan use groups against tsunami vulnerability. These are extremely important places, such as prefabricated buildings, gas stations, electricity transformers, pedestrian underpasses, the İDO-Istanbul Sea-bus Terminal (the glass-wall building), the entrance to the Eurasia (Avrasya) Undersea Highway Tunnel, ruins, assembly areas (e.g., religious facilities, sports facilities, schools, wedding halls), flat areas (e.g., asphalt roads, suburban railways, parking spaces, green fields, and medians), cultural heritage sites (e.g., stationary city walls, demolished city walls, historical places/buildings), and buildings (e.g., factories, small-scaled production centers, buildings under-construction, residential areas/private homes, commercial buildings).
b) Geology layer
Tsunamis are mostly generated by earthquakes at inter-plate subduction areas. Once the stability of geological units is altered by lateral or vertical forces, unwanted slope instabilities, ground deformation, and liquefaction may occur. Furthermore, the geotechnical properties influencing local site conditions, and thus earthquake ground motion, may influence building damage and stability after stronger earthquakes, and thus building resistivity to waves in case of high energetic tsunami waves.
c) Elevation (DEM) layer
When a tsunami reaches land, standing on higher ground will keep coastal buildings, structures and infrastructures safe, compared with being at low elevation near the shore. Not only elevation, but also the distance from the shoreline is also used in the numerical computation of inundation parameters. These are the two basic vulnerability parameters of inundation that should have to be taken into account, regardless of the type of marine-induced hazard.
The natural terrain elevation dataset was produced by aerial photogrammetric techniques in 2006 by IMM with a 5 m pixel size. The pixel size is reduced to 1 m by resampling the DEM in order to make it coherent with the building (metropolitan) topography that was used to calculate the numerical tsunami models. Based on regional tsunami records and previous expertise, the elevation layer is divided into four classes: elevations lesser than 3 m, 3–5 m, 5–8 m and elevations higher than 8 m. The generated parameter layer is shown in Fig. 9c.
d) Distance from shoreline layer
In case of any tsunami threat, it would be good to be away from the shoreline. Structures near the shoreline would be in danger. Independent of the resistivity of the buildings and/or structures depending on material type, proximity to the shore increases the probability of being close to or within inundation, which would affect vulnerability negatively. Hence, distance from shoreline is taken as a parameter to determine vulnerability (Çankaya 2015).
This layer is calculated as the nearest perpendicular distance of each 1 m raster cell from the shoreline vector (Fig. 9d). The computed continuous raster is then divided into five classes: distances less than 50 m, 50–100 m, 100–200 m, 200–300 m and distances greater than 400 m.
Evacuation resilience analysis at Yenikapı region
Recently, research has indicated that a key concept in the assessment of tsunami events is resilience. As previously introduced, there are four layers in this group: distance to buildings, slope, distance to road networks, and distance within flat area layers. The MCDA framework is created to calculate the resilience score for evacuation of places that may be exposed to tsunamis (Fig. 8).
a) Slope layer
b) Distance within flat areas layer
Although open areas are safer locations during an earthquake, lack of shelter and absence of vertical evacuation make open areas susceptible to tsunamis. Hence, the distance within an open area limits the evacuation potential (Çankaya 2015). For producing this layer, the metropolitan use layer is used as input to select flat areas from available attribute tables. Parking lots and other open spaces (including green fields and public squares) are selected, and the nearest perpendicular distances within these units are calculated (Fig. 11b). Distances within flat areas layer are divided into three classes: nearer than 10 m, 10–30 m, and farther 30 m.
c) Distance to buildings layer
The number of the floors in a building should be considered in calculating evacuation resilience of residents. However, when buildings are low and evacuation in the vertical direction is not possible, injury and death will be inevitable (Dominey-Howes and Papathoma 2007).
It is accepted that where vertical evacuation is possible, residents are able to evacuate from the tsunami disaster easily (Mas et al. 2014), thus preventing risk of injury and death. All types of buildings in the attribute table of the metropolitan use layer are integrated to generate the parameter layer for buildings. The nearest perpendicular distances to the building polygons from any location on the map are calculated for every 1 m pixel (Fig. 11c). The distance to the buildings layer is divided into five classes: less than 10 m, 10–50 m, 50–100 m, 100–250 m, and greater than 250 m.
d) Distance to road networks
The location of residents in the area during a tsunami is critical. In most cases, there is a warning and alert system at locations that have been exposed to tsunamis. Evacuation signs and routes produced by local officials are available in such places. Tsunami evacuation routes/roads guides coastal residents to safer locations in case of natural disasters such as earthquakes and tsunamis. Evacuation signs are placed along routes to mark the direction inland or to higher elevations. If the coastal area does not have any evacuation routes, the main roads should be considered for escape when a tsunami approaches inland. In tsunami-flooded flat areas, it is very difficult to reach safer places on high ground and away from the shoreline.
The metropolitan use layer is used to select suburban railways and asphalt roads as potential escape corridors. The nearest perpendicular distances to these corridors are calculated, and distances to the road network layer are presented in Fig. 11d. The distances to the road network layer are then classified into five classes: less than 5 m, 5–10 m, 10–20 m, 20–50 m, 50–100 m, 100–250 m, and greater than 250 m.
Creating AHP framework and final map production for vulnerability at location and evacuation resilience scores
Saaty’s (1990) rating scale
Very strongly dominant
2, 4, 6, 8
For inverse judgments
The computed weight and rank values of vulnerability at location
Weight × rank
Distance from shoreline
Extremely important places
The computed weight and rank values of evacuation resilience
Weight × rank
Distance to buildings
Distance to road networks
Distance within flat areas
Tsunami risk analysis
A new approach to tsunami risk analysis via preparation of vulnerability and evacuation resilience maps for the Yenikapı coastal region, combining tsunami numerical modeling and GIS-based MCDA, is presented.
The maximum flow depths and inundated area based on simulations of the PIN and YAN tsunami sources are obtained, and the inundation maps are plotted. The inundation maps produced by simulation of these two tsunami sources are compared. According to the generated inundation maps, the tsunami source YAN is found more critical than the tsunami source PIN, since it causes greater flow depth and longer inundation distance.
The map of evacuation resilience is given in Fig. 17b. The buildings are obviously the most resilient places, since it is assumed they allow vertical evacuation. In contrast, the breakwaters are the least resilient places because of their proximity to the sea and escape routes. The more resilient places, including buildings, structures, road networks, and flat areas are colored blue. The less resilient areas for evacuation are places near the shore, west of the Yenikapı Fishery Port. The reason for this is the existence of fish restaurants, which are assumed to be rigid and not easily damaged. Likewise, depending on the evacuation resilience map, the glass-wall building is represented in blue as a safer place because it allows to vertical evacuation, thus its locational vulnerability is decreased. When the study area is visited, it is noted that the buildings mostly consist of more than one story in the Yenikapı region. The dominant effect of the building layer with the weight of 0.5808 is seen in the evacuation resilience map.
The vulnerability and evacuation resilience maps are combined for two critical tsunami sources, PIN and YAN, separately. For these two sources, the risk maps are produced using proposed Eq. (1). The vulnerability and evacuation resilience are relatively defined for each pixel in the study boundary. The values of vulnerability and evacuation resilience maps are in the range of 0–1. In the risk maps, blue represents the relatively safer places, whereas the more hazardous places are depicted in red. White pixels represent the value of zero because of no tsunami effect (in other words, flow depth is equal to zero). Tsunami risk increases from the blue to red-colored areas. According to the colors in the maps, the less hazardous places near shoreline are the İDO-Istanbul Seabus Terminal (ignoring the construction material), the restaurants behind the Yenikapı Fishery port, and the wedding hall located west of the study area, near the shore. The relative vulnerability east of the Fishery Yenikapı port is at a maximum because of the gates of the ancient city walls. A small part of the meeting area west of the study area is seen an important place because of tsunami impact. The entire meeting area must be considered in tsunami risk analyses, and the necessary precautions should be taken accordingly before facing a tsunami disaster.
In this study, the main road in the study area runs parallel to the coastline, which is not convenient for evacuation when a tsunami occurs. The places assigned as extremely important places (the prefabricated buildings, the glass wall building, the entrance to the Eurasia Undersea Highway Tunnel, ruins, the entrance to pedestrian underpasses, gas stations, electricity transformers) are more vulnerable considering vulnerability at location and evacuation resilience. Some car underpasses on the main road in Yenikapı would cause penetration of water inland, which would probably make evacuation more difficult.
In this study, a new approach is applied to define tsunami human vulnerability parameters, and a new model for high-resolution tsunami risk analysis based on tsunami numerical modeling and GIS-based MCDA is proposed.
There is tsunami potential in the Sea of Marmara, and tsunami risk analysis, including detailed vulnerability, hazard, and risk analysis are necessary. A new approach is presented and tested with the case study for the Yenikapı region in Istanbul.
Determination of the tsunami sources affecting the study area is one of the main requirements of tsunami numerical modeling. These sources must be analyzed and compared to determine the critical deterministic tsunami scenarios for the study area. A valid and verified numerical model is necessary for detailed computation of tsunami parameters (such as inundation, maximum positive amplitudes, flow depths, and maximum currents) near shore and over land. The inundation map, including the flow depth (depth of overland tsunami flow), must be calculated.
High-resolution bathymetry, topography, and vector data of metropolitan use are the main requirements for the proposed, detailed and proper vulnerability, resilience, hazard, and risk analyses.
A relationship for tsunami risk analysis is proposed (Eq. 1) considering hazard intensity, vulnerability of location, evacuation resilience, and the community’s degree of tsunami preparedness.
The flow depth in Yenikapı exceeds 6 m near the shore, and between the historical city walls behind the Yenikapı Fishery port and the shoreline, because of the accumulation of water volume. The inundation distance reaches 200 m inland, depending on the YAN source simulations by tsunami numerical modeling.
Vulnerability and evacuation resilience mapping are conducted, and inundation is assessed using GIS-based MCDA. The AHP method is used to assign weight values to parameter layers and rank values to the classes. Four parameter layers (distance from shoreline, geology, elevation, metropolitan use) are prepared and used to produce the locational vulnerability map. Four other parameter layers (distance to buildings, slope, distance to road network, and distance within flat areas) are prepared and used to produce the evacuation resilience map. The weight/rank values for these parameters are revealed using the AHP method, and combined accordingly to produce vulnerability at location and evacuation resilience maps for the Yenikapı region. In the calculation of the vulnerability score at location, the distance from the shoreline layer is the most influential layer depending on its weight value (0.4833), whereas the least influential layer is the metropolitan use layer based on its weight value (0.1042). The most effective parameter for evacuation resilience is found to be the distance to buildings layer with the value of 0.5808, whereas the least effective is the distance within flat areas layer with the value of 0.0716.
Two tsunami risk maps are generated by combining the results of tsunami numerical models of two different sources, and GIS-based MCDA method results (vulnerability map and evacuation resilience map) in the new proposed tsunami risk map Eq. (1). For the Yenikapı region, the tsunami source YAN is selected as the most critical source, since the results of the simulations for YAN give higher flow depths and longer inundation distances. Therefore, the risk map obtained by combining the results of YAN source simulations, vulnerability at location, and the evacuation resilience is presented to be considered in the tsunami disaster mitigation system.
ZCC compiled the data and carried out MeTHuVA procedure. She also made tsunami numerical simulations for Yenikapi and prepared M.Sc. thesis which was the main starting document of this paper. MLS developed MeTHuVA procedure, supervised M.Sc. thesis, and assessed the quality of the compiled data. ACY, developed the tsunami numerical model with AZ, commented on development of MeTHuVA, co-supervised M.Sc. thesis, monitored the simulations and checked the quality of outputs and the figures of simulation outputs. CK implemented MCDA procedure, and compiled the data. AZ developed the tsunami numerical code NAMI DANCE with ACY, and developed necessary modules in teh code to output required tsunami parameters for the paper. BA made tsunami numerical simulations from the source to Yenikapi and reviewed the processed (mainly bathymetry) data and applied to the numerical model. All authors read and approved the final manuscript.
This study was partly supported by a Japan–Turkey Joint Research Project of JICA on earthquakes and tsunamis in the Marmara region by MARDim SATREPS; EC project ASTARTE—Assessment, Strategy And Risk Reduction for Tsunamis in Europe-FP7-ENV2013 6.4-3, Grant 603839; UDAP-Ç-12-14 project granted by Disaster Emergency Management Presidency of Turkey (AFAD), the RAPSODI (CONCERT_Dis-021) project in the framework of CONCERT-Japan, Research and Innovation Joint Call for connecting and cooperating European Research and Technology Development with Japan and TUBITAK 113M556 and 108Y227 Projects. The authors also thank Duygu Tufekci, Rachid Omira, and the anonymous reviewers for their valuable and constructive comments.
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