Preprints
https://doi.org/10.5194/nhess-2024-207
https://doi.org/10.5194/nhess-2024-207
18 Nov 2024
 | 18 Nov 2024
Status: this preprint is currently under review for the journal NHESS.

Improving Pluvial Flood Simulations with Multi-source DEM Super-Resolution

Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi

Status: open (until 30 Dec 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2024-207', Anonymous Referee #1, 19 Nov 2024 reply
Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi
Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi

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Short summary
This study addresses the challenge of accurately predicting floods in regions with limited terrain data. By utilizing a deep learning model, we developed a method that improves the resolution of digital elevation data by fusing low-resolution elevation data with high-resolution satellite imagery. This approach not only substantially enhances flood prediction accuracy, but also holds potential for broader applications in simulating natural hazards that require terrain information.
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