Abstract
The objective of this paper was to project possible impacts of climate change on heavy rainfall-related water damage insurance claims and incurred losses for four selected cites (Kitchener-Waterloo, London, Ottawa, and Toronto) located at Ontario, Canada. To achieve this goal, the future climate change scenarios and rainfall simulations, at local scale, were needed. A statistical downscaling method was used to downscale five global climate model (GCM) scenarios to selected weather stations. The downscaled meteorological variables included surface and upper-air hourly temperature, dew point, west-east and south-north winds, air pressure, and total cloud cover. These variables are necessary to project future daily rainfall quantities using within-weather-type rainfall simulation models. A model result verification process has been built into the whole exercise, including rainfall simulation modeling and the development of downscaling transfer functions. The results of the verification, based on historical observations of the outcome variables simulated by the models, showed a very good agreement. To effectively evaluate heavy rainfall-related water damage insurance claims and incurred losses, a rainfall index was developed considering rainfall intensity and duration. The index was evaluated to link with insurance data as to determination of a critical threshold of the rainfall index for triggering high numbers of rainfall-related water damage insurance claims and incurred losses. The relationship between rainfall index and insurance data was used with future rainfall simulations to project changes in future heavy rainfall-related sewer flood risks in terms of water damage insurance claims and incurred losses. The modeled results showed that, averaged over the five GCM scenarios and across the study area, both the monthly total number of rainfall-related water damage claims and incurred losses could increase by about 13%, 20% and 30% for the periods 2016-2035, 2046-2065, and 2081-2100, respectively (from the four-city seasonal average of 12 ± 1.7 thousand claims and $88 ± $21 million during April-September 1992-2002). Within the context of this study, increases in the future number of insurance claims and incurred losses in the study area are driven by only increases in future heavy rainfall events.
Highlights
Increased risks of flooding from heavy rainfall events are recognized in many regions of the world because of the most important threat from climate change
The Province of Ontario, Canada could in the future possibly receive more heavy rainfall-related water damage insurance claims and incurred losses than ones are currently projected by this study
The models were evaluated using downscaled global climate model (GCM) historical runs to ascertain whether the models are suitable for projecting the number of future rainfall-related water damage insurance claims and incurred losses
Summary
Increased risks of flooding from heavy rainfall events are recognized in many regions of the world because of the most important threat from climate change. Such a changing climate could increase the flood disaster economic losses in the future In light of this concern locally in southern Ontario, Canada, this study was designed to project possible changes in the frequency and intensity of heavy rainfall-related flooding risks late this century for four selected cities in Ontario. The simulation models were developed by considering physical process of rainfall formation with combining theories from both conceptual and statistical modeling These studies [14,15] attempted to project possible changes in the frequency and intensity of daily rainfall events late this century for the four selected watersheds (Grand, Humber, Rideau, and Upper Thames as shown in Figure 2) in southern Ontario, Canada. Cheng et al [15] concluded that the methods used in the study are suitable for projecting future station-scale daily rainfall information since data distributions of daily rainfall from both downscaled GCM historical runs and observations over the same time period (1961-2000) in the selected river basins are very similar
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