Suchergebnisse

Abstract. The republic of Georgia is a mountainous and tectonically active area that is vulnerable to landslides. Because landslides are one of the most devastating natural hazards, their detection and monitoring is of great importance. In this study we report on a previously unknown landslide in central Georgia near the town of Sachkhere. We used a set of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data to generate displacement maps using interferometric synthetic aperture radar (InSAR). We detected a sliding zone of dimensions 2 km north–south by 0.6 km east–west that threatens four villages. We estimated surface displacement of up to ∼30 cm/yr over the sliding body in the satellite line-of-sight (LOS) direction, with the largest displacement occurring after a local tectonic earthquake. We mapped the morphology of the landslide mass by aerial photography and field surveying. We found a complex set of interacting processes, including surface fracturing, shear and normal faults at both the headwall and the sides of the landslide, local landslide velocity changes, earthquake-induced velocity peaks, and loss in toe support due to mining activity. Important implications that are applicable elsewhere can be drawn from this study of coupled processes. We used inverse dislocation modelling to find a possible dislocation plane resembling the landslide basal décollement, and we used that plane to calculate the volume of the landslide. The results suggest a décollement at ∼120 m depth, dipping at ∼10° sub-parallel to the surface, which is indicative of a translational-type landslide.

Abstract. The aim of this study is to assess and map the seismic risk for Germany, restricted to the expected losses of damage to residential buildings. There are several earthquake prone regions in the country which have produced Mw magnitudes above 6 and up to 6.7 corresponding to observed ground shaking intensity up to VIII–IX (EMS-98). Combined with the fact that some of the earthquake prone areas are densely populated and highly industrialized and where therefore the hazard coincides with high concentration of exposed assets, the damaging implications from earthquakes must be taken seriously. In this study a methodology is presented and pursued to calculate the seismic risk from (1) intensity based probabilistic seismic hazard, (2) vulnerability composition models, which are based on the distribution of residential buildings of various structural types in representative communities and (3) the distribution of assets in terms of replacement costs for residential buildings. The estimates of the risk are treated as primary economic losses due to structural damage to residential buildings. The obtained results are presented as maps of the damage and risk distributions. For a probability level of 90% non-exceedence in 50 years (corresponding to a mean return period of 475 years) the mean damage ratio is up to 20% and the risk up to hundreds of millions of euro in the most endangered communities. The developed models have been calibrated with observed data from several damaging earthquakes in Germany and the nearby area in the past 30 years.

Abstract. Both aleatory and epistemic uncertainties associated with different sources and components of risk (hazard, exposure, vulnerability) are present at each step of seismic risk assessments. All individual sources of uncertainty contribute to the total uncertainty, which might be very high and, within the decision-making context, may therefore lead to either very conservative and expensive decisions or the perception of considerable risk. When anatomizing the structure of the total uncertainty, it is therefore important to propagate the different individual uncertainties through the computational chain and to quantify their contribution to the total value of risk. The present study analyses different uncertainties associated with the hazard, vulnerability and loss components by the use of logic trees. The emphasis is on the analysis of epistemic uncertainties, which represent the reducible part of the total uncertainty, including a sensitivity analysis of the resulting seismic risk assessments with regard to the different uncertainty sources. This investigation, being a part of the EU FP7 project MATRIX (New Multi-Hazard and Multi-Risk Assessment Methods for Europe), is carried out for the example of, and with reference to, the conditions of the city of Cologne, Germany, which is one of the MATRIX test cases. At the same time, this particular study does not aim to revise nor to refine the hazard and risk level for Cologne; it is rather to show how large are the existing uncertainties and how they can influence seismic risk estimates, especially in less well-studied areas, if hazard and risk models adapted from other regions are used.

At the end of October 2012, Hurricane Sandy moved from the Caribbean Sea into the Atlantic Ocean and entered the United States not far from New York. Along its track, Sandy caused more than 200 fatalities and severe losses in Jamaica, Bahamas, Haiti, Cuba, and the US. This paper demonstrates the capability and potential for near-real time analysis of catastrophes. It is shown that the impact of Sandy was driven by the superposition of different extremes (high wind speeds, storm surge, heavy precipitation) and by cascading effects. In particular the interaction between Sandy and an extra-tropical weather system created a huge storm that affected large areas in the US. It is examined how Sandy compares to historic hurricane events, both from a hydro-meteorological and impact perspective. The distribution of losses to different sectors of the economy is calculated with simple input-output models as well as government estimates. Direct economic losses are estimated about 4.2 billion US$ in the Caribbean and between 78 and 97 billion US$ for the US. Indirect economic losses from power outages is estimated in the order of 16.3 billion US$. Modelling sector-specific dependencies, quantifies total business interruption losses between 10.8 and 15.5 billion US$. Thus, seven years after the record impact of Hurricane Katrina in 2005, Hurricane Sandy is the second costliest hurricane in the history of the United States.

At the end of October 2012, Hurricane Sandy moved from the Caribbean Sea into the Atlantic Ocean and entered the United States not far from New York. Along its track, Sandy caused more than 200 fatalities and severe losses in Jamaica, The Bahamas, Haiti, Cuba, and the US. This paper demonstrates the capability and potential for near-real-time analysis of catastrophes. It is shown that the impact of Sandy was driven by the superposition of different extremes (high wind speeds, storm surge, heavy precipitation) and by cascading effects. In particular the interaction between Sandy and an extra-tropical weather system created a huge storm that affected large areas in the US. It is examined how Sandy compares to historic hurricane events, both from a hydro-meteorological and impact perspective. The distribution of losses to different sectors of the economy is calculated with simple input-output models as well as government estimates. Direct economic losses are estimated about USD 4.2 billion in the Caribbean and between USD 78 and 97 billion in the US. Indirect economic losses from power outages is estimated in the order of USD 16.3 billion. Modelling sector-specific dependencies quantifies total business interruption losses between USD 10.8 and 15.5 billion. Thus, seven years after the record impact of Hurricane Katrina in 2005, Hurricane Sandy is the second costliest hurricane in the history of the United States.

Abstract. At the end of October 2012, Hurricane Sandy moved from the Caribbean Sea into the Atlantic Ocean and entered the United States not far from New York. Along its track, Sandy caused more than 200 fatalities and severe losses in Jamaica, The Bahamas, Haiti, Cuba, and the US. This paper demonstrates the capability and potential for near-real-time analysis of catastrophes. It is shown that the impact of Sandy was driven by the superposition of different extremes (high wind speeds, storm surge, heavy precipitation) and by cascading effects. In particular the interaction between Sandy and an extra-tropical weather system created a huge storm that affected large areas in the US. It is examined how Sandy compares to historic hurricane events, both from a hydro-meteorological and impact perspective. The distribution of losses to different sectors of the economy is calculated with simple input-output models as well as government estimates. Direct economic losses are estimated about USD 4.2 billion in the Caribbean and between USD 78 and 97 billion in the US. Indirect economic losses from power outages is estimated in the order of USD 16.3 billion. Modelling sector-specific dependencies quantifies total business interruption losses between USD 10.8 and 15.5 billion. Thus, seven years after the record impact of Hurricane Katrina in 2005, Hurricane Sandy is the second costliest hurricane in the history of the United States.

At the end of October 2012, Hurricane Sandy moved from the Caribbean Sea into the Atlantic Ocean and entered the United States not far from New York. Along its track, Sandy caused more than 200 fatalities and severe losses in Jamaica, The Bahamas, Haiti, Cuba, and the US. This paper demonstrates the capability and potential for near-real-time analysis of catastrophes. It is shown that the impact of Sandy was driven by the superposition of different extremes (high wind speeds, storm surge, heavy precipitation) and by cascading effects. In particular the interaction between Sandy and an extra-tropical weather system created a huge storm that affected large areas in the US. It is examined how Sandy compares to historic hurricane events, both from a hydro-meteorological and impact perspective. The distribution of losses to different sectors of the economy is calculated with simple input-output models as well as government estimates. Direct economic losses are estimated about USD 4.2 billion in the Caribbean and between USD 78 and 97 billion in the US. Indirect economic losses from power outages is estimated in the order of USD 16.3 billion. Modelling sector-specific dependencies quantifies total business interruption losses between USD 10.8 and 15.5 billion. Thus, seven years after the record impact of Hurricane Katrina in 2005, Hurricane Sandy is the second costliest hurricane in the history of the United States.