the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Mar 2025
Brief communication: Depth-averaging of 3D depth-resolved MPM simulation results of geophysical flows for GIS visualization
Abstract. Significant advances in full-3D modeling of geophysical flows have provided deeper insights into complex processes and predictive potential. However, practical application in the natural hazard community remains limited due to inadequate GIS integration of simulation results. This study addresses the oxymoronic transformation of 3D depth-resolved MPM simulation outputs into simplified depth-averaged results, such as flow depth and thickness, and slope-parallel and slope-normal velocities. Specifically, we present an algorithm that rasterizes scattered MPM outputs into a 2D format, enhancing their utility for hazard mapping and mitigation. We demonstrate our approach by applying it to an ice avalanche event, which is simulated using MPM and visualized in GIS.
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Status: open (until 07 May 2025)
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CC1: 'Comment on egusphere-2024-3421', Zheng Han, 22 Apr 2025
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In this brief communication, the authors discuss an algorithm for visualizing 3D-MPM simulation results of geophysical flows within a GIS platform. The algorithm effectively bridges the gap between the absolute-coordinate-based representation used in 3D-MPM simulations and the topography-linked expressions widely adopted in hazard prevention and mitigation. This contribution enhances the applicability of 3D-MPM in disaster analysis. In general, the manuscript is well-structured and clearly written, while the algorithmic framework is logically developed. Below are my detailed comments after reviewing this brief communication.
(1) The title of this brief communication is somewhat confused, since the descriptions of “depth averaging” are unclear to the readers, who cannot to access what the purpose of this study is. In my opinion, the major contribution of this study is to output the 3D-MPM into a 2D format for visualization within a GIS platform.
(2) Page 2, Line 26-30. The authors summarize five bullets of problems in the practical application of 3D models. It is good, however, that bullets from i to iv are not relevant to the topic of this study. I am interested in bullet iv, but it is a pity that the authors did not provide more information on why the integration of 3D simulation results with GIS tools is difficult. I think the more detailed illustration of this issue would be beneficial for the readers to know the significance of this study.
(3) Page 2, Line 31. The description of “depth-resolved” appears unclear and may even cause confusion that the MPM model in this paper is 2D-based. I suggest replacing it with the more intuitive expression “fully 3D” or "3D" which also aligns better with the subsequent use of “3D MPM” throughout the manuscript.
(4) Page 2, Line 33. The authors have mentioned that a very similar tool by Su et al. (2024) has been reported. I agree that each model, e.g., MPM and SPH, should require dedicated algorithms; however, the connections and similarities between the methods in the brief communication and the previous paper should be illustrated in the introduction section or better discussed and compared in a separate discussion section.
(5) The title of section 2.2 is too short, making it difficult to know the purpose of this section. It is better to replace it with “topography representation”.
(6) Page 4, Line 75-80. The algorithm to determine the cell to which each particle belongs is quite similar to the previous one that has already been presented in Han et al. (2020). The citation should be added. [Han, Z., Su, B., Li, Y.G., Dou, J., Wang, W.D., Zhao, L.H., 2020. Modeling the progressive entrainment of bed sediment by viscous debris flows using the three-dimensional SC-HBP-SPH method. Water Res. 116031. ttps://doi.org/10.1016/j. watres.2020.116031]
(7) Page 4, Line 90. As equation 7 shows, the flow height of the DTM’s cell is determined by the largest Z-position of the particles in the cell. Does this dealing way overestimate the flow height? Imagine that 100 particles have been recognized in the cell, 99 have a smaller Z-position, but the last 1 has a greater Z-position. Is it rational to take the largest Z-position for calculating flow height in the cell?
(8) Page 4, Line 95-98. I am confused about the physical difference between maximum flow depth and maximum flow thickness in this context. Intuitively, they appear to represent the same quantity. I suggest the authors clearly illustrate the relationship between the two in Figure 1 for better clarity. Is the difference only apparent when particles detach from the ground and scatter into the air?
(9) Page 6, Line 130-135. I agree with the authors that cell size is difficult to choose. I am also glad to see that the authors have provided a preliminary criterion for estimating cell size. However, the theoretical basis for this equation is unclear. I would appreciate it if the authors could provide more information on it.
(10) Page 7, Line 160-175. The explanation of the flow behavior is too detailed. I suggest the authors could make it compact and brief, because it is not the key of this brief communication.
(11) Page 9, Line 207-208. As the authors commented, it is right that the algorithm in Su et al. (2024) saves the result on boundary particles. However, it is not precise. As in Han et al. (2020), see the reference in bullet 6 of my comment, the flow depth and basal velocities along both directions are exported in accordance with 2D rasterized grid format. The improvement and major contribution of the authors regard the promotion of this kind of idea to the complex topography and GIS platform.
Citation: https://doi.org/10.5194/egusphere-2024-3421-CC1
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