Risk Assessment of Aggregate Loss by Storm Surge Inundation in Ise and Mikawa Bay

Abstract

In the event of disaster, the risk of disaster are intertwined, and there is an occurrence possibility of simultaneous damage in multiple areas. Nationwide companies have more risks of simultaneous damage in multiple areas by one disaster. For example, factories in Osaka and in Nagoya, can be damaged by one typhoon. In this case, company will need more money when damage happened and better to make special insurance contract, e.g. Catastrophe bond. On the other hand, insurance company has to assess amount of insurance payout because to pay it for contracted companies quickly. Insurance company may have difficulty to estimate total amount since there are few researches assessing aggregate loss caused by coastal disasters. This research proposes a procedure of assessment of aggregate loss by storm surges in Ise and Mikawa Bay located in Aichi prefecture, Japan.

LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations

“…LISFLOOD-FP is a raster-based open-source hydrodynamic modelling framework that has been applied in many fields of earth sciences, including morphodynamic modelling (Coulthard et al, 2013;Ziliani et al, 2020), urban drainage modelling (Wu et al, 2018;Yang et al, 2022), population mapping (Zhu et al, 2020), coastal flooding (Hirai and Yasuda, 2018;Seenath, 2018), uncertainty quantification (Liu and Merwade, 2018;Beevers et al, 2020;Jafarzadegan et al, 2021;Karamouz and Mahani, 2021;Yin et al, 2022;Zeng et al, 2022) and coupled hydrological-hydraulic modelling (Siqueira et al, 2018;Towner et al, 2019;Rajib et al, 2020;Makungu and Hughes, 2021;Nandi and Reddy, 2022). It has undergone extensive developments and testing since its conception (e.g.…”

LISFLOOD-FP 8.1: New GPU accelerated solvers for faster fluvial/pluvial flood simulations

“…LISFLOOD-FP is a raster-based open-source hydrodynamic modelling framework that has been applied in many fields of 25 earth sciences, including morphodynamic modelling (Coulthard et al, 2013;Ziliani et al, 2020), urban drainage modelling (Wu et al, 2018;Yang et al, 2022), population mapping (Zhu et al, 2020), coastal flooding (Hirai and Yasuda, 2018;Seenath, 2018;Irwan et al, 2021), uncertainty quantification (Liu and Merwade, 2018;Beevers et al, 2020;Jafarzadegan et al, 2021;Karamouz and Mahani, 2021;Yin et al, 2022;Zeng et al, 2022) and coupled hydrological-hydraulic modelling (Siqueira et al, 2018;Towner et al, 2019;Hoch et al, 2019;Rajib et al, 2020;Makungu and Hughes, 2021;Nandi et al, 30 2022). It has undergone extensive developments and testing since its conception (e.g., Bates and De Roo, 2000;Hunter et al, 2005;Bates et al, 2010;Sosa et al, 2020;Shustikova et al, 2020;Zhao et al, 2020), becoming a state-of-the-art tool for flood modelling applications at urban, catchment, regional and continental scales (e.g., Amarnath et al, 2015;Chaabani et al, 2018;O'Loughlin et al, 2020;Zare et al, 2021;Bessar et al, 2021;Chone et al, 2021;Asinya et al, 2021;Zhao et al, 2021).…”