Analyzing the spatial interactions between rainfall levels and flooding prediction in São Paulo

Abstract

Rainfall is one of the primary triggers for many geological and hydrological natural disasters. While the geological events are related to mass movements in land collapse due to waterlogging, the hydrological ones are usually assigned to runoff or flooding. Studies in the literature propose predicting mass movement events as a function of accumulated rainfall levels recorded at distinct periods. According to these approaches, a two‐dimensional rainfall levels feature space is segmented into the occurrence and non‐occurrence decision regions by an empirical critical curve (CC). Although this scheme may easily be extended to other purposes and applications, studies in the literature need to discuss its use for flooding prediction. In light of this motivation, the present study is unfolded in (1) verifying that defining CCs in the rainfall levels feature space is a practical approach for flooding prediction and (2) analyzing how geospatial components interact with rainfall levels and flooding prediction. A database containing the rainfall levels recorded for flooding and non‐flooding events in São Paulo city, Brazil, regarding the period 2015–2016, was considered in this study. The results indicate good accuracy for flooding prediction using only partial rain, which can be improved by adding physical characteristics of the flooding locations, demonstrating a direct correlation with spatial interactions, and rainfall levels.

Analyzing the Mitigation Effect of Urban River Channel Flood Diversion on Waterlogging Disasters Based on Deep Learning

“…where h represents the water depth (m), u and v represent the velocities in the x and y directions, respectively (m/s), S 0,x and S 0,y represent the bed slopes in the x and y directions, respectively, S f,x and S f,y represent the frictional forces in the x and y directions, respectively, q 1D denotes the discharge per unit area (m 3 /s), and u 1D and v 1D represent the velocities of q 1D in the x and y directions, respectively (m/s). Next, 120 rainfall scenarios comprising six common rainfall patterns in Zhengzhou (Figure 4) combined with 20 return periods (1,2,3,4,5,7,10,20,30,40,50,70,100,200, 300, 400, 500, 700, 800, and 1000a) were input into the InfoWorks ICM model to simulate the water depth for each triangular mesh. Finally, a heavy rainfall inundation dataset was constructed based on the design rainfall data and simulated results from the InfoWorks ICM model.…”

“…Intense rainfall events have led to urban river channel overflows, exacerbating the challenges associated with coping with urban waterlogging disasters and posing significant threats to people's lives, property, and urban infrastructure. For example, in February 2023, São Paulo, Brazil, was hit by heavy rainfall, which resulted in river flooding, severe flooding, collapsed houses, and hindered transportation across the city [5]. In July 2023, Beijing, China, experienced severe waterlogging disasters, with the water level of the Yongding River sharply rising and the collapse of the Xiaoqing River Bridge, causing extensive damage and sweeping away numerous vehicles [6,7].…”