Toward near real-time flood loss estimation: Post-disaster index

The increase in the frequency and impact of extreme hydro-meteorological events worldwide highlights the need for more effective financial strategies providing coverage against the economic consequences of such events, particularly in developing countries. Near Real-Time Loss Estimation (NRTLE) models represent a new generation of catastrophe risk models that can serve as a basis for the development of innovative parametric insurance schemes. NRTLE models can help to estimate the impact of an extreme event, in near real time, for instance, through a Post-Disaster Index (PDI), upon which the issued payments depend. This study introduces a new methodology to compute such an index for flood events in the Philippines, which relies on satellite precipitation estimates, exposure information provided by national censuses issued by the Philippine Statistics Authority (PSA), and historic loss data from the EM-DAT International Disaster Loss database. Firstly, the risk model components (hazard, exposure and vulnerability) employed to generate the above index are described. Then, model performance in terms of number of affected residential buildings, estimated by means of the suggested PDI, is analyzed. Finally, an example of parametric insurance coverage based upon the designed PDI is illustrated

Satellite precipitation–based extreme event detection for flood index insurance

This paper introduces a novel Satellite Precipitation-based Extreme Event Detection (SPEED) model to effectively support parametric or index insurance instruments specifically designed to cover flood risk. Such financial tools are intended to promote fast payouts in the aftermath of a disastrous event. They leverage on measured hazard parameters, gathered immediately after the event and defined as the trigger(s), which are used to identify such hazardous events and estimate their resulting consequences in terms of physical damage and losses. This paper addresses the first step of such a modeling approach, the detection of a flood event, which plays an important role in the overall methodology, determining its performance in terms of false and missed detections. Different types of triggers for identifying flood events, based on satellite precipitation estimates, are investigated, and the overall model performance is assessed for a case-study country (the Philippines). A statistical procedure for selecting the optimal configuration of model parameters is presented. Such an optimal configuration minimizes the so-called basis risk, defined in this study as the mismatch between modeled and actual events. Finally, the accuracy of the proposed approach in terms of event localization is investigated by subdividing the case-study country into three main areas, corresponding to the coarsest administrative levels, and assessing the model's capability to capture events in each considered area. The results from this study confirm that the proposed SPEED model can be effectively used as an input for parametric insurance products, given its ability to identify hazardous events correctly

Toward near real-time flood loss estimation: Model structure and event definition

Near Real-Time Loss Estimation Models (NRTLEMs) represent effective tools for developing improved parametric insurance products. This type of financial instruments enables rapid payments as they use one or more environmental variables measured immediately after the event and defined as trigger(s), to identify disaster events and predict the consequent impact. This study presents the preliminary development of such a NRTLEM, specific for floods. Given the importance of the event identification within the proposed methodology, different types of triggers are investigated and compared, with special focus on satellite precipitations estimates. NRTLE-based framework for identification of flood events in the Philippines using satellite precipitation estimates is investigated here. The methodology for event identification and the model calibration procedure are discussed. Finally, the model performance is investigated and the optimal configuration of model parameters minimizing basis risk, i.e., the mismatch between insurance claim settlement and the actual losses, is presented for the case-study application

PHERMEX electron gun development

The PHERMEX facility is a 50-MHz standing-wave linear accelerator. Electrons are injected, accelerated, and transported to a tungsten target where bremsstrahlung x rays are generated for flash radiography of hydrodynamic systems. The purpose of this article is to describe the progress of PHERMEX electron gun development. The goal of this program is to generate and transport a 200-ns, 1-MV, 1-kA electron beam into the first PHERMEX accelerating cavity. The standard gun is operated at a pulse voltage of 550 kV, which is the limit determined by internal breakdown of the vacuum insulator. This insulator has been redesigned, and the gun has been pulsed at 750 kV without internal breakdown. At present, the current output is not limited by voltage but by a phenomenon called pulse shortening, which occurs at a pulse voltage of approximately 650 kV. The phenomenon has been investigated and the results are presented