Urban flood modeling is a prevalent research topic worldwide, including surface flow, underground drainage flow and especially the inevitable flow connecting with underground infrastructures in highly urbanized areas. A comprehensive modeling approach and effective mitigation measures are necessary for urban risk assessment and management. Herein in this work, surface flow modeling was first investigated based on an experiment; then, the underground modeling system was included by a coupled model proposed in this work; finally, urban rainfall - runoff simulations were carried out accounting for different spatial and temporal resolutions of rain data and further mitigation effect of rain garden. In pure surface flow modeling, depth-dependent roughness and infiltration methods were numerically investigated based on rainfall - runoff experiments, under two conditions in terms of traditional and LID (Low Impact Development measures) surface conditions, different slopes and rainfall intensities. First, observed runoff data has been used to calibrate the model parameters for the depth - dependent roughness and infiltration approaches for both traditional and LID surfaces. The calibrated parameter values then have been applied to validate other two rainfall events. High NSE Coefficient (Nash-Sutcliffe Efficiency) and low SDR (Standard Deviation R) values were obtained, indicating a satisfied agreement between simulated results and observed data for both calibration and validation cases. Then, further comparison of constant and depth - dependent methods clearly pointed out the superiority of the latter methods, as they led to much better evaluation criteria. In addition, the depth-dependent roughness method ensured stability. We also refer to Mügler et al. (2011a) who compared four roughness methods within one case study also proving that the best results were obtained with a water depth-dependent Manning law. The results demonstrated the superiority of the depth - dependent infiltration method when compared to constant infiltration and the necessity of the depth - dependent roughness approach for accuracy and stability reasons. The methods implemented here might also improve other shallow water models. In coupling between the hydrodynamic surface flow model and the underground drainage system, large underground urban infrastructures were explored especially. While several similar coupled models existed, none of them ever have been used to include large underground urban infrastructures such as transportation tunnels, metro stations or car parks. This, however, is quite important on the one hand to simulate flooding and associated risks within these underground infrastructures; on the other hand, to have a correct representation of the surface flow processes in the surroundings where the underground infrastructures are connected to the surface flow. First, a bidirectional coupling between the open-source surface flow solver hms++ (Hydroinformatics Modeling System) and the SWMM (Storm Water Management Model), which was realized as a plugin, was enabled allowing its capability to be loaded at runtime without changing the hms++ code. Second, the coupled model was verified using an idealized case with separated surface catchments connected via a subsurface link, to establish plausibility and mass conservation. Then, the results of a classic validation case consisting of a system of pipes splitting into parallel paths and reuniting were compared to those obtained with the commercial tool InfoWorks ICM, demonstrating close alignment between the two. Third, the extended hms++ model was applied to two real-world cases, a metro station and a transportation tunnel which both have been idealized as large underground pipes. While the pure surface flow model predicted implausible backwaters at the metro station entrance and the transportation tunnel portals, the coupled model correctly eliminated these backwaters by modeling inflow into the metro station and the transportation tunnel. This further enabled investigating water depths and flow velocities within the metro station and the transportation tunnel and in the latter risks for human stability and recommendations for vehicle speeds were assessed. Overall, the extended coupled hms++ model has demonstrated its capabilities to qualitatively and quantitatively account for large underground urban infrastructures and thus to contribute to representation of urban flooding processes in a more precise way than offering a parameterization of pipe flow capacity and drainage overflow. In the application of the proposed rainfall – runoff model, different spatial and temporal rainfall resolutions, including spatial temporal distribution (TSD), temporal uniform (TU), spatial uniform (SU), spatial uniform reverse (SUR), and spatial temporal uniform (TSU), and the mitigation effect of green infrastructure were explored. The TSD rainfall data, which accounts for both spatial and temporal variability, provided reference for flood extent and depth in this application. First, the analysis among different rain data resolutions with surface - only model revealed that both spatial and temporal characteristics of rain data affect inundation, spatial resolutions (SU, SUR and TSU) performed stronger effect than temporal resolution (TU), with respect to the maximum inundation depth in this case. Second, incorporating drainage systems, the comparison between the surface model and the coupled model over different rain data resolutions highlighted the critical role of drainage systems in altering urban flood dynamics, both in inundation time and depth. A larger reduction on final inundated depth than the maximum inundated depth indicated that the drainage system was more efficient in after-peak water removal than in peak attenuation. This further suggests that current drainage systems are more suited for accelerating inundation in recession periods rather than limiting the peak severeness. With regard to the reduction peak inundated depth caused by drainage system, the largest occurred in TU resolution among those five resolutions. Third, the analysis among those rain resolutions with the coupled mode showed that, ignoring spatial variability (SU resolution) led to increased peak inundation volume compared with TSD, while temporal smoothing of rainfall (TU and TSU resolutions) led to substantial reductions in peak inundation volume but advanced the onset of inundation time and delayed the peak arrival. This indicated that neglecting temporal variability would distort the timing of urban flood response even if overall flooding appears reduced. Furthermore, for effect of different rain resolutions on drainage outfalls outflow displayed that, SU, TU, and TSU resolutions delayed outflow peak at both observed outfalls compared to the TSD resolution, only SUR advances it. These changes highlighted the sensitivity of drainage system response to rainfall structure in spatial and temporal. In the end, the exploration of rain garden as a mitigation measure under three rainfalls was investigated. Optimal placement of rain gardens in areas with higher runoff accumulation significantly reduced flood depths at selected hot spot, indicating that the potential of nature-based solutions in urban flood mitigation. With the simulated rain cases, the effectiveness of rain gardens in reducing surface inundation diminished with increasing storm intensity was demonstrated. And the implementation of a rain garden significantly alters the hydrodynamic response across different storm return periods. These results indicated that while rain gardens effectively mitigate flood depth under moderate storms, their performance becomes weaker under extreme events. These findings provided insights for urban planners and policymakers in designing resilient flood management systems that integrated accurate modeling, high-resolution data and sustainable mitigation practices. Overall, this work proposed a comprehensive modeling approach for simulating urban areas including drainage systems and large underground infrastructures. With the development of depth - dependent roughness and infiltration methods, the investigation of spatial and temporal rainfall resolutions and the application green infrastructures - the rain garden, the model was successfully applied to several test cases and real urban areas. The exploration of rain data resolutions and rain garden cases offered insights into their respective impacts on surface runoff dynamics, drainage performance and the overall effectiveness of mitigation strategies in urban flood scenarios.
Yangwei Zhang (Thu,) studied this question.