Research on disaster thresholds and quantitative risk evaluation of torrential flood in Inner Mongolia

In this study, taking the Banzhijian gully in Chifeng City as an example and using German two-dimensional hydrodynamic model called FloodArea, generalized extreme value distribution function, scenario simulation evaluation method and statistical correlation analysis, we performed the submerged simulation of five rainstorm events occurred in Banzhijian gully based on meteorological data, torrential flood cases and basic geographic information, etc. The disaster thresholds of different torrential flood levels are calculated by establishing the regression relationship between the submerged depth and the surface rainfall, and the possible impacts of torrential flood disasters on population and GDP under different return periods were quantitatively evaluated as well. The results showed that:(1) The FloodArea model has a good ability of submerged simulation for the Banzhijian gully, which can be apply to the disaster thresholds determination and quantitative risk assessment of the gully without hydrological data in Inner Mongolia. (2) The time-limitation of the Banzhijian gully is 4 h, and the disaster thresholds of the level 1-4 torrential flood are respectively 58.5 mm, 48.9 mm, 39.3 mm and 29.7 mm. (3) The population and GDP affected by the Banzhijian gully decrease with the increase of submerged depth, among which the largest impact was 0.1-0.6 m. (4) The population and GDP affected by torrential flood in the T years increase with the increase of recurrence period when the submerged depth is 0.6-1.8 m and greater than 0.6 m, respectively. When the submerged depth is greater than 1.8 m, the population affected by torrential flood once every 5 years is the smallest, while the population affected once every 100 years is the largest. When the submerged depth is between 0.1 and 0.6 m, the GDP affected by torrential flood once in 100 years is the smallest, while the GDP affected by torrential flood once in 30 years is the largest.