Abstract

This paper presents an overview of the structure of probabilistic catastrophe risk models, discusses their importance for appraising sovereign disaster risk financing and insurance instruments and strategy, and puts forward a model and a process for improving decision making on the linked disaster risk management strategy and sovereign disaster risk financing and insurance strategy. The paper discusses governments use of probabilistic catastrophe models to inform sovereign disaster risk financing decision making and describes the ex ante and ex post financing instruments available for responding to extreme natural events. It also discusses the challenge of appraising sovereign disaster risk financing and insurance instruments, including a review of the multiple dimensions of disaster risks and the value that probabilistic catastrophe risk models provide. The decision making framework for sovereign disaster risk financing and insurance put forward by the paper includes the use of a decision model (an influence diagram) as a rigorous representation of the relationships between the decisions, uncertain events, and consequences relevant to sovereign disaster risk financing and insurance decision making. The framework also includes a process for generating high-quality customized components for the decision model, and a tool for designing coherent sovereign disaster risk financing and insurance strategies. The paper ends with suggestions for improving catastrophe risk models to facilitate sovereign disaster risk financing and insurance decision making. Document type: Book

Abstract

occupied by oil pipelines and storage facilities are prone to severe contamination due to leaks caused by natural forces, poor maintenance or third parties. These threats have to be detected as quickly as possible in order to prevent serious environmental damage. Periodical and emergency monitoring activities need to be carried out for successful disaster management and pollution minimization. Airborne remote sensing stands out as an appropriate choice to operate either in an emergency or periodically. Hydrocarbon Index (HI) and Hydrocarbon Detection Index (HDI) utilize the unique absorption features of hydrocarbon based materials at SWIR spectral region. These band ratio based methods require no a priori knowledge of the reference spectrum and can be calculated in real time. This work introduces a flexible airborne pipeline monitoring system based on the online quasi-operational hyperspectral remote sensing system developed at Fraunhofer IOSB, utilizing HI and HDI for oil leak detection on the data acquired by an SWIR imaging sensor. Robustness of HI and HDI compared to state of the art detection algorithms is evaluated in an experimental setup using a synthetic dataset, which was prepared in a systematic way to simulate linear mixtures of selected background and oil spectra consisting of gradually decreasing percentages of oil content. Real airborne measurements in Ettlingen, Germany are used to gather background data while the crude oil spectrum was measured with a field spectrometer. The results indicate that the system can be utilized for online and offline monitoring activities.