352 research outputs found

    Operational tsunami modelling with TsunAWI – recent developments and applications

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    In this article, the tsunami model TsunAWI (Alfred Wegener Institute) and its application for hindcasts, inundation studies, and the operation of the tsunami scenario repository for the Indonesian tsunami early warning system are presented. TsunAWI was developed in the framework of the German-Indonesian Tsunami Early Warning System (GITEWS) and simulates all stages of a tsunami from the origin and the propagation in the ocean to the arrival at the coast and the inundation on land. It solves the non-linear shallow water equations on an unstructured finite element grid that allows to change the resolution seamlessly between a coarse grid in the deep ocean and a fine representation of coastal structures. During the GITEWS project and the following maintenance phase, TsunAWI and a framework of pre- and postprocessing routines was developed step by step to provide fast computation of enhanced model physics and to deliver high quality results

    Proceedings of the XXVIIIth TELEMAC User Conference 18-19 October 2022

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    Hydrodynamic

    Handbook on tsunami hazards and damage scenarios

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    The handbook is one of the products of the SCHEMA project (FP 6 Space priority) and has been conceived to illustrate the basic concepts and methods that have been elaborated and applied in the project to produce tsunami scenarios in view of providing tools to assess hazard and potential damage resulting from tsunamis. One of the main objectives was the elaboration of a general methodology that can be used in all possible cases and that can be adapted easily to the needs of the end users, i.e. chiefly the public administrators responsible for planning of the coastal zone development and protection strategies as well as people and organisations involved in disasters management and mitigation policies. It is for these reasons, that the SCHEMA methodology has been applied to five test sites (Rabat, Morocco; Setúbal, Portugal; Mandelieu, France; Catania, Italy; Balchik, Bulgaria) that differ very much from one another, so proving that it is suitable for a quite large variety of cases, and that it has been tested with the active involvement of the end users, so ensuring that it will provide practical and useful tools and it is flexible enough to cover local needs.JRC.DG.G.7-Traceability and vulnerability assessmen

    Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

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    The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

    Urban morphology analysis by remote sensing and gis technique, case study: Georgetown, Penang

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    This paper was analysed the potential of applications of satellite remote sensing to urban planning research in urban morphology. Urban morphology is the study of the form of human settlements and the process of their formation and transformation. It is an approach in designing urban form that considers both physical and spatial components of the urban structure. The study conducted in Georgetown, Penang purposely main to identify the evolution of urban morphology and the land use expansion. In addition, Penang is well known for its heritage character, especially in the city of Georgetown with more than 200 years of urban history. Four series of temporal satellite SPOT 5 J on year 2004, 2007, 2009 and 2014 have been used in detecting an expansion of land use development aided by ERDAS IMAGINE 2014. Three types of land uses have been classified namely build-up areas, un-built and water bodies show a good accuracy with achieved above 85%. The result shows the built-up area significantly increased due to the rapid development in urban areas. Simultaneously, this study provides an understanding and strengthening a relation between urban planning and remote sensing applications in creating sustainable and resilience of the city and future societies as well

    Tsunami Risk and Vulnerability

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    The research focuses on providing reliable spatial information in support of tsunami risk and vulnerability assessment within the framework of the German-Indonesian Tsunami Early Warning System (GITEWS) project. It contributes to three major components of the project: (1) the provision of spatial information on surface roughness as an important parameter for tsunami inundation modeling and hazard assessment; (2) the modeling of population distribution, which is an essential factor in tsunami vulnerability assessment and local disaster management activities; and (3) the settlement detection and classification from remote sensing radar imagery to support the population distribution research. Regarding the surface roughness determination, research analyses on surface roughness classes and their coefficients have been conducted. This included the development of remote sensing classification techniques to derive surface roughness classes, and integration of the thus derived spatial information on surface roughness conditions to tsunami inundation modeling. This research determined 12 classes of surface roughness and their respective coefficients based on analyses of published values. The developed method for surface roughness classification of remote sensing data considered density and neighborhood conditions, and resulted in more than 90% accuracy. The classification method consists of two steps: main land use classification and density and neighborhood analysis. First, the main land uses were defined and a classification was performed applying decision tree modeling. Texture parameters played an important role in increasing the classification accuracy. The density and neighborhood analysis further substantiated the classification result towards identifying surface roughness classes. Different classes such as residential areas and trees were combined to new surface roughness classes, as “residential areas with trees”. The density and neighborhood analysis led to an appropriate representation of real surface roughness conditions. This was used as an important input for tsunami inundation modeling. By using Tohoku University’s Analysis Model for Investigation Near-field Tsunami Number 3 (TUNAMI N3), the spatially distributed surface roughness information was integrated in tsunami inundation modeling and compared to the modeling results applying a uniform surface roughness condition. An uncertainty analysis of tsunami inundation modeling based on the variation of surface roughness coefficients in the Cilacap study area was also undertaken. It was demonstrated that the inundation modeling results applying uniform and spatially distributed surface roughness resulted in high differences of inundation lengths, especially in areas far from the coastline. This result showed the important role of surface roughness conditions in resisting tsunami flow, which must be considered in tsunami inundation modeling. With respect to the second research focus, the population distribution, a concept of population distribution modeling was developed. Within the modeling process, weighting factor determination, multi-scale disaggregation and a comparative study to other methods were conducted. The basis of the developed method was a combination of census and land use data, which led to an improved spatial resolution and accuracy of the population distribution. Socio-economic data were used to derive weighting factors to distributing people to land use classes. Moreover, in case of missing input data, an approach was developed that allows for the determination of generalized weighting factors. The approach to use specific weightings, where possible and generalized ones, where necessary, led to a flexible methodology with respect to the achievable accuracy and availability of data. A comparative study was performed by comparing this new model with previously developed population distribution models. The newly developed model showed a higher accuracy. The detailed population distribution information was a valuable input for the vulnerability assessment being the main data source for human exposure assessment and an important contribution to evacuation time modeling. In support of the population distribution research, settlement classification using TerraSAR-X imagery was conducted. A current classification method of speckle divergence analysis on SAR imagery was further developed and improved by including the neighborhood concept. The settlement classification provided highly accurate results in dense urban areas, whereas the method needs to be further developed and improved for rural settlement areas. Finally, it has been shown how the results of this research can be applied. These applications cover the integration of surface roughness conditions into the tsunami inundation modeling and hazard mapping. The contributions to tsunami vulnerability assessment and evacuation planning were shown. Additionally, the results were integrated into the decision support system of the Tsunami Early Warning Center in Jakarta
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