article published yes Yue Zhu author Paolo Burlando author Puay Yok Tan author Christian Geiß author Simone Fatichi author cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6 Accurate flood simulation remains a significant challenge in many flood-prone regions, particularly in developing countries and urban areas, where the availability of high-resolution topographic data is especially limited. 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 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 effect of the improved DEM on flood simulation. The method employs a tailored multi-source input module, enabling it to effectively integrate and learn from diverse data sources. By utilising publicly accessible global datasets, such as low-resolution DEM datasets (i.e. 30 m Shuttle Radar Topography Mission, SRTM) in conjunction with high-resolution multispectral imagery (e.g. Sentinel-2A), our approach allows us 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. We evaluated the performance of the super-resolution DEM in flood simulations. Compared to conventional methods (e.g. bicubic interpolation), the simulation results demonstrated that our approach significantly improved the accuracy of flood simulations, with a reduction in the mean absolute error of floodwater depth of about 13.1 % and an increase in the intersection over union (IoU) for inundation area predictions of about 46 %. Accordingly, this study underscores the practical value of machine learning techniques that leverage publicly available global datasets to generate DEMs that allow for the enhancement of flood simulations. Copernicus Publications2025 eng 1561-8633 1684-998110.5194/nhess-25-2271-2025 2572271-2286 yes Zhu, Yue, et al. “Improving Pluvial Flood Simulations with a Multi-Source Digital Elevation Model Super-Resolution Method.” <i>Natural Hazards and Earth System Sciences</i>, vol. 25, no. 7, Copernicus Publications, 2025, pp. 2271–86, doi:<a href="https://doi.org/10.5194/nhess-25-2271-2025">10.5194/nhess-25-2271-2025</a>. Zhu, Y., Burlando, P., Tan, P. Y., Geiß, C., &#38; Fatichi, S. (2025). Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method. <i>Natural Hazards and Earth System Sciences</i>. Copernicus Publications. <a href="https://doi.org/10.5194/nhess-25-2271-2025">https://doi.org/10.5194/nhess-25-2271-2025</a> Zhu Y, Burlando P, Tan PY, Geiß C, Fatichi S. 2025. Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method. Natural Hazards and Earth System Sciences. 25(7), 2271–2286. Zhu Y, Burlando P, Tan PY, Geiß C, Fatichi S. Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method. <i>Natural Hazards and Earth System Sciences</i>. 2025;25(7):2271-2286. doi:<a href="https://doi.org/10.5194/nhess-25-2271-2025">10.5194/nhess-25-2271-2025</a> Y. Zhu, P. Burlando, P.Y. Tan, C. Geiß, S. Fatichi, Natural Hazards and Earth System Sciences 25 (2025) 2271–2286. Zhu, Yue, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi. “Improving Pluvial Flood Simulations with a Multi-Source Digital Elevation Model Super-Resolution Method.” <i>Natural Hazards and Earth System Sciences</i>. Copernicus Publications, 2025. <a href="https://doi.org/10.5194/nhess-25-2271-2025">https://doi.org/10.5194/nhess-25-2271-2025</a>. Y. Zhu, P. Burlando, P. Y. Tan, C. Geiß, and S. Fatichi, “Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method,” <i>Natural Hazards and Earth System Sciences</i>, vol. 25, no. 7. Copernicus Publications, pp. 2271–2286, 2025. 224692026-07-27T12:30:23Z2026-07-30T11:49:35Z