Improving urban flood resilience - Tiago Souza Mattos (2020)
Flooding in urban areas due to extreme stormwater in short time has caused material, economic, environmental and human losses in several places worldwide. The combination of climate change and increasing urbanization brings great challenges to planning and managing cities, because are considered the main responsible for increasing the severe flooding risks in urban areas. Therefore, it is necessary improving the urban flood resilience. Then, the main objective of the study presented in this doctoral thesis was to evaluate and develop techniques to improving the urban flood resilience. To achieve that, in the chapter 1, it was evaluated how Low Impact Development (LID) practices affect the resilience of stormwater drainage system under climate change scenarios. The resilience of the drainage system was quantified by means of a resilience index. The results indicate that the increased runoff peak can be mitigated satisfactory by using combined LID practices. In general, LID combinations showed reduction in runoff peak higher than 20%, and the best LID combination presented reduction up to 46%. The Weather radar data is useful for rainfall-runoff models used in urban flooding studies. Then, In the chapter 2, we developed a new bias correction approach based on Cumulative Distribution Function (CDF) matching method that focuses to correct biased radar rainfall estimates on daily, hour and sub-hour basis. The results showed that Nash-Sutcliffe Efficiency (NSE) index increased from 0.11 to 0.63 and Mean Absolute Error (MAE) decreased from 11.66 (biased data) to 6.97 mm (unbiased data) for all rainfall events. These results indicate that there was a significant improvement on the radar rainfall estimates. Additionally, in the chapter 3, a Decision Support System coupled with a mobile application is used to develop a flood alert system. The application based on Progressive Web Application was developed to support the visualization of flood status and to deliver early warning messages to population. The results found in this doctoral thesis is an essential information to decision making by the public authorities as well as to population for improving the urban flood resilience.