Articles | Volume 25, issue 7
https://doi.org/10.5194/nhess-25-2271-2025
https://doi.org/10.5194/nhess-25-2271-2025
Research article
 | 
08 Jul 2025
Research article |  | 08 Jul 2025

Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method

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

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Cited articles

Argudo, O., Chica, A., and Andujar, C.: Terrain Super-resolution through Aerial Imagery and Fully Convolutional Networks, Comput. Graph. Forum, 37, 101–110, https://doi.org/10.1111/cgf.13345, 2018. 
Arun, P. V.: A comparative analysis of different DEM interpolation methods, The Egyptian Journal of Remote Sensing and Space Science, 16, 133–139, https://doi.org/10.1016/j.ejrs.2013.09.001, 2013. 
Brock, J., Schratz, P., Petschko, H., Muenchow, J., Micu, M., and Brenning, A.: The performance of landslide susceptibility models critically depends on the quality of digital elevation models, Geomat. Nat. Haz. Risk, 11, 1075–1092, https://doi.org/10.1080/19475705.2020.1776403, 2020. 
Carrão, H., Gonçalves, P., and Caetano, M.: Contribution of multispectral and multitemporal information from MODIS images to land cover classification, Remote Sens. Environ., 112, 986–997, https://doi.org/10.1016/j.rse.2007.07.002, 2008. 
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Short summary
This study addresses the challenge of accurately predicting floods in regions with limited terrain data. By utilising 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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