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

Abstract

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.