FullPapers 5thITS Ispra Theilen Willige

Proceedings of the Fifth International Tsunami Symposium

(ISPRA-2012)

Tsunami Society International

3-5 Sept. 2012, Joint Research Centre, Ispra, Italy

Remote Sensing and GIS Contribution to the Inventory of Infrastructure susceptible to Earthquake and Tsunami Hazards

- demonstrated by Case Studies in Japan and Chile

Barbara Theilen-Willige Berlin University of Technology (TU Berlin), Institute of Applied Geosciences

Helmut Wenzel VCE GmbH, Vienna

SUMMARY: The contribution of remote sensing and GIS techniques to earthquake and tsunami hazard analysis was investigated in NE-Japan and Central-Chile in order to contribute to the systematic, standardized inventory of those areas that are more susceptible to earthquake ground motions, to earthquake related secondary effects and to tsunami-waves. When knowing areas with aggregated occurrence of causal (“negative”) factors influencing earthquake shock and, thus, the damage intensity, this knowledge can be integrated into disaster preparedness and mitigation measurements. Keywords: RapidEye, local site conditions, tsunami flooding, NE-Japan, Central-Chile 1. INTRODUCTION When catastrophic earthquake and tsunami hazards happen and affect cities, settlements and infrastructure, immediate and efficient actions are required which ensure the minimization of the damage and loss of human lives. Proper mitigation of damages following disastrous events highly depends on the available information and the quick and proper assessment of the situation. Responding local and national authorities should be provided in advance with information and maps where the highest damages due to unfavourable, local site conditions in case of stronger earthquakes and earthquake related secondary effects such as landslides, liquefaction, soil amplifications or compaction can be assumed. The better a pre-existing reference database of an area at risk is prepared and elaborated, the better a crisis-management can react in case of hazards and related secondary effects. The potential of social and economic losses due to those events is increasing. Therefore information of geodynamic processes is a basic need for the long-term safety of cities, settlements, infrastructure and industrial facilities. The assessment of potential hazard prone areas is fundamental for planning purposes and risk preparedness, especially with regard to supervision and maintenance of settlements, infrastructure, industrial facilities and of extended lifelines. Areas at particular risk include networks, buildings, production, extracting and processing plants and non-electronic data records (Federal Ministry of the Interior, 2005). Technical interdependencies between infrastructures have a potential for initiating widespread cascading effects of failure or loss of service. The catastrophic events in March 2011 in Japan have demonstrated this. The Tohoku earthquake was a magnitude 9.0 (Mw) undersea megathrust earthquake, that occurred at 14:46 JST (05:46 UTC) on Friday, 11 March 2011,

with the epicenter approximately 70 kilometres east of the Oshika Peninsula of Tōhoku and the hypocenter at a depth of approximately 30 km (USGS, Earthquake Hazards Program,2011). The clash of a climate change driven increase of flooding hazards such as storm surge and flash floods with explosive, uncontrolled urban sprawl and changing urban patterns in coastal areas contains a further increasing risk. The research challenge has to strengthen the economic and societal resilience to potential disasters and to improve preparedness, prevention and mitigation through more appropriate risk assessment and new management strategies. This highlights the need for multidisciplinary scientific approaches to converge as well on the problem of identification of vulnerability. (Vulnerability is the condition determined by physical, social, economic and environmental factors or processes, which increase the susceptibility of a community to the impact of hazards, UN/ISDR, 2004.) An important aspect for the vulnerability assessment and damage loss estimations is the almost actual inventory of land use and infrastructure (bridges, railroads, roads, river embankments, etc.), of industrial facilities and of the structure of settlements and cities (considering age, structure and function of buildings). In the scope of this study open-source tools as OpenStreetMap or Google Earth were used for gaining the necessary information, as well as evaluations of RapidEye satellite imageries, ESRI base maps and further Web-tools. Airborne and spaceborne remote sensing systems and image analysis techniques have developed to an extent where civil and commercial earth observation (EO) instruments can contribute significantly to supporting the management of major technical and natural disasters as well as humanitarian crisis situations. A standardized, reference data base of industrial facilities and of critical infrastructures with environmental impact in case of accident should be available, in order to improve the preparedness and mitigation management. The aim of this contribution is to develop adaptation strategies by presenting an approach in which Geographic Information Systems (GIS), used together with remote sensing data, contribute to the analysis and presentation of information, especially required for the increasing geo-hazards in coastal areas such as earthquakes, landslides, flooding, tsunamis or storms. The ability to undertake the assessment, monitoring and modelling can be improved to a considerable extent through the current advances in remote sensing and GIS technology. Geographic Information Systems (GIS) provide the appropriate platform for the registration and management of information. Causal or critical environmental factors influencing the disposition of settlements, industrial and infrastructural facilities to be affected by natural hazards and the potential damage intensity can be analysed interactively in a GIS database. The interactions and dependencies between different causal factors can be visualized and weighted step by step in this GIS environment. Objective is the detection of areas more susceptible to hazards and, thus, as consequence, the vulnerability assessments according to a standardized, systematic and clearly arranged approach that can be used in any area. The elaboration of a database for factors of local site conditions which influence the damage potential, for example in case of earthquakes the shock intensities, could become in the future part of a comprehensive management system. Local site conditions play an important role when considering earthquake shaking and damage intensities and their local variations. The ground-shaking during an earthquake predominantly depends on complex factor interactions such as the magnitude, properties of fault plane solutions, the distance from the fault and local geologic conditions. An estimation of expected ground motion is fundamental for earthquake hazard assessment. Local, geomorphologic site conditions play an important role as well when considering flooding susceptibilities. Local morphometric properties, that can be derived from digital elevation data and evaluations of aerial and satellite data from any coastal area according to the same approach in a GIS, influence the susceptibility to flooding. Whenever inundation events happen in coastal areas due to flash floods, storm surge or tsunami waves, the morphometric settings determine the susceptibility to be affected by inundations to a great extent. 2. METHODS In the scope of this study satellite data were used for gaining almost actual information of land use information by its extraction from digital processed satellite imageries. Some land use and forest layers, are dynamic in nature and need to be updated frequently. For disaster preparedness the almost

detailed detection and documentation of settlements, infrastructure, industrial facilities, etc. that might be exposed to earthquakes, especially their different exposures to soil amplification, landslides, active tectonic processes or tsunami waves is of fundamental value. Furtheron, satellite data served as base for lineament analysis. 2.1. Digital Image Processing Actual land use information were derived from evaluations and classifications of the available LANDSAT, ASTER and RapidEye data, as well on available open-source data such as open-street-map, Google Earth and ESRI-geodata. An important aspect is the delivery of satellite data for both, • for creating a most actual, high spatial resolution, GIS integrated reference data base visualizing critical points and areas, and • for providing information of damages in case of emergency due to natural hazards as fast as possible, as the civil protection units need the information for their management. Digital image processing was used not only for the enhancement of RGB-imageries in ENVI software, but also to derive water index (NDWI -Normalized Difference Water Index) and vegetation index (NDVI Normalized Difference Vegetation Index) images. The NDVI is determined by using bands 3 (Red) and 4 (NIR): NDVI = (4 − 3)/(4 + 3). The Normalized Difference Water Index (NDWI) is a satellite-derived index from the Near-Infrared (NIR) and Short Wave Infrared (SWIR) channels (Gao, 1996). These images help to detect the influence of the flooding event by abrasion and debris sediments on soils and vegetation in those areas prone to tsunami flooding. This is demonstrated by the case of the Sendai-area in NE-Japan (Fig.1). Especially on NDVI-images these areas are characterized by low to no vegetation photosynthetic activity and on NDWI-images by higher soil moisture. As settlements appear in dark-blue colours as well, a careful correlation of the evaluation results with land use information is necessary.

Figure 1. Colour-coding and histogram-stretching of NDWI- and NDVI-scenes of the Sendai area in NE Japan

Another important digital processing method is the supervised and unsupervised image classification. The results of the image evaluations are classes, subdividing the areas for example into residential areas, industrial and commercial areas, streets, forest, grassland, farmland, wetland, and water. In urban areas the inventory of building stocks, built-up density, floor-space index, building heights,

vegetation fraction, infrastructure, or undeveloped areas is relevant for the vulnerability identification and quantification of (Fig.2).

Figure 2. Land use mapping based on satellite data provided by USGS, Global Land Cover Facility /University of Maryland, OpenStreetMap and ArcGIS-Online / ESRI

2.2. Evaluations of Digital Elevation Data For getting a geomorphologic overview morphometric maps were created and terrain parameters extracted based on DEM data as shaded relief, aspect and slope degree, minimum and maximum curvature or plan convexity maps using ENVI and ArcGIS software. For example the morphometric parameters height level, slope degree and minimum curvature provide information of the terrain morphology related to inundation susceptibility.

The integration of different factors in a GIS environment using weighting procedures served as one of the key objectives in the GIS application for this study. The application of a weight-linear-combination in susceptibility assessment has been identified as a semi-quantitative method, involving both expert evaluation and the idea of ranking and weighting factors. Yet it is capable of producing quantitative results based on expert evaluation, forming a quick-to-implement and cost efficient method. The weighted overlay method takes into consideration the relative importance of the parameters and the classes belonging to each parameter (ESRI, online support in ArcGIS). The basic pre-requisite for use of weighting tools of GIS is the determination of weights and rating values representing the relative importance of factors and their categories. The weights and ratings are determined using subjective experts knowledge. The method starts by assigning an arbitrary weight to the most important criterion (highest percentage), as well as to the least important attribute according to the relative importance of parameters. The susceptibility is calculated by adding every layer with a weighted influence together and to sum all layers. The factors influencing the occurrence, type and intensity of earthquake induced secondary effects can be separated into causal and triggering. The causal factors determine the initial favourable conditions for the occurrence while the triggering factors such as high precipitation rates determine more the timing. Causal factors are the slope gradient, curvature, lithology, groundwater

table level and land use, etc. The triggering mechanisms are quite unpredictable, as they vary in time, however, the causal factors can be integrated as layers into a GIS. The influence of the factors on earthquake ground motion is not equally important in the analysis, varying according to the specific local settings (surface geology, structure) or according to the distance to the earthquake source. The percentage of influence of one factor is changing as well due to seasonal and climatic reasons, In very hot and dry seasons the risk of liquefaction and landslides is generally lower than in wet seasons with high precipitations. As a stronger earthquake during a wet season will probably cause more secondary effects than during a dry season, the percentage of its influence has to be adapted to the specific situation. Therefore the percentage of the weight of the different factors has to be adjusted as well to seasonal effects. The efficacy of the weighted overlay-method lies in the fact that human judgement can be incorporated in the analysis.

The sum over all factors / layers that can be included into GIS, provides some information of the susceptibility to amplify seismic signals. After weighting (in %) the factors according to their probable influence on ground shaking, susceptibility maps of can be elaborated, where those areas are considered as being more susceptible to higher earthquake shock intensities, where “negative” causal factors occur aggregated and are interfering with each other. This approach is described as Weighted-Overlay for Soil Amplification Detection (WOSAD) approach using ArcGIS and ENVI-software (Fig.3). It comprises the overlay of some of the causal factors that can be determined systematically: From SRTM and ASTER DEM data derived causal factors such as

• slope degrees < 10°, • drop calculation < 200.000, providing information of highest surface water flow input • minimum curvature > 250, (calculation in ENVI-software providing information of flat,

broader valleys, basins and depressions with younger sedimentary covers and higher groundwater tables, result is a grey-tone image with values between 0-255)

• The lowest local height levels are indicating areas with relatively higher groundwater tables. • Flow Accumulation > 1, highest flow-accumulations, providing information of areas with

higher surface water-flow input. These information are combined with lithologic and seisomotectonic information in a GIS data base as

• from geologic maps derived Quaternary sediment distributions and faults, • from LANDSAT ETM and RapidEye imageries derived lineaments, • from International Earthquake Centres downloaded earthquake data (International

Seismological Centre, ISC, US Geological Survey, USGS, etc.) • Vs30-IDW-interpolation (data from USGS) • Shake maps, macroseismic observation records and further available data

The causal factors were selected based on the availability of data and on the study area’s specific geological, geomorphological, climatic, tectonic and land-use features. The different factors were converted into ESRI-GRID-format and summarized / aggregated and weighted in % in the weighted overlay-tool of ArcGIS according to their estimated influence on the local specific conditions or in equal percentages. Those areas are considered to be susceptible more to soil amplification where the following causal factors are summarizing and aggregating their effects: lowest height level of the terrain combined with relative high groundwater tables, flat morphology with low slope gradients and no curvature, loose sedimentary covers within a basin topography or within flat coastal areas. When an area is underlain by larger, active fault zones, especially when intersecting each other, the soil amplification susceptibility will probably rise, depending on given specific earthquake properties and parameters (Theilen-Willige, 2010, Theilen-Willige & Burnett,2011). The resulting maps are divided into susceptibility classes. The susceptibility to soil amplification is classified by values from 0 to 6, whereby the value 6 is standing for the strongest, assumed susceptibility to soil amplification due to the aggregation of causal factors. When comparing the results of the weighted overlay-calculations with geologic maps, there is a clearly visible coincidence of areas with higher susceptibility values and the outcrop of unconsolidated, Quaternary sediments in broader valleys and depressions. Whenever an earthquake happens, now it can be derived better where the “islands” of higher ground shaking are most likely to occur in the affected areas by adding the specific information of the

earthquake to the susceptibility map using the WOSAD- weighted overlay - approach. The hereby presented approach is proposed to serve as a first basic data stock for getting a perception of potential sites susceptible to higher earthquake ground motion, including in next steps the integration of further, available data such as movements along active faults, focal planes, 3D structure, lithologic properties and thickness of lithologic units or shear wave velocities. The analysis method and integration rules can easily be modified in the open GIS architecture as soon as additional information becomes available.

Figure 3. Workflow for the weighted overlay approach aggregating factors with influence on local site conditions

This approach helps as well to map areas susceptible to flooding. The flooding risk (the probability for damage) is related to vulnerability (the potential for damage), which in turn is a function of a number of parameters that include amongst others: distance from the shore, depth of flood water, construction standards of buildings, socio-economic status and means, level of understanding and hazard perception and amount of warning and ability to move away from the flood zone (Papathoma et al.2003). Therefore, a flooding vulnerability analysis should rely on a database that includes as many of these factors as possible in order to gain a more realistic preview of spatial and temporal patterns of vulnerability. There are numerous sources which may be considered responsible for severe tsunami events. Probable tsunamigenic sea bottom structures have to be taken into account. The evaluations of geologic and geophysic information such as distribution of fault zones and earthquake induced fault movements, submarine activity of volcanoes, turbidity currents and submarine mass movements, or even cosmic impacts, form an important input for tsunami risk assessment. Thus, as high energetic flood wave generation is related to various processes, hydrodynamic modelling is a very complex task. The input of remote sensing and GIS can be considered only as a small part of the whole “mosaic” of tsunami research approaches. Nevertheless, it offers a low-cost to no-cost approach (as the used DEM data are free), that can be used in any area without high-level expertise, providing a first basic data stock for emergency preparedness by providing susceptibility-to-flooding maps.

Near-shore-bathymetry and the coastal morphology have a great influence on the inundation extent of tsunami waves (Szczuciński et al.,2006). For example settlements situated at river mouths experienced the worst damage during the last catastrophic events. The systematic inventory of morphometric properties according to a standardized GIS-approach based on digital elevation data and evaluations of satellite imageries from tsunami prone areas contribute considerably to the detection of areas, that are more susceptible to flooding due to their geomorphologic disposition. Traces of former flooding events can be detected on morphometric maps such as slope gradient maps or hillshade maps. Areas affected by extreme flooding events from the seaside often show characteristic properties such as fan-shaped abrasion planes or arc-shaped walls and terraces opened towards the sea.

It has been observed on many satellite imageries of recently from catastrophic tsunami waves affected countries such as in Indonesia, Japan or Chile, that the inundation extents were closely related to coastal areas below 5 - 10 m height level and to the aggregation of the in Fig.4 described morphometric properties. Creating contour lines indicating areas below 5 to 10 m height levels, merging these contour lines with high resolution satellite data or export these shape-files into Google Earth, contributes to the detection and visualization of those areas that might be affected in case of extreme flooding events.

Figure 4. Workflow of the weighted overlay for the detection of areas susceptible to flooding based on SRTM DEM data 3. CASE STUDIES The use of these methods is demonstrated by examples of Japan and Chile. 3.1. Earthquakes and Tsunami Event of 11.03.2011 in Japan According to the before described weighted-overlay approach a susceptibility-to-soil-amplification map was elaborated based on SRTM DEM and ASTER DEM data and further geodata to get an

overview of areas with relatively higher susceptibility to ground motion in Japan (Fig.5). Areas that are assumed to be more susceptible to amplify seismic signals due to the aggregation of causal factors are shown in dark-red colours.

Figure 5: Susceptibility to relatively higher soil amplification due to local site conditions. The next figures show combinations of the 11.03.2011-ShakeMap-calculations, recorded macroseismic observations related to this earthquake (USGS), lineament analysis and weighted overlay results. ShakeMap-calculations provided near-real-time maps of ground motion and shaking intensity following the Tohoku-earthquake and after-shock earthquakes (Fig.6 a). The Community Internet Intensity Map (CIIM, USGS) summarized the questionnaire responses provided by Internet users. In this case 1046 responses in 264 cities in Japan were provided by the USGS - Earthquake Hazard Program. The distribution pattern of macroseismic intensities recorded after the Tohoku-earthquake is not only related to the distance to the hypocentre of the earthquake as derived from attenuation calculations, it is clearly related to specific local site conditions as well. When comparing the results of the weighted overlay calculations based on SRTM DEM data with the macroseismic observations during the 11.03.2011-Magnitude 9- earthquake (USGS, Earthquake Hazards Program, access: 24.05.2012), a clearly coincidence of higher macroseismic intensity values with the dark-red areas corresponding to the aggregation of local site factors becomes visible. The macroseismic observations were interpolated (IDW- Inverse Distance Weighted-interpolation in ArcGIS) and merged with the weighted overlay results. Even in far distances from the hypocentre of about 600 km the influence of local site conditions can be visualized by Fig.6 b, showing macroseismic intensities of 7 according to the Modified Mercalli Intensity scale (MMI is composed of 12 increasing levels of intensity that range from imperceptible shaking to catastrophic destruction.), where only intensity values of about 3-4 were expected when using attenuation relationships of the ShakeMap-software. When merging the observed macroseismic intensities with the weighted overlay map according to the WOSAD-approach, a coincidence of the higher intensity values with higher susceptibility values within areas assumed to be exposed to higher ground motion due to the aggregation of causal factors can be realized.

Figure 6 a. ShakeMap of the Tohoku-earthquake based on shapefiles of the USGS, ShakeMap Archive, combined with macroseismic observations and the WOSAD-weighted overlay map http://earthquake.usgs.gov/earthquakes/shakemap/global/shake/c0001xgp/ http://earthquake.usgs.gov/earthquakes/dyfi/events/us/c0001xgp/us/index.html

Figure 6 b. Overlay of macroseismic observations with the WOSAD-weighted overlay-map showing a coincidence of higher susceptibility values within areas assumed to be exposed to higher ground motion due to the aggregation of causal factors with observed intensities during the earthquake Flood mapping, especially in coastal areas, is a demanding task requiring substantial data stocks. The need for reliable, but in general cost effective, risk mapping at the regional scale is rising, especially due to climate change follow-up processes such as sea-level rise or increasing storm surge events and flash floods. Tsunami risk assessment methods cannot rely only on modelling, as this would require more data and efforts that are not yet available in many potentially tsunami prone countries. This is especially the case in coastal urban areas, where modelling requires for example knowledge of the detailed urban terrain, the drainage networks, and their interactions. A flooding susceptibility map and a potential tsunami hazard map of coastal areas, that predicts the probable locations of possible future inundation and tsunami occurrences, is required which takes into consideration as well the potential morphodynamic consequences of these events at the coasts such as, abrasion, sedimentation and landslides. Undercutting the slopes at the coasts by abrasion and erosion those flood waves initiate a high potential of slope failure. In the case of a catastrophic event these maps can provide rescue teams with the map of the areas where the tsunami energy is expected to be destructively large and damage is most severe. According to the in Fig.4 described method a weighted overlay map was elaborated based on SRTM and ASTER DEM data showing those areas in Japan, where “negative” morphometric preparatory / causal factors occur aggregated (dark-blue colours in Fig.7) such as relatively lowest height levels (< 10 m), slope gradients < 10°, drop raster < 100.000, terrain curvature (values=0 – flat terrain), and high flow accumulation values, compared with evaluations of LANDSAT imageries and documented flooded areas (DLR, ZKI,2011). During the 11-03.2011 earthquake tsunami waves flooded low lands in many segments of the coast. The from ESRI, RapidEye AG, DLR, USGS and NOAA provided pre- and post-tsunami satellite imageries were evaluated. The past inundation events were mapped and overlapped with details of land cover. Fig.8 shows a comparison of the weighted overlay results (based on ASTER DEM) and the during the 11.03.2011-tsunami flooded areas on the Oshika peninsula, east of the city Ishinomaki. These flooded

areas were mapped based on ASTER, LANDSAT- and RapidEye- satellite imageries and on aerial images from the Geospatial Information Authority of Japan (GSI, implemented in ArcGIS-Online). Comparing the susceptibility results with the flooded areas during the 11.03.2011-tsunami, an overlap of the highest susceptibility values with the flooded areas is obvious. Those areas with the highest susceptibility-to-flooding values according to the weighted overlay results overlap nearly exactly with those areas flooded by tsunami waves. Thus, this weighted overlay approach seems to be a useful tool for the detection of areas prone to flooding due to the aggregation of morophometric properties, rising the susceptibility to flooding in case of storm surge, tsunami waves or flash floods. This approach could contribute to a basis data stock in a disaster management system in any coastal area. Another example is shown from the Sendai-region. The weighted overlay results were superimposed on land use information of the Sendai-area to show the past tsunami inundation impact on different land use classes (Figs. 9 and 10). Land use information of this area were derived from satellite image classifications, OpenStreetMap-shapefiles and own evaluations. Contour lines representing the different susceptibility values (ranging from 0 – 6), were drawn on the land use maps. Those buildings and infrastructural facilities situated in areas with higher susceptibility to flooding can be investigated in more detail as prerequisite for vulnerability inventory (Fig.10) in order to correlate building types with building stability for an earthquake and tsunami impact.

Figure 7. Weighted overlay of causal factors influencing the susceptibility to flooding by flash floods, storm surges and tsunami waves in the coastal areas of Japan (tsunami events: NOAA) 3.2. Earthquakes and Tsunami Events in the Valparaiso Area in Central Chile The same approach for the detection of areas susceptible to flooding was carried out in the area of Valparaiso in Central-Chile, that is prone to similar tsunami events as in Japan. The study area in Central-Chile is part of one of the longest coherent subduction zones. The convergent plate margin is produced by the movement between the oceanic Nazca plate and the continental South American plate. In the younger history there are 6 earthquakes related with the occurrence of tsunami waves in the Bay of Valparaiso:13.05.1647, 08.07.1730, 19.11.1822, 16.08.1906, 03.03.1985 and 27.02.2010 (Pararas-Carayannis,2010). During this century, the most important disaster was the 1960 earthquake and tsunami in Valdivia. Using the weighted overlay approach based on ASTER DEM data, geologic and bathymetric maps, areas susceptible to flooding can be visualized as well as the affected settlements

Figure 8. Detection of areas susceptible to flooding such by aggregating morphometric factors influencing flooding susceptibility

Figure 9. Comparison of the susceptibility calculation results with the flooding extent of the 11.03.2011-tsunami, aerial images: ArcGIS-Online /ESRI, land use data : OpenStreetMap and own evaluations

Figure 10. Assessment of buildings situated in areas with high susceptibility to flooding and types of infrastructure (Fig.11). RapidEye-satellite imageries show detailed water currents and streaming patterns (Fig.12), that contribute to a better understanding of the influence of coastal morphology on waves. 4. CONCLUSIONS Fast data acquisition and extraction of relevant information on the extent and impact of earthquakes and tsunami hazards are important issues for managing civil catastrophes in coastal areas. Remote sensing and GIS form an essential tool for getting actual infrastructural information as reference base for monitoring and maintenance or emergency planning and vulnerability assessment. The comparison of pre- and post-disaster imageries contributes to the documentation of damages. The current study focuses on specific tools and measures of remote sensing and GIS and their applicability for earthquake and tsunami awareness and preparedness. Effective adaptation and risk reduction need to be based on the expected impacts of the hazards and the vulnerabilities of communities and infrastructures exposed. Remote sensing is an independent available, area-wide and up-to-date data source, and thus indispensable.The presented weighted overlay approaches in ArcGIS successfully identifies the areas of susceptibility to higher earthquake shock and to flooding. This study showed the manifold capabilities of multi-source remote sensing to support decision making in the fields of risk and vulnerability assessment. ACKNOWLEDGEMENT The support of EU, FP 7, Large Collaborative Research Project, IRIS – Integrated European Industrial Risk Reduction System, CP-IP 213968-2, is kindly acknowledged. The authors thank the RapidEye AG, Brandenburg, Germany, for providing RapidEye satellite imageries from Central Chile and Japan. Especially the helpful and reliable support of Mrs.Petra Seiffert , RapidEye AG, has to be mentioned and thanked.

Figure 11. Susceptibility to flooding in Valparaiso, Central-Chile superimposed on a RapidEye-scene

Figure 12. RapidEye satellite scene visualizing the wave pattern

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Satellite Data: Global Land Cover Facility: http://glcfapp.glcf.umd.edu:8080/esdi/index.jsp USGS: http://earthexplorer.usgs.gov/ DLR, ZKI: http://www.zki.dlr.de/article/1893 RapidEye AG: http://www.rapideye.net/myrapideye/myre.php Digital Elevation Data: http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp http://glcfapp.glcf.umd.edu:8080/esdi/index.jsp http://www.gdem.aster.ersdac.or.jp/search.jsp Earthquake Data: USGS, Earthquake Hazards Program http://earthquake.usgs.gov/earthquakes/dyfi/events/us/c0001xgp/us/index.html

http://earthquake.usgs.gov/earthquakes/dyfi/events/us/c0001xgp/us/index.html NOAA/WDC Tsunami Event Database: http://maps.ngdc.noaa.gov/viewers/hazards/?layers=0 http://www.ngdc.noaa.gov/nndc/struts/form?t=101650&s=70&d=7 GIS-Shapefiles: OpenStreetMap: http://download.geofabrik.de/osm/ Fault Zones: http://www.j-shis.bosai.go.jp/map/JSHIS2/download.html?lang=en