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This paper investigated the impact of the spatial distribution of SuDS components on their flood reduction performance and the underlying mechanisms in a community with high groundwater levels. The effects of SuDS components' connectivity, decentralized level, and installation position along the flow direction on the reduction of total discharge volume (TDV), average discharge flow rate (ADFR), maximum discharge flow rate (MDFR), inundated area (IA), average inundated depth (AID), and maximum inundated depth (MID) were studied by coupling of the storm water management model (SWMM) and high-performance integrated hydrodynamic modelling system (HiPIMS). The results demonstrate that the connectivity has a positive linear correlation with the reduction of TDV (R2 > 0.991), ADFR (R2 > 0.992), and MDFR (R2 > 0.958), while the decentralized level of rain gardens and green roofs present positive one-phase exponential correlation with the reduction of TDV (R2 > 0.935), ADFR (R2 > 0.934) and MDFR (R2 > 0.967). A better-integrated mitigation of TDV and ADFR could be achieved by installing SuDS upstream along the flow direction. The connectivity from green roofs to rain gardens has a positive effect on the reduction of AID and MID but leads to the increase of IA. The findings of this study may contribute to the development of general spatial distribution guidelines and strategies to optimize the overall performance of SuDS components, especially at a community scale. [ABSTRACT FROM AUTHOR]

Hydrodynamic models have been widely used in urban flood modelling. Due to the prohibitive computational cost, most of urban flood simulations have been currently carried out at low spatial resolution or in small localised domains, leading to unreliable predictions. With the recent advance in high-performance computing technologies, GPU-accelerated hydrodynamic models are now capable of performing high-resolution simulations at a city scale. This paper presents a multi-GPU hydrodynamic model applied to reproduce a flood event in a 267.4 km2 urbanised domain in Fuzhou, Fujian Province, China. At 2 m resolution, the simulation is completed in nearly real time, demonstrating the efficiency and robustness of the model for high-resolution flood modelling. The model is used to further investigate the effects of varying spatial resolution and using localised domains on the simulation results. It is recommended that urban flood simulations should be performed at resolutions higher than 5 m and localised simulations may introduce unacceptable numerical errors. [ABSTRACT FROM AUTHOR]

Numerical models solving the full 2-D shallow water equations (SWEs) have been increasingly used to simulate overland flows and better understand the transient flow dynamics of flash floods in a catchment. However, there still exist key challenges that have not yet been resolved for the development of fully dynamic overland flow models, related to (1) the difficulty of maintaining numerical stability and accuracy in the limit of disappearing water depth and (2) inaccurate estimation of velocities and discharges on slopes as a result of strong nonlinearity of friction terms. This paper aims to tackle these key research challenges and present a new numerical scheme for accurately and efficiently modeling large-scale transient overland flows over complex terrains. The proposed scheme features a novel surface reconstruction method (SRM) to correctly compute slope source terms and maintain numerical stability at small water depth, and a new implicit discretization method to handle the highly nonlinear friction terms. The resulting shallow water overland flow model is first validated against analytical and experimental test cases and then applied to simulate a hypothetic rainfall event in the 42 km2 Haltwhistle Burn, UK. [ABSTRACT FROM AUTHOR]

Urban flooding may lead to significant losses of properties and lives and numerical modelling can facilitate better flood risk management to reduce losses. Flood modelling generally involves seeking numerical solutions to the shallow water equations (SWEs) or one of the simplified forms using the traditional numerical methods including the finite difference method (FDM), finite volume method (FVM) and finite element method (FEM). Recently, a relatively new approach, smoothed particle hydrodynamics (SPH), has also been used to solve the SWEs and encouraging results have been reported. However, the SPH method is computationally too demanding for efficient simulations, which has been one of the major disadvantages dogging its wider applications. This work presents an SPH model that is computationally accelerated by modern graphic processing units (GPUs) for efficient urban flooding modelling. The model's predictive capability and enhanced computational efficiency are demonstrated by application to experimental and field-scale hypothetic urban flood events. [ABSTRACT FROM AUTHOR]

• A full-scale fluvial flood modelling framework based on a high-performance hydrodynamic model solving the 2D SWEs. • Successful application to reproduce a storm induced flood in a 2500 km2 catchment at 5 m resolution. • High-resolution grid is essential for accurately simulating large-scale fluvial flood using a 2D hydrodynamic model. Full-scale fluvial flood modelling over large catchments has traditionally been carried out using coupled hydrological and hydraulic/hydrodynamic models. Such a traditional modelling approach is not well suited for the simulation of extreme floods induced by intense rainfall, which is usually featured with highly transient and dynamic rainfall-runoff and flooding process. This work aims to develop and demonstrate a modelling framework to predict the full-scale process of fluvial flooding from the source (rainfall) to impact (inundation) over a large catchment using a single high-performance hydrodynamic model driven by rainfall inputs. The modelling framework is applied to reproduce the flood event caused by the 2015 Storm Desmond in the 2500 km2 Eden Catchment at 5 m resolution. Without intensive model calibration, the predicted results compare well with field observations in terms of inundation extent and gauged water levels across the catchment. Sensitivity tests reveal that high-resolution grid is essential for accurate simulation of fluvial flood events using a 2D hydrodynamic model. Accelerated by multiple modern GPUs, the simulation is more than 2.5 times faster than real time although it involves 100 million computational cells inside the computational domain. This work provides a novel and promising approach to assess and forecast at real time the risk of extreme fluvial floods from intense rainfall. [ABSTRACT FROM AUTHOR]

Urban flooding as a result of inadequate drainage capacity, failure of flood defenses, etc. is usually featured with highly transient hydrodynamics. Reliable and efficient prediction and forecasting of these urban flash floods is still a great technical challenge. Meanwhile, in urban environments, the flooding hydrodynamics and process may be influenced by flow regulation and flood protection hydraulic infrastructure systems, such as sluice gates, which should be effectively taken into account in an urban flood model. However, direct simulation of hydraulic structures is not a current practice in 2D urban flood modeling. This work aims to develop a robust numerical approach to directly simulate the effects of gate structures in a 2D high-resolution urban flood model. A new modeling component is developed and fully coupled to a finite volume Godunov-type shock-capturing shallow water model, to directly simulate the highly transient flood waves through hydraulic structures. Different coupling approaches, i.e., flux term coupling and source term coupling, are implemented and compared. A numerical experiment conducted for an analytical dam-break test indicates that the flux term coupling approach may lead to more accurate results, with the calculated RMSE against water level 28%–38% less than that produced by the source term coupling approach. The flux term coupling approach is therefore adopted to improve the current urban flood model, and it is further tested by reproducing the laboratory experiments of flood routing in a flume with partially open sluice gates, conducted in the hydraulic laboratory at the Zhejiang Institute of Hydraulics and Estuary, China. The numerical results are compared favorably with experimental measurements, with a maximum RMSE of 0.0851 for all the individual tests. The satisfactory results demonstrate that the flood model implemented with the flux coupling approach is able to accurately simulate the flow through hydraulic structures, with enhanced predictive capability for urban flood modeling. [ABSTRACT FROM AUTHOR]

• A CNN model is proposed to estimate flood water depths. • The model can predict water depths for over half of a million cells instantly. • The CNN can potentially be used as a surrogate model for real-time applications. • The benefit of using CNN over other ML methods for flood modelling is presented. Most of the two-dimensional (2D) hydraulic/hydrodynamic models are still computationally too demanding for real-time applications. In this paper, an innovative modelling approach based on a deep convolutional neural network (CNN) method is presented for rapid prediction of fluvial flood inundation. The CNN model is trained using outputs from a 2D hydraulic model (i.e. LISFLOOD-FP) to predict water depths. The pre-trained model is then applied to simulate the January 2005 and December 2015 floods in Carlisle, UK. The CNN predictions are compared favourably with the outputs produced by LISFLOOD-FP. The performance of the CNN model is further confirmed by benchmarking against a support vector regression (SVR) method. The results show that the CNN model outperforms SVR by a large margin. The CNN model is highly accurate in capturing flooded cells as indicated by several quantitative assessment matrices. The estimated error for reproducing maximum flood depth is 0–0.2 m for the 2005 event and 0–0.5 m for the 2015 event at over 99% of the cells covering the computational domain. The proposed CNN method offers great potential for real-time flood modelling/forecasting considering its simplicity, superior performance and computational efficiency. [ABSTRACT FROM AUTHOR]

• A new fully quantitative assessment framework for multi-hazard risk assessment. • Joint probability of contributing hazards including statistical inter-dependencies. • Fully hydrodynamic modelling of interactive compound flooding processes. Multi-hazard risk assessment may provide comprehensive analysis of the impact of multiple hazards but still needs to resolve major challenges in three aspects: (1) proper consideration of hazard inter-dependency, (2) physically based modelling of hazard interactions, and (3) fully quantitative risk assessment to show the probability of loss. Compound flooding is a typical multi-hazard problem that involves the concurrence of multiple hazard drivers, e.g. heavy rainfall, extreme river flow, and storm surge. These hazard drivers may result from the same weather system and are thus statistically inter-dependent, physically overlayed and interacted in the same region. This paper aims to address the mentioned challenges and develop an integrated assessment framework to quantify compound flood risk. The framework is constructed based on the three typical components in disaster risk assessment, i.e. hazard, vulnerability and exposure analysis. In hazard analysis, joint probability and return period distributions of the three hazard drivers of compound flooding are estimated using Copula functions with hazard dependency analysis, which are then used to generate random multi-hazard events to drive a 2D high-performance hydrodynamic model to produce probabilistic inundation maps and frequency-inundation curves. Vulnerability and exposure analysis provides damage functions of the elements at risk, which are used to quantify multi-hazard risk with the frequency-inundation curves. The framework is applied in the Greater London and its downstream Thames estuary to demonstrate its capability to analyse hazard interactions and inter-dependencies to produce fully quantitative risk assessment results such as risk curves quantifying the probability of loss and risk maps illustrating the annual expected loss of residential buildings. [ABSTRACT FROM AUTHOR]