Storm surge risk assessment for the insurance system: A case study in Tokyo Bay, Japan

With the renewal of the basic disaster management plan in Japan, the transferring flood risk itself to insurance carriers is promoted by the Japanese government. An insurance company to which flood risks are transferred must assess infrequent flood risks to manage the overall risk of the company. Insurance systems may reach peak risk condition when a high wind coincides with a high storm surge level. However, there are few studies on the assessment of storm surges using stochastic approaches for Japan. This study suggests the procedure of stochastic storm surge risk assessment with low calculation load and applies it to the Tokyo Bay coast in Japan, a region exposed to large storm surges. Risk mapping and economic losses on the Tokyo Bay coast due to extreme flooding caused by storm surges are predicted for exceedance probability using stochastic approaches. Data on one thousand modeled typhoons passing through Tokyo Bay are extracted using a probabilistic tropical cyclone model over 10,000 years. Flooding due to storm surges is calculated with a validated numerical storm surge model. Next, a storm surge risk map that indicates inundation depth for a representative exceedance probability is created from the results of a series of stochastic hazard calculations. The inundation depth data sets for 1000 typhoons-along with the spatially distributed assets and values and damage functions provided by the Japanese government for Tokyo Bay-are used to estimate the economic loss from typhoons for corresponding exceedance probability. From the risk mapping, it was found that the storm surge risk in Chiba and Kanagawa prefectures is relatively high for exceedance probabilities and the economic losses estimated for the exceedance probability of 0.005 are 158.4 and 91.5 billion Japanese yen for the private and commercial sectors respectively along the Tokyo Bay coast. Moreover, the effect of the astronomical tide on the exceedance probability is evaluated using a neural network. However, it was found that the randomly determined astronomical tide does not have a significant influence on the exceedance probability targeted for insurance purposes when losses over 10,000 years are investigated.