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Discretisation of the friction terms to ensure numerical stability and accuracy remains to be challenging for the development of robust numerical schemes to solve the shallow water equations (SWEs), particularly for applications involving very shallow flows (e.g. overland flows and wet/dry fronts) over complex domain topography. The key challenge is to ensure relaxation of the flow towards an equilibrium state characterised by the balance between friction and gravity in a computationally efficient way. To overcome this numerical challenge, this paper proposes a novel approach for discretising the friction source terms in the SWEs in the context of an explicit finite volume method. The overall numerical scheme adopts the HLLC Riemann solver and surface reconstruction method (SRM) to explicitly discretise the flux and bed slope source terms. Whilst a fully implicit scheme is used to handle the friction source terms, solution to the implicit formulation is analytically derived to explicitly update the flow variables. Compared with the existing approaches, the proposed scheme effectively resolves the issue associated with stiff relaxation without necessity to use an iteration method and it supports efficient simulation using time steps controlled only by the Courant–Friedrichs–Levy (CFL) condition. The current friction term discretisation scheme is not coupled with flux and bed slope calculation and therefore may be readily implemented in any other explicit finite volume SWE models. After being successfully validated against two benchmark tests with analytical solutions, the resulting new SWE model is applied to reproduce a rainfall-flooding event in the Upper Lee catchment in the UK. [ABSTRACT FROM AUTHOR]

A number of studies have predicted more frequent and intensive storms as a result of climate change in UK and other parts of the world, which may consequently cause more hazardous flash floods in steep catchments of small to medium size (e.g. up to 100 km 2 ). These flash floods are commonly characterized by high-velocity overland flow as a result of rapid catchment response to the intense rainfall. The hydrological processes related to the rapid catchment response are poorly understood and reliable prediction is generally beyond the capability of traditional hydrological models or simplified hydrodynamic models. This work aims to present a shock-capturing hydrodynamic modelling system to simulate the complex rainfall-runoff and the subsequent flash surface flooding process in a rapid-response catchment. The model solves the fully 2D shallow water equations using a finite volume Godunov-type shock-capturing numerical scheme for the rapidly varying overland flow hydrodynamics following intense rainfall. Typically, this type of shock-capturing hydrodynamic models is not able to provide efficient and high-resolution simulations for large-scale flash flood events due to their high computational demand. In order to substantially improve the computational efficiency and enable catchment-scale simulations at very high resolution involving millions of computational nodes, the model is implemented on GPUs for high-performance parallel computing. After being validated against the analytical benchmark of Tilted V-catchment test, the GPU-accelerated hydrodynamic modelling system is applied to simulate the rainfall-runoff process in the 42 km 2 Haltwhistle Burn Catchment in England. [ABSTRACT FROM AUTHOR]

Smoothed particle hydrodynamics (SPH) is a fully Lagrangian meshless computational method for solving the fluid dynamics equations. In recent years, it has also been employed to solve the shallow water equations (SWEs) and promising results have been obtained. However, SPH models are computationally very demanding and the SPH-SWE models considered in this work have no exception. In this paper, the Graphic Processing Units (GPUs) are explored to accelerate an SPH-SWE model for wider applications. Unlike Central Processing Units (CPUs), GPUs are highly parallelized, which makes it suitable for accelerating scientific computing algorithms like SPH. The aim is to design a GPU-based SPH model for solving the two-dimensional SWEs with variable smoothing lengths. Furthermore, a quad-tree neighbour searching method is implemented to further optimize the model performance. An idealized benchmark test and two real-world dam-break cases have been simulated to demonstrate the superior performance of the current GPU-accelerated high-performance SPH-SWE model. [ABSTRACT FROM AUTHOR]

Flow-like landslides commonly happen in mountainous areas and may threaten people's lives, damage their properties, and create negative impact on the environment. Computer modelling has become an effective tool to support landslide risk assessment and management. Models based on discrete element method (DEM) can capture micro-mechanical behaviour of soils, simulate large deformation and have been widely used for landslide simulations. However, these models are computationally too demanding for large-scale applications. On the other hand, depth-averaged models (DAM) have been well reported for simulation of flow-like landslides over large spatial domains due to its relatively high computational efficiency. To combine the advantages of both types of modelling approaches, this paper develops a novel landslide model by coupling a DEM model with a DAM for landslide simulation, in which the DEM component is employed to better simulate the complex landslide dynamics in the source area and the DAM is adopted to predict the predominantly convective movement in the runout and deposition zone. Finally, the new coupled landslide model is validated against several test cases, including a field-scale event. Satisfactory results have been obtained, demonstrating that the coupled model is able to reproduce the dynamic process of flow-like landslides. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new SPH model is presented for solving the 2D shallow water equations. [•] Well-balanced issue is thoroughly discussed in the context of SPH discretization. [•] Corrected SPH formulation is derived to ensure well-balanced solution. [•] The MUSCL approach is applied to enable Riemann solver based artificial viscosity. [•] Local bed modification method is proposed to prevent negative water depth. [ABSTRACT FROM AUTHOR]

Shallow flow models, which solve these depth-integrated equations, are widely used for both research and practical applications, including understanding catchment processes and predicting hydrological hazards such as flooding. [9] and [8] present a multiwavelet-based AMR strategy, which in combination with a DG scheme yields a robust and efficient shallow flow model. [3] and [17] use GPU-accelerated shallow flow models for large scale flood inundation modelling applications, e.g., simulating flooding from source (rainfall) to impact (inundation) in a 2500 km SP 2 sp catchment. [Extracted from the article]

• 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]

Flow-like landslides are a common type of natural hazards that may impose a great risk to people and their properties. Different models have been reported for simulating flow-like landslides, all of which possess limitations due to the underlying assumptions and simplifications. Harnessing the advantages of two types of prevailing modelling approaches, a new coupled model is developed which adopts a discrete element method (DEM) model to simulate the complex collapsing process in the source area and a depth-averaged model (DAM) to predict the predominantly convective movement in the runout and deposition zones. The coupled model is finally implemented on the NVIDIA CUDA programming platform to achieve GPU high-performance computing. Two laboratory tests are considered to validate the model and a field-scale landslide event is simulated to verify its applicability in real-world conditions. Satisfactory results confirm that the coupled model is able to reproduce the dynamic process of real-world flow-like landslides. • A new modelling framework is developed for flow-like landslide simulation from geomaterial collapse to runout and deposition. • The coupled model outperforms the depth-averaged model and discrete element method in reproducing experimental tests. • The coupled model is successfully applied to simulate the dynamic process of a real-world flow-like landslide. • The coupled model is computationally 2.5 times more efficient than the DEM counterpart. [ABSTRACT FROM AUTHOR]

Nonpoint source (NPS) particulate pollutants are a major source of urban surface pollution. It is essential to better understand and model the dynamic process of NPS pollutants to inform the design of effective strategies for urban stormwater management and pollution control. This work presents a novel coupled hydrodynamic and particle-based model to simulate the full-process dynamics of NPS particulate pollutants from detachment, transport to deposition in urban areas. A particle-based approach is proposed to represent the physical processes of pollutant detachment and deposition. The transport of pollutant particles is simulated using a random-walk particle-tracking model, which can directly trace out the trajectories of individual particles and hence identify the pathways of pollutants. The new coupled hydrodynamic and particle-based stormwater quality model is successfully validated against several analytical and experimental test cases to demonstrate its capability in accurately simulating the full-process dynamics of NPS particulate pollutants in urban areas. • A new coupled hydrodynamic and particle-based model for urban NPS pollutant dynamics. • New particle-based approach for physical representation of pollutant wash-off dynamics. • The new model enables direct prediction of trajectories and pathways of pollutants. [ABSTRACT FROM AUTHOR]

• A novel 1D-2D coupled model is developed for hydrodynamic simulation of transient flows in drainage networks. • The pipe flow is calculated using an improved 1D TPA model and the junction flow is approximated using a 2D hydrodynamic model. • The new approach automatically takes into account mass and momentum conservation in junction calculation and removes the necessity of complicated boundary settings. Drainage network modelling is often an essential component in urban flood prediction and risk assessment. Drainage network models most commonly use different numerical procedures to handle flows in pipes and junctions. Numerous numerical schemes and models of different levels of complexity have been developed and reported to predict flows in pipes. However, calculation of the flow conditions in junctions has received much less attention and has been traditionally achieved by solving only the continuity equation. This method is easy to implement but it neglects the momentum exchange in the junctions and cannot provide sufficient boundary conditions for the pipe calculation. In this work, a novel numerical scheme based on the finite volume solution to the two-dimensional (2D) shallow water equations (SWEs) is proposed to calculate flow dynamics in junctions, which directly takes into account both mass and momentum conservation and removes the necessity of implementing complicated boundary settings for pipe calculations. This new junction simulation method is then coupled with the widely used two-component pressure approach (TPA) for the pipe flow calculation, leading to a new integrated drainage network model. The new 1D-2D coupled drainage network model is validated against an experimental and several idealised test cases to demonstrate its potential for efficient and stable simulation of flow dynamics in drainage networks. [ABSTRACT FROM AUTHOR]