the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving Pluvial Flood Simulations with Multi-source DEM Super-Resolution
Abstract. Due to the limited availability of high-resolution topographic data, accurate flood simulation remains a significant challenge in many flood-prone regions, particularly in developing countries and in urban domains. While publicly available Digital Elevation Model (DEM) datasets are increasingly accessible, their spatial resolution is often insufficient for reflecting fine-scaled elevation details, which hinders the ability to simulate effectively pluvial floods in built environments. To address this issue, we implemented a deep learning-based method, which efficiently enhances the spatial resolution of DEM data, and quantified the improvement in flood simulation. The method employs a tailored multi-source input module, enabling it to effectively integrate and learn from diverse data sources. By utilizing publicly open global datasets, low-resolution DEM datasets (such as the 30 m SRTM) in conjunction with high-resolution multispectral imagery (e.g., Sentinel-2A), our approach allows to produce a super-resolution DEM, which exhibits superior performance compared to conventional methods in reconstructing 10 m DEM data based on 30 m DEM data and 10 m multispectral satellite images. Such superior performance translates, when applied to pluvial flood simulations, into significantly improved accuracy of floodwater depth and inundation area predictions compared to existing alternatives. This study underscores the practical value of machine-learning techniques that leverage publicly available global datasets to generate DEMs that allow enhancing flood simulations.
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Status: open (until 30 Dec 2024)
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RC1: 'Comment on nhess-2024-207', Anonymous Referee #1, 19 Nov 2024
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The paper introduces a deep-learning method that combines low reolution DEM and multi-spectral images to obtain a high-resolution DEM that is ultimately used for running a pluvial flood simulation. The authors also compare this approach with other DL and not methods showing its improved efficacy.
The manuscript is well written, clear, concise, and informative. As such I recommend publication with just few minor details that might further improve the quality of the paper.
Minor comments:
In the results/discussion section, I would emphasize that the difference between the RCAN and the RCAN-MS is mainly in the inputs used (if I followed everything correctly), thus further proving your point that the extra information coming from multi-spectral images is beneficial, since so far it seemed "just" a difference in method as you have with VDSR, for example.
Could you explain why do the results in terms of flood simulations look more consistent in Dataset II rather than in Dataset I, at least visually?
For example, in Figure 6, all interpolation methods seem to produce some sort of accumulation ponds in correspondence of the bifurcations of the rivers and the bicubic approximation results in a noisy pattern. However, that does not seem the case for Figure 8 with Dataset II. Do you have any clue why?I think you could also comment further on why is the IoU very low (despite the proportional increase) for high thresholds of water depths.
In terms of metrics you could also consier adding a different metric such as the critical success index (CSI), which has been used in several flood studies.While most figures are of high quality, I think Figure 7 and 9 can be better, despite already being informative. Consider changing their style.
Citation: https://doi.org/10.5194/nhess-2024-207-RC1
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