Changes in the hydrological process caused by urbanization lead to frequent flooding in cities. For fast-growing urban areas, the impact of urbanization on the hydrological process needs to be systematically analyzed. This study takes Zhengzhou as an example to analyze the impact of urbanization on the hydrological process based on 1971–2012 hourly rainfall-runoff data, combining Geographic Information Systems with traditional hydrological methods. Our study indicates that the rain island effect in different districts of city became stronger with the increase of its built-up. The uneven land use resulted in the difference of runoff process. The flood peak lag was 25–30% earlier with the change of land use. The change of flood peak increased by 10–30% with the change of built-up. The runoff coefficient increases by 20–35% with the increase of built-up, and its change increased with the change of land use. Affected by the rain island effect, precipitation tends to occur in areas where built-up is dominant, which overall magnifies the impact of urbanization on the hydrological process. This provides new ideas for urban flood control. Refine flood control standards according to regional land use changes to cope with the hydrological process after urbanization.

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.

Over the past several decades, urban flooding and other water-related disasters have become increasingly prominent and serious. Although the urban rain flood model’s benefits for urban flood simulation have been extensively documented, the impact of rainfall input to model simulation accuracy remains unclear. This systematic review aims to provide structured research on how rain inputs impact urban rain flood model’s simulation accuracy. The selected 48 peer-reviewed journal articles published between 2015 and 2019 on the Web of Science™ database were analyzed by key factors, including rainfall input type, calibration times and verification times. The results from meta-analysis reveal that when a traditional rain measurement was used as the rainfall input, model simulation accuracy was higher, i.e., the Nash–Sutcliffe efficiency coefficient (NSE) of traditional technology for rain measurement was higher than the 0.18 for the new technology rain measurement with respect to flow simulation. In addition, the single-field sub-flood calibration model was better than the multi-field sub-flood calibration model. NSE was higher than 0.14. The precision was better for the verification period; NSE of the calibration value showed a 0.07 higher verification value on average in flow simulation. These findings have certain significance for the development of future urban rain flood models and propose the development direction of the future urban rain flood model. Finally, in view of the rainfall input problem of the urban storm flood model, we propose the future development direction of the urban storm flood model.

Climate change and rapid urbanization have increased pressure on drainage systems, posing new challenges to preventing and controlling urban waterlogging. In 2013, China proposed the Sponge City, a strategic measure for urban waterlogging control. This study quantifies the effects of stormwater management measures in runoff reduction for different levels of rainfall and conducts a one‐dimensional visual analysis of urban waterlogging risks. At the same time, the best cost‐effective scheme is determined based on life‐cycle cost, analytic hierarchy process, and regret decision theory. The results showed stormwater management measures could realize the function of runoff control and waterlogging prevention, especially under low precipitation. However, these measures were still not enough to eliminate waterlogging risk. Combined measures have stronger runoff control capabilities than single measures. Considering economic, environmental, and operational impacts comprehensively, the combined measures of bio‐retention (BR), permeable pavement (PP), and green roof (GR) were determined as the best cost‐effective scheme because of the lowest regret value. The proposed method is helpful to provide reference and decision‐making basis for the construction of sponge cities in the future.

The storm water management model (SWMM) is widely used in urban rainfall runoff simulations, but there are no clear rules for the division of its sub catchment areas. At present, the popular sub catchment area division method takes the average slope as the slope parameter of the sub catchment area, which brings errors to the model in mechanism. Based on the current method, this paper proposes a new method to further subdivide the sub catchment area of the SWMM model, according to the Digital Elevation Model (DEM) data of underlying surface, slope and aspect information. By comparing with the previous methods, it was found that the division method based on slope and aspect can make the setting of model parameters and hydraulic exchange conditions clearer, and improve the accuracy of the model on a certain level.

The graphical abstract presents a three-stage workflow for dynamic urban waterlogging forecasting using real-time SWMM model calibration. Step 1: Multi-source Data Fusion integrates rainfall forecasts, urban drainage monitoring data, and crowdsourced waterlogging information from social media platforms such as Weibo.Step 2: Real-time Dynamic Forecasting Framework runs the SWMM model, judges forecast errors (triggering calibration when the water depth error exceeds 5 cm), identifies sensitive parameters, performs real-time calibration, and updates forecasts iteratively. Step 3: The results show two key improvements: (1) Accuracy improvement: average Nash-Sutcliffe Efficiency (NSE) increased by 0.29, with all nodes reaching Class B accuracy; (2) Timeliness superiority: the timeliness coefficient (TC) is ? 0.9 (Class A), with each node’s calibration completed in 40 seconds to 1 minute. The framework balances accuracy and timeliness for real-time urban waterlogging early warning. Under the climate change paradigm, urban waterlogging triggered by extreme rainfall is intensifying. Precise, dynamic waterlogging forecasting is vital for urban management and residents to cope with disasters. Current studies often use near-real-time rainfall forecasts to drive hydro-hydraulic models for short-term urban waterlogging prediction, with static inputs and parameters, termed static forecasting. Absent a correction mechanism, static forecasts' accuracy often falls short for effective waterlogging control. This study takes the SWMM model as an example, introduces a real-time calibration mechanism for model parameters, and proposes a method for dynamic forecasting of urban waterlogging processes. A case application was carried out in Zhengzhou, China. The results show that compared with static forecasting, dynamic forecasting can improve the forecasting accuracy in stages during the process, with the average Nash–Sutcliffe Efficiency (NSE) coefficient increased by 0.29, and all nodes achieving a forecasting level above Class B. This method balances accuracy and timeliness, and can be effectively applied to urban waterlogging emergency management.