In recent decades, climate change is exacerbating meteorological disasters around the world, causing more serious urban flood disaster losses. Many solutions in related research have been proposed to enhance urban adaptation to climate change, including urban flooding simulations, risk reduction and urban flood-resistance capacity. In this paper we provide a thorough review of urban flood-resilience using scientometric and systematic analysis. Using Cite Space and VOS viewer, we conducted a scientometric analysis to quantitively analyze related papers from the Web of Science Core Collection from 1999 to 2021 with urban flood resilience as the keyword. We systematically summarize the relationship of urban flood resilience, including co-citation analysis of keywords, authors, research institutions, countries, and research trends. The scientometric results show that four stages can be distinguished to indicate the evolution of different keywords in urban flood management from 1999, and urban flood resilience has become a research hotspot with a significant increase globally since 2015. The research methods and progress of urban flood resilience in these four related fields are systematically analyzed, including climate change, urban planning, urban system adaptation and urban flood-simulation models. Climate change has been of high interest in urban flood-resilience research. Urban planning and the adaptation of urban systems differ in terms of human involvement and local policies, while more dynamic factors need to be jointly described. Models are mostly evaluated with indicators, and comprehensive resilience studies based on traditional models are needed for multi-level and higher performance models. Consequently, more studies about urban flood resilience based on local policies and dynamics within global urban areas combined with fine simulation are needed in the future, improving the concept of resilience as applied to urban flood-risk-management and assessment.

The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most extensively implemented numerical models for simulating urban runoff. Parameter optimization is essential for reliable SWMM model simulation results, which are heterogeneously sensitive to a variety of parameters, especially when involving complicated simulation conditions. This study proposed a Genetic Algorithm-based parameter optimization method that combines the Morris screening method with the generalized likelihood uncertainty estimation (GLUE) method. In this integrated methodology framework, the Morris screening method is used to determine the parameters for calibration, the GLUE method is employed to narrow down the range of parameter values, and the Genetic Algorithm is applied to further optimize the model parameters by considering objective constraints. The results show that the set of calibrated parameters, obtained by the integrated Morris and GLUE methods, can reduce the peak error by 9% for a simulation, and then the multi-objective constrained Genetic Algorithm reduces the model parameters’ peak error in the optimization process by up to 6%. During the validation process, the parameter set determined from the combination of both is used to obtain the optimal values of the parameters by the Genetic Algorithm. The proposed integrated method shows superior applicability for different rainfall intensities and rain-type events. These findings imply that the automated calibration of the SWMM model utilizing a Genetic Algorithm based on the combined parameter set of both has enhanced model simulation performance.

How to allocate the limited water consumption and pollutant carrying capacity of water function areas to sub-areas and water sectors is key for the practice of the most stringent water resource management decision-making. In response to the joint optimal allocation mechanism of water consumption and pollutant-carrying capacity, joint optimal allocation and the unified quantification problem of multiform benefits, the whole process of joint optimal distribution is refined into the links “water supply—water use and consumption—drainage—pollutant carrying” and the unified quantification method based on the emergy theory to achieve various economic, social, and ecological benefits in different links is proposed. Then the joint optimal allocation joint optimal allocation model of regional water consumption and pollutant carrying capacity is constructed, which realizes the reverse constraint relationship between water quantity and pollution carrying capacity in the water quality and quantity conversion processes. Using Zhengzhou city as an example, the models and methods were applied and researched. Countermeasures and suggestions for the rational allocation of available water resources and water pollution capacity were put forward accordingly.

Refined urban flood risk assessment requires a paradigm beyond traditional single-scale analyses. Existing approaches often overlook fine-scale functional differentiation and cascading risk connectivity across urban systems. This study proposes an integrated framework combining semantic–spatial representation of Point-of-Interest (POI) data, hierarchical feature integration, and a hydrological–functional–road tripartite potential flood risk connectivity network. A semantic–spatial dual-branch autoencoder extracts POI features for functional zone identification, while hazard, exposure, and coping capacity indicators are integrated to quantify block-scale flood risk. Hydrological connectivity, functional coupling, and road cascading failure models are further combined to characterize multi-path flood risk connectivity patterns. Results show that (1) functional zones were derived from dominant POI categories. The semantic–spatial clustering achieved a silhouette coefficient of 0.564 with 0.2% noise blocks. The spatial-only configuration achieved 0.554. This suggests that semantic information provided complementary but limited refinement. (2) A total of 302 high-risk blocks (20.5%) were identified, while the median-based classification showed that public service zones had the highest proportion of the high-risk group (61.3%). (3) The coupled flood risk connectivity network covers 99.2% of blocks with significant spatial dependence (Moran’s I = 0.703). Connectivity intensity varied among functional zones, but most pairwise differences were not significant after Bonferroni correction. Network centrality showed significant associations with connectivity intensity and range. (4) The mean standardized functional mixture degree was 0.629. Geographically weighted regression (GWR) revealed spatial variation in the local association between functional mixture and physical flood susceptibility, but the Monte Carlo test did not provide evidence of statistically significant global spatial non-stationarity (p = 0.960). This framework provides a function-oriented perspective for understanding urban flood resilience by integrating functional structure, multidimensional flood risk, and multi-path risk connectivity, and supports the characterization of potential flood-sensitive areas through function-based flood risk assessment.

Increasing urban extreme rainfall, driven by global climate change and rapid urbanization, highlights the deficiencies of existing studies on urban system responses to flooding. Current functional assessments of floods often neglect spatial variations and the temporal dynamics of urban functions. We first developed a GIS-based spatiotemporal assessment framework that integrates system functional curves and complex network analysis to evaluate urban functional conditions. Focusing on the 20 July 2021 Zhengzhou flood, the High-Performance Integrated Hydrodynamic Modeling System (HiPIMS) coupled with the Storm Water Management Model (SWMM) was used to simulate flood processes and spatiotemporal variations. The results are as follows: (1) Four rainfall events increased inundation areas, with the extreme event (Pr3) causing an 82.4% expansion relative to the initial water surface. (2) Flood stages (absorption, resistance, overload) were classified by maximum flood depth and inundation area, as well as the maximum growth rate of flood depth and inundation area. Grids with medium-level maximum inundation depth predominated, while those with the lowest-level maximum depth growth were least frequent. (3) Overall urban function declined sharply at 17:20, reaching a minimum of 18.2% at 21:00, resulting in a 32.8% total functional loss. (4) Complex network analysis reveals strong temporal co-movement among urban functions, with the temporal correlation network comprising 10 edges (density = 0.667, global efficiency = 0.865). Education and emergency shelter were consistently identified as the most central nodes across different correlation thresholds, indicating potential systemic vulnerability. The framework elucidates the spatiotemporal dynamics of urban function assessment during flooding and provides actionable insights for understanding urban system functioning mechanisms and emergency planning.