LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs

Abstract

Abstract. LISFLOOD-FP 8.0 includes second-order discontinuous Galerkin (DG2) and first-order finite-volume (FV1) solvers of the two-dimensional shallow-water equations for modelling a wide range of flows, including rapidly propagating, supercritical flows, shock waves or flows over very smooth surfaces. The solvers are parallelised on multi-core CPU and Nvidia GPU architectures and run existing LISFLOOD-FP modelling scenarios without modification. These new, fully two-dimensional solvers are available alongside the existing local inertia solver (called ACC), which is optimised for multi-core CPUs and integrates with the LISFLOOD-FP sub-grid channel model. The predictive capabilities and computational scalability of the new DG2 and FV1 solvers are studied for two Environment Agency benchmark tests and a real-world fluvial flood simulation driven by rainfall across a 2500 km2 catchment. DG2's second-order-accurate, piecewise-planar representation of topography and flow variables enables predictions on coarse grids that are competitive with FV1 and ACC predictions on 2–4 times finer grids, particularly where river channels are wider than half the grid spacing. Despite the simplified formulation of the local inertia solver, ACC is shown to be spatially second-order-accurate and yields predictions that are close to DG2. The DG2-CPU and FV1-CPU solvers achieve near-optimal scalability up to 16 CPU cores and achieve greater efficiency on grids with fewer than 0.1 million elements. The DG2-GPU and FV1-GPU solvers are most efficient on grids with more than 1 million elements, where the GPU solvers are 2.5–4 times faster than the corresponding 16-core CPU solvers. LISFLOOD-FP 8.0 therefore marks a new step towards operational DG2 flood inundation modelling at the catchment scale. LISFLOOD-FP 8.0 is freely available under the GPL v3 license, with additional documentation and case studies at https://www.seamlesswave.com/LISFLOOD8.0 (last access: 2 June 2021).

Flood Hazard Mapping with Distributed Hydrological Simulations and Remote-Sensed Slackwater Sediments in Ungauged Basins

“…Obviously, the new approach proposed in this work (Section 2.2.2) is more accurate than the former one because it accounts for the spatial variability of the precipitation. As a matter of fact, the novel approach yields not only the mean value (13) but also the standard deviation (14). We got σ BI PMR m = 0.92 and σ BI I PMR m = 1.83 mm•h −1 .…”

“…In the last decades, the application of distributed hydrological models has increased [9,[11][12][13][14] due to the speedup of the numerical simulations using GPU [10]. Here we combine the modelling of rainfall-runoff processes through a feedback loop with paleohydrology.…”

“…The number of sub-basins in BI were N = 22 with 0.68% ≤ w i ≤ 20% and, in BII, we got N = 19 with 0.7% ≤ w i ≤ 12%. The wide ranges of the weight values highlight the importance of accounting for the sub-basins areas in the weighted statistical variables PMR m (13) and σ PMR m (14).…”

“…Outstanding free software accelerated by multi-CPU and graphics processing unit (GPU) emerged in the last decade [10]. More recently, IBERPLUS [11], SERGHEI-SWE [12], TRITON [13] and LISFLOOD-FP [14]. Such distributed hydrological models use a physical-based approach to simulate complex physical processes leading to runoff, routing the water through all the elements of the drainage network, and generating even flood maps.…”

Locally Relevant High‐Resolution Hydrodynamic Modeling of River Floods at the Regional Scale

“…( 2019 ) conclude that numerical issues in these problematic regions may originate from simplifying the shallow water equations (SWEs) while discretizations of the full SWEs are not subject to these limitations. Second‐order finite‐volume (FV) schemes (Audusse & Bristeau, 2005 ; Hou et al., 2015 ; Murillo et al., 2008 ) or discontinuous Galerkin (DG) schemes (Kesserwani et al., 2008 ; Shaw et al., 2021 ; Vater et al., 2017 ) offer additional accuracy, however at the cost of higher runtimes. For large‐scale river flooding, the usage of a second‐order scheme instead of its first‐order counterpart may be preferrable in view of the workload–accuracy tradeoff (Horváth et al., 2020 ).…”