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
