Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method Zhu, Yue Burlando, Paolo Tan, Puay Yok Geiß, Christian Fatichi, Simone ddc:550 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 Publications 2025 info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_2df8fbb1 https://research-explorer.ista.ac.at/record/22469 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> eng info:eu-repo/semantics/altIdentifier/doi/10.5194/nhess-25-2271-2025 info:eu-repo/semantics/altIdentifier/issn/1561-8633 info:eu-repo/semantics/altIdentifier/e-issn/1684-9981 info:eu-repo/semantics/openAccess