the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An improved subgrid channel model with upwind form artificial diffusion for river hydrodynamics and floodplain inundation simulation
Paul Bates
Jeffrey Neal
Abstract. An accurate estimation of river channel conveyance capacity and the water exchange at the river-floodplain interfaces is pivotal for flood modelling. However, in large-scale models limited grid resolution often means that small-scale river channel features cannot be well represented in traditional 1D/2D schemes. As a result instability over river and floodplain boundaries can occur, and flow connectivity, which has a strong control on the floodplain hydraulics, is not well-approximated. A subgrid channel model (SGC) based on the local inertial form of the shallow water equations, which allows utilization of approximated sub-grid scale bathymetric information while performing very efficient computations has been proposed as a solution, and it has been widely applied to calculate the wetting and drying dynamics in river-floodplain systems at regional scales. Unfortunately, SGC approaches to date have not included latest developments in numerical solutions of the local inertial equations, and the original solution scheme was reported to suffer from numerical instability in low friction regions such as urban areas. In this paper, for the first time, we implement a newly developed diffusion and explicit adaptive weighting factor in the SGC model. An adaptive artificial diffusion is explicitly included in the form of an upwind solution scheme based on the local flow status to improve the numerical flux estimation. A structured sequence of numerical experiments is performed, and the results confirm that the new SGC model improved the model performance in terms of water level and inundation extent, especially in urban areas where the Manning parameter is less than 0.03 m−1/3 s. By not compromising computational efficiency, this improved SGC model is a compelling alternative for river-floodplain modelling, particularly in large-scale applications.
Youtong Rong et al.
Status: open (extended)
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RC1: 'Comment on gmd-2022-234', Anonymous Referee #1, 06 Feb 2023
reply
This manuscript proposed a newly developed diffusion and explicit adaptive weighting scheme for river channel hydrodynamics modelling. It aims to tackle instability over river and floodplain interfaces. The weighing factor is adapted depending on local velocity, flow depth, grid resolution, and time step size. A constraint on the available range of the weighting factor is used to balance the contribution of the flux from the local and upwind surfaces. It can thus provide an improved and computationally efficient solution for river-floodplain flow inundation simulations with multi-core CPUs. The potential capability of the proposed subgrid channel model has been demonstrated in 5 case studies including realistic scenarios of flooding in Glasgow (2002) and Carlisle (2005). The paper is well written. I recommend the paper to be published in a revised version.
Minor revisions:
- Please explain DEM
- In line 155, "the weighting factor 𝜃 defined in equation (6)". Here it should be equation (4). Again, in line 161, replace equation (6) with equation (4).
- In line 180, please replace “gird” with grid.
Citation: https://doi.org/10.5194/gmd-2022-234-RC1 -
AC1: 'Reply on RC1', Youtong Rong, 06 Feb 2023
reply
Thank you very much for your positive comments on the manuscript. Our paper has been revised and checked carefully. Here we reply to these comments from the reviewer point-by-point
1, DEM in this paper refers to Digital Elevation Model, which is a grid-based representation of the bare ground topographic surface in an area. In our improved subgrid channel model, the DEM (bottom boundary conditions) together with the discharge from previous time step controls the discharge exchange in two neighbouring grids, which helps to predict the direction and velocity of water flow during a flood event. Added regarding the DEM that we will define this acronym when first introduced for clarity.
2, Corrected as suggested.
3, Corrected as suggested.
Citation: https://doi.org/10.5194/gmd-2022-234-AC1
Youtong Rong et al.
Youtong Rong et al.
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