Estimation of Economic Impacts of Climate-Driven Hazards Using Stochastic Process Model

Abstract

Projections using global climate models indicate that climate change will influence the patterns of natural hazards, such as thunderstorms, atmospheric river landfalls, extreme droughts, and ocean waves. The frequency and intensity of these hazards are expected to increase gradually in proportion to global temperature. The design principles based on the philosophy of cost optimization need to be updated to accommodate the nonstationarity of the load processes, primarily because the prevalent cost analysis methods in the literature predominantly assume that the loads are stationary. This study provides a novel methodology for calculating the first two moments and the distribution of the economic losses for nonstationary loading processes. Here, the load processes are modeled as a nonhomogeneous Poisson process (NHPP) with time-dependent rates. The presented methodology is applied to estimate the losses due to tornadoes in Ontario, Canada and heat waves in US cities. It was found that if adaptive measures are applied to increase the capacity of structures, the losses due to these climate-driven hazards can be significantly reduced. For example, if mitigation strategies are employed in Ontario, such that the effect of tornadoes with wind speeds lower than 50.3 m/s becomes negligible, then the tornado losses until 2100 can be reduced by 66%.