Study on the Classification of Urban Waterlogging Rainstorms and Rainfall Thresholds in Cities Lacking Actual Data

Abstract

Extreme rainfall is the main influencing factor of urban waterlogging. Different types of rainfall often have different characteristics of waterlogging. In order to establish a more accurate urban flood control system, it is necessary to classify waterlogging rainstorms and divide their thresholds. This study proposes a method for applying web crawlers to identify waterlogging rainfall in cities lacking waterlogging observation data and classifying them using the rainfall intensity–duration curves. By selecting appropriate duration thresholds and return period, waterlogging rainstorms are divided into rainfall intensity waterlogging (IW), rainfall amount of waterlogging (AW), combined waterlogging (CW) and no waterlogging (NW). In the application of Zhengzhou City, China, the urban flood control standard and the rainfall time distribution characteristics are used as the basis for the selection of the return period and duration thresholds, and the storm water management model (SWMM) is constructed to simulate the 4 kinds of rainfall characteristics of waterlogging, which is similar to actual situations. It proves that the method is suitable for the classification and thresholds division of different waterlogging rainfall in cities. The results show that the best duration thresholds in Zhengzhou are 20 min (M20) and 60 min (M60), and the best return period standard is 2 a. The thresholds for the 4 types of waterlogging rainstorm are: M20 ≥ 26.47 mm, M60 ≥ 43.80 mm, CW; M20 ≥ 26.47 mm, M60 < 43.80 mm, IW; M20 < 26.47 mm, M60 ≥ 43.80 mm, AW; M20 < 26.47 mm and M60 < 43.80 mm, No waterlogging.

Understanding China’s Urban Rainstorm Waterlogging and Its Potential Governance

“…Rainstorms often affect the normal operation of the whole city through the weak links in the city [27]. The existing urban drainage pipe network is old, the management is chaotic, and it has not been improved with global warming and regional environment changes, which leads to a low level of urban storm waterlogging protection compared with the increasing number of rainstorm [29,32]. At the top level of urban development, emergency planners and urban development planners do not pay enough attention to urban rainstorm waterlogging (a basic security issue), leading to the frequent occurrence of urban rainstorm waterlogging in China at an institutional level [21,23].…”

“…The concept of a sponge city coincides with the rainwater management measures of foreign cities. How to absorb foreign experience and serve the construction of a local sponge city has become one of the key problems in solving urban waterlogging in China [29].…”

“…In general, rainstorm waterlogging disasters in China are more serious in the south than in the north, more serious in the mid-east, especially in the eastern urban agglomerations, and light in the west [22][23][24]. The rainstorm waterlogging disaster in China is becoming more serious [25][26][27][28][29][30][31], and the situation is not optimistic.…”

Impacts of Urbanization on Variations of Extreme Precipitation over the Yangtze River Delta

“…Conversely, the statistical analysis using observed data requires a simple computing process and applies to a smaller region [22]. Previous studies compared the variations of the rainfall characteristics (e.g., the intensity and duration of rainfall events) between different urbanized development stages in the same stations [23,24], and between different stations during the same period [25][26][27][28]. In these studies, the classification of the stations into urban and rural stations usually based on the administrative attributes.…”

Study on the Application of Clustering Method in the Determination of Uncertainty Parameters of SWMM Model

“…In the actual application of the SWMM model, it is a recognized method to calibrate and verify model parameters based on a large number of measured flood events (Hu et al 2020). However, this method needs to continuously adjust each sub-catchment according to the flood results, which is very time-consuming and complicated (Ma et al 2020). In the absence of data, the method proposed by (Zaghloul, 1983) to calibrate the parameters of the SWMM model based on the internal connection and mutual influence between the runoff calculation parameters of the system analytical model and the spatial distribution of the catchment area (Zaghloul, 1983).…”