the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Mar 2024
Effects of spatial resolution of digital terrain obtained by drone on mountainous urban fluvial flood modelling
Abstract. The effect of resolution and quality of terrain data, as the most sensitive input to 2D hydrodynamic modelling, has been one of the main research areas in flood modelling. However, previous studies have lacked the discussion on the limitation of the target area and the data source, as well as the underlying causes of simulation bias due to different resolutions. This study first discusses the performance of high-resolution DSM acquired by drone for flood modelling in a mountainous riverine city, and the effect of DSM resolution on results using grid resolutions from 6 cm to 30 m. The study then investigates the root causes of the effect based on topographic attributes. Xuanhan city, a riverine city in the mountainous region of southwest China, was used as the study area. The HEC-RAS 2D model was used for all simulations, and the results generated using 6 cm DSM acquired by drone were used as a benchmark. Results show that the simulation effect of flood characteristics shows a certain step change with the change of DSM resolution. DSMs with a resolution within 10 m can better capture the undulating features of the topography in the study area, which is crucial for the modelling of the inundation area. However, if features with specific elevation difference values are involved, it is best to keep the resolution within 5 m, which will have a direct impact on the accuracy of the modelling of the flood depth. The analysis of topographic attributes provides theoretical support for obtaining the optimal resolution to match simulation requirements.
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CC1: 'Comment on egusphere-2024-404', Ziqi Yan, 28 Mar 2024
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The application of high-resolution terrain data in flood forecasting and warning has been a hot research topic in this field. The most important difficulties in practical application are limitation of the cost of computing power and the operability.。The authors present a comprehensive exploration of drone-based acquisition of high-precision Digital Surface Models in the mountainous riverside city. The study delves into the influence of varying DSM resolutions on flood inundation simulations and investigates the fundamental factors driving these effects. The topic is interesting and the technical soundness of the method is reasonable, the contents are valuable for researchers and managers involved in the work. For better understanding of the methodology and application, some parts are still recommended to discussed and illustrated more, as follows:
- In Section 2.2, it is recommended to provide specific details regarding the setup of drone flight missions. This could include information on route planning and the precise positioning of control points. Additionally, in the results and discussion sections, it would be valuable to present the outcomes of drone aerial surveys (such as errors associated with ground control points) and the effectiveness of post-processing products. Such elaboration is crucial for readers to understand the workflow of drone operations and to establish the reliability of the result.
- When extracting and analyzing topographic attributes, the choice of using the average value within a grid cell as the central point’s indicator value is reasonable. The side length of grid cell was set at 30 m, and it is worth explaining the rationale behind this selection. If the goal is to ensure that each grid cell encompasses multiple complete raster pixels, opting for a larger side length than 30 m might be more reasonable
Citation: https://doi.org/10.5194/egusphere-2024-404-CC1 -
RC1: 'Comment on egusphere-2024-404', Anonymous Referee #1, 18 Apr 2024
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Summary: This study presents a 2D flood inundation modeling study in China. Authors investigate how the quality of flood inundation simulations depends on the resolution of the underlying digital elevation model. A high-resolution (6 cm ground resolution) DEM is derived from airborne imagery and flood simulations are performed at different resolutions ranging from 6 cm to 30 m. Results presented in this paper are interesting for the flood inundation modeling community and I recommend publication of this article after revisions, as outlined below.
Review comments:
- Improve language and grammar. The text is not always easy to read. Use ChatGPT, Google gemini or similar to improve your text, it will make the paper more accessible and impactful.
- Authors have used photogrammetry techniques to derive the DEM from drone-borne imagery. Another option would be drone-borne lidar. I think the paper would benefit from a short summary of these available options, pros and cons of each option and a few references illustrating those.
- A key step for hydraulic modeling purposes is generating the terrain model from the surface model, i.e. DSM to DTM conversion. Main factors in this are buildings and vegetation. The article explains that buildings were not removed, which I agree with – how was vegetation handled?
- As pointed out in the paper, submerged topography cannot be derived from photogrammetric DEMs. However, there are options to get bathymetric information from UAS, including green lidar, water penetrating radar and sonar. Those recent advances could be briefly summarized in the paper.
- A key result of the paper is to show how the quality of flood simulations depends on the DEM resolution. It is perhaps not surprising that quality increases with increasing resolution. One key factor that limits what is possible, at least for larger domains, is CPU time and computing resources. It would thus be informative to see those parameters for the different model runs at different spatial resolution.
- Authors introduce and calculate quantitative terrain indicators called topographic features here. They show that these indicators depend on the spatial resolution of the DEM and that the mismatch between the features calculated at native and coarsened resolution increases with decreasing resolution. Further, they argue that degradation of the terrain indicators with decreasing resolution is similar to the degradation of the flood simulation results with decreasing resolution. It would be good to analyze this further: What is the correlation between different topographic features and skill of the flood simulation? Are there specific thresholds for the feature mismatches, exceedance of which would cause the skill of the flood simulation to decrease? Are these findings site-independent and transferable?
Details:
- Avoid acronyms in abstract or write out before first use (DSM)
- Line 17: Replace “within” with “better than”
- Line 18-20: Please rephrase sentence “However…. flood depth”. Unclear
- Line 29: El Nino is not induced by global warming – rephrase
- Line 50: Be consistent with the terms DSM, DEM and DTM. DTM is the main input to flood models.
- Line 141 ff: There are drone-borne bathymetry options (https://doi.org/10.5194/hess-22-4165-2018, https://doi.org/10.1016/j.jhydrol.2022.128789)
- Line 204: Which model parameters?
- Fig 7: Legend items are mis-spelled
- Fig 9: Provide units for y-axis (absolute error)
Citation: https://doi.org/10.5194/egusphere-2024-404-RC1
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