{"year":"2025","intvolume":"        25","status":"public","issue":"7","volume":25,"has_accepted_license":"1","OA_type":"gold","article_processing_charge":"No","quality_controlled":"1","_id":"22469","page":"2271-2286","type":"journal_article","title":"Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method","abstract":[{"lang":"eng","text":"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."}],"doi":"10.5194/nhess-25-2271-2025","article_type":"original","OA_place":"publisher","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5194/nhess-25-2271-2025"}],"ddc":["550"],"oa":1,"PlanS_conform":"1","month":"07","author":[{"last_name":"Zhu","full_name":"Zhu, Yue","first_name":"Yue"},{"last_name":"Burlando","full_name":"Burlando, Paolo","first_name":"Paolo"},{"full_name":"Tan, Puay Yok","last_name":"Tan","first_name":"Puay Yok"},{"first_name":"Christian","full_name":"Geiß, Christian","last_name":"Geiß"},{"full_name":"Fatichi, Simone","last_name":"Fatichi","first_name":"Simone","id":"cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6"}],"DOAJ_listed":"1","date_updated":"2026-07-30T11:49:35Z","oa_version":"Published Version","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_published":"2025-07-08T00:00:00Z","publication_identifier":{"eissn":["1684-9981"],"issn":["1561-8633"]},"day":"08","language":[{"iso":"eng"}],"scopus_import":"1","publication_status":"published","publisher":"Copernicus Publications","das_tickbox":"1","date_created":"2026-07-27T12:30:23Z","extern":"1","publication":"Natural Hazards and Earth System Sciences","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"apa":"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>","ama":"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>","ista":"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.","short":"Y. Zhu, P. Burlando, P.Y. Tan, C. Geiß, S. Fatichi, Natural Hazards and Earth System Sciences 25 (2025) 2271–2286.","chicago":"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>.","ieee":"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.","mla":"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>."}}