No abstract

In the framework of the German Indonesian Tsunami Early Warning System (GITEWS) the assessment of tsunami risk is an essential part of the overall activities. The scientific and technical approach for the tsunami risk assessment has been developed and the results are implemented in the national Indonesian Tsunami Warning Centre and are provided to the national and regional disaster management and spatial planning institutions in Indonesia. <br><br> The paper explains the underlying concepts and applied methods and shows some of the results achieved in the GITEWS project (Rudloff et al., 2009). The tsunami risk assessment has been performed at an overview scale at sub-national level covering the coastal areas of southern Sumatra, Java and Bali and also on a detailed scale in three pilot areas. The results are provided as thematic maps and GIS information layers for the national and regional planning institutions. From the analyses key parameters of tsunami risk are derived, which are integrated and stored in the decision support system of the national Indonesian Early Warning Centre. Moreover, technical descriptions and guidelines were elaborated to explain the developed approach, to allow future updates of the results and the further development of the methodologies, and to enable the local authorities to conduct tsunami risk assessment by using their own resources

An innovative newly developed modular and standards based Decision Support System (DSS) is presented which forms part of the German Indonesian Tsunami Early Warning System (GITEWS). The GITEWS project stems from the effort to implement an effective and efficient Tsunami Early Warning and Mitigation System for the coast of Indonesia facing the Sunda Arc along the islands of Sumatra, Java and Bali. The geological setting along an active continental margin which is very close to densely populated areas is a particularly difficult one to cope with, because potential tsunamis' travel times are thus inherently short. National policies require an initial warning to be issued within the first five minutes after an earthquake has occurred. There is an urgent requirement for an end-to-end solution where the decision support takes the entire warning chain into account. The system of choice is based on pre-computed scenario simulations and rule-based decision support which is delivered to the decision maker through a sophisticated graphical user interface (GUI) using information fusion and fast information aggregation to create situational awareness in the shortest time possible. The system also contains risk and vulnerability information which was designed with the far end of the warning chain in mind – it enables the decision maker to base his acceptance (or refusal) of the supported decision also on regionally differentiated risk and vulnerability information (see Strunz et al., 2010). While the system strives to provide a warning as quickly as possible, it is not in its proper responsibility to send and disseminate the warning to the recipients. The DSS only broadcasts its messages to a dissemination system (and possibly any other dissemination system) which is operated under the responsibility of BMKG – the meteorological, climatological and geophysical service of Indonesia – which also hosts the tsunami early warning center. The system is to be seen as one step towards the development of a "system of systems" enabling all countries around the Indian Ocean to have such early warning systems in place. It is within the responsibility of the UNESCO Intergovernmental Oceonographic Commission (IOC) and in particular its Intergovernmental Coordinating Group (ICG) to coordinate and give recommendations for such a development. Therefore the Decision Support System presented here is designed to be modular, extensible and interoperable (Raape et al., 2010)

Abstract. More than 4 million Indonesians live in tsunamiprone areas along the southern and western coasts of Sumatra, Java and Bali. Although a Tsunami Early Warning Center in Jakarta now exists, installed after the devastating 2004 tsunami, it is essential to develop tsunami risk knowledge within the exposed communities as a basis for tsunami disaster management. These communities need to implement risk reduction strategies to mitigate potential consequences.The major aims of this paper are to present a risk assessment methodology which (1) identifies areas of high tsunami risk in terms of potential loss of life, (2) bridges the gaps between research and practical application, and (3) can be implemented at community level. High risk areas have a great need for action to improve people's response capabilities towards a disaster, thus reducing the risk. The methodology developed here is based on a GIS approach and combines hazard probability, hazard intensity, population density and people's response capability to assess the risk.Within the framework of the GITEWS (GermanIndonesian Tsunami Early Warning System) project, the methodology was applied to three pilot areas, one of which is southern Bali. Bali's tourism is concentrated for a great part in the communities of Kuta, Legian and Seminyak. Here alone, about 20 000 people live in high and very high tsunami risk areas. The development of risk reduction strategies is therefore of significant interest. A risk map produced for the study area in Bali can be used for local planning activities and the development of risk reduction strategies.

The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatio-temporal variability of elements at risk, detailed information is costly both in terms of time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very high resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed, however, the design of information extraction procedures with a high level of automatisation remains challenging. In this paper, we uniquely combine remote sensing data and Volunteered Geographic Information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in