Urbanisation is an irreversible trend as a result of social and economic development. Urban areas, with high concentration of population, key infrastructure, and businesses, are extremely vulnerable to flooding and may suffer severe socio-economic losses due to climate change. Urban flood modelling tools are in demand to predict surface water inundation caused by intense rainfall and to manage associated flood risks in urban areas. These tools have been rapidly developing in recent decades. In this study, we present a comprehensive review of the advanced urban flood models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. The study explores the advantages and limitations of existing model types, highlights the most recent advances, and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.

In this paper, we describe a new method of modeling coastal inundation arising from storm surge by coupling a widely used storm surge model (ADCIRC) and an urban flood inundation model (FloodMap). This is the first time the coupling of such models is implemented and tested using real events. The method offers a flexible and efficient procedure for applying detailed ADCIRC storm surge modeling results along the coastal boundary (with typical resolution of ∼100 m) to FloodMap for fine resolution inundation modeling (70 m). In further testing, we explored the effects of mesh resolution and roughness specification. Results agree with previous studies that fine resolution is essential for capturing intricate flow paths and connectivity in urban topography. While the specification of roughness is more challenging for urban environments, it may be empirically optimized. The successful coupling of ADCIRC and FloodMap models for fine resolution coastal inundation modeling unlocks the potential for undertaking large numbers of probabilistically based synthetic surge events for street‐level risk analysis.

Emergency responders often have to operate and respond to emergency situations during dynamic weather conditions, including floods. This paper demonstrates a novel method using existing tools and datasets to evaluate emergency responder accessibility during flood events within the city of Leicester, UK. Accessibility was quantified using the 8 and 10 min legislative targets for emergency provision for the ambulance and fire and rescue services respectively under "normal" no-flood conditions, as well as flood scenarios of various magnitudes (1 in 20-year, 1 in 100-year and 1 in 1000-year recurrence intervals), with both surface water and fluvial flood conditions considered. Flood restrictions were processed based on previous hydrodynamic inundation modelling undertaken and inputted into a Network Analysis framework as restrictions for surface water and fluvial flood events. Surface water flooding was shown to cause more disruption to emergency responders operating within the city due to its widespread and spatially distributed footprint when compared to fluvial flood events of comparable magnitude. Fire and rescue 10 min accessibility was shown to decrease from 100, 66.5, 39.8 and 26.2 % under the no-flood, 1 in 20-year, 1 in 100-year and 1 in 1000-year surface water flood scenarios respectively. Furthermore, total inaccessibility was shown to increase with flood magnitude from 6.0 % under the 1 in 20-year scenario to 31.0 % under the 1 in 100-year flood scenario. Additionally, the evolution of emergency service accessibility throughout a surface water flood event is outlined, demonstrating the rapid impact on emergency service accessibility within the first 15 min of the surface water flood event, with a reduction in service coverage and overlap being observed for the ambulance service during a 1 in 100-year flood event. The study provides evidence to guide strategic planning for decision makers prior to and during emergency response to flood events at the city scale. It also provides a readily transferable method for exploring the impacts of natural hazards or disruptions in other cities or regions based on historic, scenario-based events or real-time forecasting, if such data are available.

Flash floods have occurred frequently in the urban areas of southern China. An effective process-oriented urban flood inundation model is urgently needed for urban storm-water and emergency management. This study develops an efficient and flexible cellular automaton (CA) model to simulate storm-water runoff and the flood inundation process during extreme storm events. The process of infiltration, inlets discharge and flow dynamics can be simulated with little preprocessing on commonly available basic urban geographic data. In this model, a set of gravitational diverging rules are implemented to govern the water flow in a rectangular template of three cells by three cells of a raster layer. The model is calibrated by one storm event and validated by another in a small urban catchment in Guangzhou of southern China. The depth of accumulated water at the catchment outlet is interpreted from street-monitoring closed-circuit television (CCTV) videos and verified by on-site survey. A good level of agreement between the simulated process and the reality is reached for both storm events. The model reproduces the changing extent and depth of flooded areas at the catchment outlet with an accuracy of 4 cm in water depth. Comparisons with a physically based 2-D model (FloodMap) show that the model is capable of effectively simulating flow dynamics. The high computational efficiency of the CA model can meet the needs of city emergency management.

Numerical modelling of flood inundation over large and complex floodplains often requires mesh resolutions coarser than the structural features (e.g. buildings) that are known to influence the inundation process. Recent research has shown that this mismatch is not well represented by conventional roughness treatments, but that finer‐scale features can be represented through porosity‐based subgrid‐scale treatments. This paper develops this work by testing the interactions between feature representation, subgrid‐scale resolution and mesh resolution. It uses as the basis for this testing a 2D diffusion‐based flood inundation model which is applied to a 2004 flood event in a topologically complex upland floodplain in northern England. This study formulated simulations with different grid mesh resolution and subgrid mesh ratio. The sensitivity of the model to mesh resolution and roughness specification was investigated. Model validation and verification suggest that the subgrid treatment with higher subgrid mesh ratio can give much improved predictions of flood propagation, in particular, in terms of the predicted water depth. This study also highlighted the limitation of using at‐a‐point in time inundation extent for validation of flood models of this type. Copyright © 2010 John Wiley & Sons, Ltd.