Abstract
Abstract. Qiqihar is a significant city on the Nen River in China, which is the main stream of the Songhua River basin. The length of the return period of Qiqihar's flood control design standard is fifty years. If a 100-year flood event happened, Qiqihar would face the risk of a burst levee. To quantitatively evaluate flood risk to the city from a burst levee or proactive flood diversion, a model for analysing flood submergence from a burst levee in the City of Qiqihar is established based on MIKE Flood. The model integrates one- and two-dimensional hydrodynamic models to implement coupled simulation. The terrain data are from city elevation data on a scale of 1:10 000. Following local modifications made based on survey data, such as on levees, roads, and buildings, a 20 m × 20 m grid of terrain data was formed as the terrain input of the model. The model simulates the water level of Nen River and the flood path, submerged time/depth/area, and duration in floodplain under three scenarios: baseline, proactive downstream flood diversion, and an upstream levee burst under a flood with a one hundred-year return period. Proactive downstream flood diversion can reduce the maximum water level by 0.068 m and correspondingly decrease peak flood flow by 1120 m3 s−1. These results provide basic information to support urban flood risk analysis and flood dispatching and management across the whole river basin.
Highlights
Flood protection levees are a vitally important hydraulic structure; if a levee bursts, it will bring immeasurable losses
The urban flood simulation system is established based on the MIKE 11 and MIKE 21 hydrodynamic (HD) models
This paper has established an urban flood model based on MIKE Flood, which integrates MIKE 11 HD and MIKE 21 HD models
Summary
Flood protection levees are a vitally important hydraulic structure; if a levee bursts, it will bring immeasurable losses. There are two main types of models that simulate urban flooding: static and dynamic models. Static models focus on statistical representation of simulation results while dynamic models focus on the changes that take place during the whole flooding period (Siddiqui et al, 2011). Two-dimensional hydrodynamic models are the most widely used type of model, are based on exact physical significance, and can dynamically predict and simulate the flood process (Shirvan et al, 2013). Many researchers have applied numerical simulation techniques and proposed new perspectives and concepts (Soares-Frazão et al, 2008; Chen et al, 2009). Some researchers have drawn on the methods and techniques of other academic fields, such as neural networks (Kia et al, 2012) and cellular automata (Guidolin et al, 2016)
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