New Japan inland flood model launched
Catastrophe modelling firm AIR Worldwide has introduced a new inland flood model for Japan, which it believes is the first of its kind in the sense that it is able to factor in all types of precipitation, Milan Simic, the firm’s executive vice president and managing director, international operations, told SIRC Today.
The company has also made enhancements to its Japan typhoon model. These models are part of Version 5.1 of AIR’s catastrophe modelling software platform, Touchstone.
“To the best of our knowledge, this is the first model of its kind—a model that considers all types of precipitation (tropical cyclone and non-tropical cyclone) and is able to propagate the water on the ground through the stream and river network using the same detailed and rigorous calculations, while simultaneously taking into account all the associated flood defences,” Simic said.
“It can then combine the tropical cyclone precipitation-induced losses with wind losses to present overall typhoon losses.”
Jayanta Guin, chief research officer at AIR Worldwide, added: “Until now, the industry has lacked the tools to quantify inland flood risk in Japan effectively. We’re excited to add Japan to our growing portfolio of high-resolution flood models.
“The model represents another step forward in helping the industry manage this complex risk by better understanding the severity, frequency, and loss potential of extreme flood events in Japan.”
The AIR Inland Flood Model for Japan simulates the effects of on- and off-plain inland flooding from sources other than tropical cyclones. Non-tropical systems that trigger inland flooding account for 40 percent of flood losses in Japan. The new probabilistic model captures the complexities of the flood peril in an area of more than 377,000 square kilometres divided into more than 20,000 unit catchments for a river network exceeding 100,000 km in length—and takes flood defences into consideration.
The model was calibrated and validated using observational data based on river flows at nearly 1,000 locations, as well as lake and dam storage data from more than 2,000 locations.
The model generates flood events by way of a large-scale hydrological model that simulates continuous surface runoff and river flows using a method that learns the patterns of precipitation from observed tropical cyclone and non-tropical cyclone systems.
The model takes into account river shape, dams, levees, and other structures when computing discharge at each location along the river network. Pre-existing conditions—including lake and dam storage and soil saturation—that can exacerbate flood conditions are explicitly accounted for.
The new inland flood model can be used with AIR’s Japan typhoon model to develop a complete understanding of flood risk for Japan. This model, updated to include a completely new typhoon-induced precipitation module, also captures the risk from wind and storm surge.
Both Japan models are released in Touchstone 5.1, which also features other enhancements to the geospatial module.
The update also includes new mapping tools to provide more robust geographic information system (GIS) functionality and support for a wider variety of data formats that allow users to import GIS data from many more external sources directly into the geospatial module. Expanding the ability to bring data into Touchstone from third party sources, including the National Oceanic and Atmospheric Administration (NOAA), Federal Emergency Management Agency (FEMA) and other GIS data providers, enables users to better visualise and understand their risk.
“Touchstone is fast becoming the industry’s preferred catastrophe modelling application, and now, on our fifth full release, we’re excited to bring new enhancements to the usability and functionality of the software,” said Rob Newbold, executive vice president at AIR Worldwide. “These latest updates will continue to simplify companies’ risk management workflows while also helping them develop their own view of risk in one easy-to-use, comprehensive risk management platform.”
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