Other noninsurance data (for example, topographic, geographic information system, and tax roll) can augment both exposure and claim data. In addition to the exposure data, an insurance company will also have claim data from past catastrophic events. The cat model filters the insurance exposure through its library of building classes, assigns vulnerability functions to each building class identified in the exposure, and applies hazard data, to produce the insured losses. For an insurance risk model, an insurance company will provide exposure data, which is the input to the cat model. Figure 1 represents the interplay between the different types of data in a cat model, circled in red. These probabilistic natural hazard catastrophe (cat) models are the product of complex simulations, sometimes coupled with regression analyses, and involve many stochastic variables and sources of data. Some notable examples include MH-HAZUS (Schneider and Schauer 2006 FEMA 2015), and ShakeCast (Wald et al. In many cases, these models are in the public domain and can even be open source code. In this case, the input can be databases of building or other infrastructure exposure from tax rolls or other sources, and the outputs are physical or monetary damage. 2017), where the focus is on emergency planning, post-disaster recovery, and increasingly on resilience studies (Muir-Wood and Stander 2016). 2013), as well as disaster managers and city and emergency planners (Chian 2016 Biasi et al. The other user group of cat models includes economists (Michel-Kerjan et al. A notable exception is the Florida Public Hurricane Loss Model (FPHLM 2019). Most of the cat models addressing the needs of the insurance industry are proprietary models from companies such as Risk Management Solutions ( 2019) and others. In this case, insurance portfolios are the input to the models, and the outputs are insured losses. One group includes the insurance industry and insurance regulators (Dong 2002 Shah et al. Cat models address the primary needs of different user groups. Lessons learned should be of interest to professionals involved in disaster risk assessment and management.Ĭatastrophe (cat) models for man-made infrastructure have four main components: a hazard component, which models the hazards, for example, hurricane or earthquake an exposure model, which categorizes the exposure, for example, buildings, into generic classes a vulnerability component, which models the effects of the hazard on the exposure and defines vulnerability functions for each building (or other type of exposure) class and an actuarial component, which combines the vulnerability, the hazard, and the exposure, to quantify the risk in terms of physical damage, economic damage, or insured losses. The article describes the impact of these uncertainty reductions on the development and validation of the vulnerability models, and suggests avenues for data improvement. These efforts produced an integrated and more complete set of building descriptors for each policy in the NFIP and wind portfolios. The FPHLM hazard teams assigned estimates of natural hazard intensity measure to each insurance claim. The data from these different sources were reformatted and processed, and the insurance databases were separately cross-referenced at the county level with tax appraiser databases. To define the input exposure, and for model development, calibration, and validation purposes, the FPHLM teams accessed three main sources of data: county tax appraiser databases, National Flood Insurance Protection (NFIP) portfolios, and wind insurance portfolios. The challenges are illustrated through the Florida Public Hurricane Loss Model (FPHLM), which estimates insured losses on residential buildings caused by hurricane events in Florida. This article identifies the different sources of epistemic uncertainty in the data, and elaborates on strategies to reduce this uncertainty, in particular through identification, augmentation, and integration of the different types of data.
The poor quality of the data is a source of epistemic uncertainty, which affects the vulnerability models as well as the output of the catastrophe models. Each of these areas requires diverse sources of data, which are very often incomplete, inconsistent, or missing altogether. Catastrophe models estimate risk at the intersection of hazard, exposure, and vulnerability.
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