A flood simulation model of Ji'nan City was constructed based on the TELEMAC-2D model, and the inundation data of a typical rainstorm of "20070718"in Ji'nan City were selected to validate the model. The results show that the error of simulated water depth is small, indicating that the constructed urban flood simulation model has high accuracy and reliability in flood simulation. The flooding evolution processes under different rainfall return periods were simulated, and the submerged depth, inundated area, and flood velocity in the study area were analyzed. The simulation results show that, with the increase of the rainfall return period, the ratio of area with water depth greater than 0.15 m to the total area also increases. For the rainfall return period of 1, 5, 10, 20, 50, and 100 years, the area ratios were 0.05%, 1.10%, 1.98%, 2.89%, 4.28%, and 5.15%, respectively, and the maximum flood velocities were 0.478, 1.019, 1.309, 1.494, 1.890, and 2.214 m/s, respectively. Based on the characteristics of flood inundation, engineering and no-nengineering measures for urban flood prevention and drainage in Ji'nan City were proposed.

Flood loss assessments are an important part of flood management for disaster prevention and mitigation. Most previous research on flood losses has been based on static assessments without high-resolution GDP spatial distribution data with only single industry-related loss assessment objectives. This study simulated urban flooding for a design storm scenario to obtain high-resolution spatial distribution GDP based on land use data, point of interest (POI) data and a random forest regression model. The Arcpy library in Python was used to superimpose the urban flood areas on spatial topology social economic data to predict the direct economic losses due to the flood. An input-output model was used to quantify the indirect economic losses caused by inter-industry linkages. The Qianshan River Basin between Zhongshan and Zhuhai cities in Guangdong Province, China was used as an example to predict the direct economic losses caused by a 50 year design rainstorm and the corresponding industry-related economic losses. The results show differences in the flood economic losses for different industries. The time with greatest flood volume and inundation depth do not necessarily correspond to the time with greatest economic losses. The indirect economic losses account for about 47% of the flood losses and the indirect economic losses of some industries even exceed the direct economic losses, which should receive more attention. This study improves flood loss assessment methods and provides technical support for disaster prevention and mitigation decision-making and industrial structural changes after flooding.

Pluvial floods can inundate urban road networks which then disrupts traffic flows and public services; thereby, increasing emergency response times. This study used the TELEMAC-2D model to simulate flooding of the Qianshanhe catchment for 50 and 100 year design rainstorms to analyze the response times of hospital ambulances, fire station emergency vehicles and police vehicles in the Qianshanhe catchment for various flooding scenarios. The results show that the average ambulance response time without flooding is 19 min, the average emergency vehicle response time is 24 min and the average police vehicle response time is 15.8 min. The 50 year flood scenario will flood some of the roads which will reduce the average ambulance response time to 133.7 min, the average emergency vehicle response time to 241.8 min and the average police vehicle response time to 201 min, which are much longer than the response times without flooding. The 100 year scenario will flood most roads which will reduce the average ambulance response time to 220.1 min, the average emergency vehicle response time to 366 min and the average police vehicle response time to 304 min. Only areas near the hospitals, fire stations or police stations will get rapid responses, while other areas will get very slow responses. These results show that flooding will significantly affect emergency response times and that roads need to be improved to avoid greater losses. The TELEMAC-2D model is very useful for analyzing the effects of flooding and the emergency response capabilities for urban flooding for urban emergency management.