方法对比

	Catastrophe Risk Modeling	Exposure Modeling (Disaster Risk)
领域	Disaster Studies	Disaster Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2005	2017
提出者	Patricia Grossi & Howard Kunreuther; Kirsten Mitchell-Wallace et al.	Catalina Yepes-Estrada & Vitor Silva (GEM); GEM Foundation global exposure program
类型	Event-based stochastic loss-simulation pipeline	Spatial-inventory construction pipeline for elements at risk
开创性文献	Mitchell-Wallace, K., Jones, M., Hillier, J., & Foote, M. (Eds.) (2017). Natural Catastrophe Risk Management and Modelling: A Practitioner's Guide. Wiley-Blackwell. ISBN: 9781118906040	Yepes-Estrada, C., Silva, V., Valcárcel, J., Acevedo, A. B., Tarque, N., Hube, M. A., Coronel, G., & Santa María, H. (2017). Modeling the Residential Building Inventory in South America for Seismic Risk Assessment. Earthquake Spectra, 33(1), 299-322.
别名	Cat Modeling, Catastrophe Loss Modeling, Natural Catastrophe Modelling, Event-Based Loss Modeling	Exposure Database Development, Asset Inventory Modeling, Building Exposure Model, Elements at Risk Mapping
相关	4	4
摘要	Catastrophe risk modeling estimates the probability distribution of losses from natural perils, such as hurricanes, earthquakes, and floods, by simulating large stochastic sets of plausible events and pushing each through hazard, exposure, vulnerability, and financial modules. It exists because catastrophe losses are rare, severe, and spatially correlated, so historical loss data alone cannot reveal the tail risk that insurers and governments must plan for; instead the model synthesizes thousands of years of possible events. Patricia Grossi and Howard Kunreuther's 2005 volume systematized the four-module structure and its use in managing risk, while Kirsten Mitchell-Wallace and colleagues' 2017 practitioner's guide is the standard modern reference for how the industry builds and uses these models. The defining output is the loss exceedance curve, from which average annual loss, return-period losses, and probable maximum loss are read. Catastrophe models are the engine of property catastrophe insurance, reinsurance pricing, and increasingly public disaster-risk finance. They turn the physics of rare hazards into the financial metrics needed to price and transfer extreme risk.	Exposure modeling builds the geolocated inventory of assets, people, and values that are at risk from a hazard, the elements-at-risk layer that, together with hazard and vulnerability, determines disaster loss. It answers what is where and worth how much: how many buildings of each construction type sit in each location, their replacement value, and the population that occupies them at different times of day. Catalina Yepes-Estrada, Vitor Silva, and colleagues' 2017 South America residential exposure model and Vitor Silva and colleagues' 2020 global seismic risk model exemplify the modern approach of synthesizing census statistics, building characteristics, and expert mapping into open, georeferenced databases. Because loss equals hazard acting on exposure through vulnerability, exposure accuracy often dominates the realism of a risk estimate. Exposure models feed catastrophe models, HAZUS-style loss estimation, and probabilistic risk metrics like average annual loss. Constructing them well, with consistent taxonomy, credible values, and validated counts, is foundational to all downstream disaster risk analysis.