Articles | Volume 25, issue 7
https://doi.org/10.5194/nhess-25-2271-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-2271-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method
Yue Zhu
CORRESPONDING AUTHOR
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Future Cities Laboratory, Singapore-ETH Centre, Singapore
Paolo Burlando
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Puay Yok Tan
Department of Architecture, National University of Singapore, Singapore
Christian Geiß
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Wessling, Germany
Department of Geography, University of Bonn, Bonn, Germany
Simone Fatichi
Department of Civil and Environmental Engineering, National University of Singapore, Singapore
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Jianning Ren, Zhaoyang Luo, Xiangzhong Luo, Stefano Galelli, Athanasios Paschalis, Valeriy Ivanov, Shanti Shwarup Mahto, and Simone Fatichi
EGUsphere, https://doi.org/10.5194/egusphere-2025-4570, https://doi.org/10.5194/egusphere-2025-4570, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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Southeast Asia’s water and carbon fluxes remain poorly understood due to limited field observations and modelling. Using available data and computer models, we show the region is mostly energy-limited: evapotranspiration is controlled by relative humidity, while plant productivity is driven by solar radiation. In some particular areas, such as the Tibetan Plateau, savannas, and dry deciduous forests, water availability is the main limiting factor.
Shanti Shwarup Mahto, Simone Fatichi, and Stefano Galelli
Earth Syst. Sci. Data, 17, 2693–2712, https://doi.org/10.5194/essd-17-2693-2025, https://doi.org/10.5194/essd-17-2693-2025, 2025
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The MSEA-Res database offers an open-access dataset tracking absolute water storage for 186 large reservoirs across Mainland Southeast Asia from 1985 to 2023. It provides valuable insights into how reservoir storage grew by 130 % between 2008 and 2017, driven by dams in key river basins. Our data also reveal how droughts, like the 2019–2020 event, significantly impacted water reservoirs. This resource can aid water management, drought planning, and research globally.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Yiran Wang, Naika Meili, and Simone Fatichi
Hydrol. Earth Syst. Sci., 29, 381–396, https://doi.org/10.5194/hess-29-381-2025, https://doi.org/10.5194/hess-29-381-2025, 2025
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In this study, we use climate model simulations and process-based ecohydrological modeling to assess the effects of solar radiation changes on hydrological variables. Results show that direct changes in solar radiation without the land–atmosphere feedback primarily affects sensible heat with limited effects on hydrology and vegetation. However, including land–atmosphere feedbacks exacerbates the effects of radiation changes on evapotranspiration and modifies vegetation productivity.
Elisabeth Schoepfer, Jörn Lauterjung, Torsten Riedlinger, Harald Spahn, Juan Camilo Gómez Zapata, Christian D. León, Hugo Rosero-Velásquez, Sven Harig, Michael Langbein, Nils Brinckmann, Günter Strunz, Christian Geiß, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci., 24, 4631–4660, https://doi.org/10.5194/nhess-24-4631-2024, https://doi.org/10.5194/nhess-24-4631-2024, 2024
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In this paper, we provide a brief introduction of the paradigm shift from managing disasters to managing risks, followed by single-hazard to multi-risk assessment. We highlight four global strategies that address disaster risk reduction and call for action. Subsequently, we present a conceptual approach for multi-risk assessment which was designed to serve potential users like disaster risk managers, urban planners or operators of critical infrastructure to increase their capabilities.
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024, https://doi.org/10.5194/hess-28-3391-2024, 2024
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Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Christian Geiß, Jana Maier, Emily So, Elisabeth Schoepfer, Sven Harig, Juan Camilo Gómez Zapata, and Yue Zhu
Nat. Hazards Earth Syst. Sci., 24, 1051–1064, https://doi.org/10.5194/nhess-24-1051-2024, https://doi.org/10.5194/nhess-24-1051-2024, 2024
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We establish a model of future geospatial population distributions to quantify the number of people living in earthquake-prone and tsunami-prone areas of Lima and Callao, Peru, for the year 2035. Areas of high earthquake intensity will experience a population growth of almost 30 %. The population in the tsunami inundation area is estimated to grow by more than 60 %. Uncovering those relations can help urban planners and policymakers to develop effective risk mitigation strategies.
Stefano Manzoni, Simone Fatichi, Xue Feng, Gabriel G. Katul, Danielle Way, and Giulia Vico
Biogeosciences, 19, 4387–4414, https://doi.org/10.5194/bg-19-4387-2022, https://doi.org/10.5194/bg-19-4387-2022, 2022
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Increasing atmospheric carbon dioxide (CO2) causes leaves to close their stomata (through which water evaporates) but also promotes leaf growth. Even if individual leaves save water, how much will be consumed by a whole plant with possibly more leaves? Using different mathematical models, we show that plant stands that are not very dense and can grow more leaves will benefit from higher CO2 by photosynthesizing more while adjusting their stomata to consume similar amounts of water.
Michael Schirmer, Adam Winstral, Tobias Jonas, Paolo Burlando, and Nadav Peleg
The Cryosphere, 16, 3469–3488, https://doi.org/10.5194/tc-16-3469-2022, https://doi.org/10.5194/tc-16-3469-2022, 2022
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Rain is highly variable in time at a given location so that there can be both wet and dry climate periods. In this study, we quantify the effects of this natural climate variability and other sources of uncertainty on changes in flooding events due to rain on snow (ROS) caused by climate change. For ROS events with a significant contribution of snowmelt to runoff, the change due to climate was too small to draw firm conclusions about whether there are more ROS events of this important type.
Stefan Fugger, Catriona L. Fyffe, Simone Fatichi, Evan Miles, Michael McCarthy, Thomas E. Shaw, Baohong Ding, Wei Yang, Patrick Wagnon, Walter Immerzeel, Qiao Liu, and Francesca Pellicciotti
The Cryosphere, 16, 1631–1652, https://doi.org/10.5194/tc-16-1631-2022, https://doi.org/10.5194/tc-16-1631-2022, 2022
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The monsoon is important for the shrinking and growing of glaciers in the Himalaya during summer. We calculate the melt of seven glaciers in the region using a complex glacier melt model and weather data. We find that monsoonal weather affects glaciers that are covered with a layer of rocky debris and glaciers without such a layer in different ways. It is important to take so-called turbulent fluxes into account. This knowledge is vital for predicting the future of the Himalayan glaciers.
Martina Botter, Matthias Zeeman, Paolo Burlando, and Simone Fatichi
Biogeosciences, 18, 1917–1939, https://doi.org/10.5194/bg-18-1917-2021, https://doi.org/10.5194/bg-18-1917-2021, 2021
Lianyu Yu, Simone Fatichi, Yijian Zeng, and Zhongbo Su
The Cryosphere, 14, 4653–4673, https://doi.org/10.5194/tc-14-4653-2020, https://doi.org/10.5194/tc-14-4653-2020, 2020
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The role of soil water and heat transfer physics in portraying the function of a cold region ecosystem was investigated. We found that explicitly considering the frozen soil physics and coupled water and heat transfer is important in mimicking soil hydrothermal dynamics. The presence of soil ice can alter the vegetation leaf onset date and deep leakage. Different complexity in representing vadose zone physics does not considerably affect interannual energy, water, and carbon fluxes.
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.
Blöschl, G., Buttinger-Kreuzhuber, A., Cornel, D., Eisl, J., Hofer, M., Hollaus, M., Horváth, Z., Komma, J., Konev, A., Parajka, J., Pfeifer, N., Reithofer, A., Salinas, J., Valent, P., Výleta, R., Waser, J., Wimmer, M. H., and Stiefelmeyer, H.: Hyper-resolution flood hazard mapping at the national scale, Nat. Hazards Earth Syst. Sci., 24, 2071–2091, https://doi.org/10.5194/nhess-24-2071-2024, 2024.
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.
Chen, Z., Qin, Q., Lin, L., Liu, Q., and Zhan, W.: DEM Densification Using Perspective Shape From Shading Through Multispectral Imagery, IEEE Geosci. Remote S., 10, 145–149, https://doi.org/10.1109/LGRS.2012.2195471, 2013.
Demiray, B. Z., Sit, M., and Demir, I.: DEM Super-Resolution with EfficientNetV2, arXiv [preprint], https://doi.org/10.48550/arXiv.2109.09661, 20 September 2021a.
Demiray, B. Z., Sit, M., and Demir, I.: D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks, SN Comput. Sci., 2, 48, https://doi.org/10.1007/s42979-020-00442-2, 2021b.
Deng, F., Rodgers, M., Xie, S., Dixon, T. H., Charbonnier, S., Gallant, E. A., López Vélez, C. M., Ordoñez, M., Malservisi, R., Voss, N. K., and Richardson, J. A.: High-resolution DEM generation from spaceborne and terrestrial remote sensing data for improved volcano hazard assessment – A case study at Nevado del Ruiz, Colombia, Remote Sens. Environ., 233, 111348, https://doi.org/10.1016/j.rse.2019.111348, 2019.
Dong, C., Loy, C. C., He, K., and Tang, X.: Image Super-Resolution Using Deep Convolutional Networks, IEEE T. Pattern Anal., 38, 295–307, https://doi.org/10.1109/TPAMI.2015.2439281, 2016.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Gao, B. and Yue, L.: DEM Super-Resolution Assisted by Remote Sensing Images Content Feature, in: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS), 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS), Wuhan, China, 12–14 April 2024, IEEE, 122–126, https://doi.org/10.1109/ICGMRS62107.2024.10581275, 2024.
Geiß, C., Wurm, M., Breunig, M., Felbier, A., and Taubenböck, H.: Normalization of TanDEM-X DSM data in urban environments with morphological filters, IEEE T. Geosci. Remote, 53, 4348–4362, https://doi.org/10.1109/TGRS.2015.2396195, 2015.
Ghimire, B., Chen, A. S., Guidolin, M., Keedwell, E. C., Djordjević, S., and Savić, D. A.: Formulation of a fast 2D urban pluvial flood model using a cellular automata approach, J. Hydroinform., 15, 676, 2013.
Guidolin, M., Chen, A. S., Ghimire, B., Keedwell, E. C., Djordjević, S., and Savić, D. A.: A weighted cellular automata 2D inundation model for rapid flood analysis, Environ. Modell. Softw., 84, 378–394, https://doi.org/10.1016/j.envsoft.2016.07.008, 2016.
Guo, K., Guan, M., and Yu, D.: Urban surface water flood modelling – a comprehensive review of current models and future challenges, Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, 2021.
Hawker, L., Bates, P., Neal, J., and Rougier, J.: Perspectives on Digital Elevation Model (DEM) Simulation for Flood Modeling in the Absence of a High-Accuracy Open Access Global DEM, Frontiers in Earth Science, 6, 233, https://doi.org/10.3389/feart.2018.00233, 2018.
Jabbar, A., Li, X., and Omar, B.: A Survey on Generative Adversarial Networks: Variants, Applications, and Training, ACM Comput. Surv., 54, 157, https://doi.org/10.1145/3463475, 2021.
Jiang, Y., Xiong, L., Hua ng, X., Li, S., and Shen, W.: Super-resolution for terrain modeling using deep learning in high mountain Asia, Int. J. Appl. Earth Obs., 118, 103296, https://doi.org/10.1016/j.jag.2023.103296, 2023.
Kim, J., Lee, J. K., and Lee, K. M.: Accurate Image Super-Resolution Using Very Deep Convolutional Networks, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, IEEE, 1646–1654, https://doi.org/10.1109/CVPR.2016.182, 2016.
Kubade, A. A., Sharma, A., and Rajan, K. S.: Feedback Neural Network Based Super-Resolution of DEM for Generating High Fidelity Features, in: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26 September–2 October 2020, IEEE, 1671–1674, https://doi.org/10.1109/IGARSS39084.2020.9323310, 2020.
Li, C., Shao, Z., Zhang, L., Huang, X., and Zhang, M.: A comparative analysis of index-based methods for impervious surface mapping using multiseasonal Sentinel-2 satellite data, IEEE J. Sel. Top. Appl., 14, 3682–3694, https://doi.org/10.1109/JSTARS.2021.3067325, 2021.
Li, J., Hong, D., Gao, L., Yao, J., Zheng, K., Zhang, B., and Chanussot, J.: Deep learning in multimodal remote sensing data fusion: A comprehensive review, Int. J. Appl. Earth Obs., 112, 102926, https://doi.org/10.1016/j.jag.2022.102926, 2022.
Li, Z., Zhu, X., Yao, S., Yue, Y., García-Fernández, Á. F., Lim, E. G., and Levers, A.: A large scale Digital Elevation Model super-resolution Transformer, Int. J. Appl. Earth Obs., 124, 103496, https://doi.org/10.1016/j.jag.2023.103496, 2023.
Ling, F. and Foody, G. M.: Super-resolution land cover mapping by deep learning, Remote Sens. Lett., 10, 598–606, https://doi.org/10.1080/2150704X.2019.1587196, 2019.
Liu, H., Wang, Y., Zhang, C., Chen, A. S., and Fu, G.: Assessing real options in urban surface water flood risk management under climate change, Nat. Hazards, 94, 1–18, https://doi.org/10.1007/s11069-018-3349-1, 2018.
Liu, X., Jiao, L., Li, L., Tang, X., and Guo, Y.: Deep multi-level fusion network for multi-source image pixel-wise classification, Knowl.-Based Syst., 221, 106921, https://doi.org/10.1016/j.knosys.2021.106921, 2021.
Löwe, R. and Arnbjerg-Nielsen, K.: Urban pluvial flood risk assessment – data resolution and spatial scale when developing screening approaches on the microscale, Nat. Hazards Earth Syst. Sci., 20, 981–997, https://doi.org/10.5194/nhess-20-981-2020, 2020.
Lu, W., Tao, C., Li, H., Qi, J., and Li, Y.: A unified deep learning framework for urban functional zone extraction based on multi-source heterogeneous data, Remote Sens. Environ., 270, 112830, https://doi.org/10.1016/j.rse.2021.112830, 2022.
Malgwi, M. B., Fuchs, S., and Keiler, M.: A generic physical vulnerability model for floods: review and concept for data-scarce regions, Nat. Hazards Earth Syst. Sci., 20, 2067–2090, https://doi.org/10.5194/nhess-20-2067-2020, 2020.
Marsh, C. B., Harder, P., and Pomeroy, J. W.: Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment, Environ. Res. Commun., 5, 031009, https://doi.org/10.1088/2515-7620/acc56d, 2023.
MIDAS: UK Sub-hourly Rainfall Data, NCAS British Atmospheric Data Centre [data set], https://catalogue.ceda.ac.uk/uuid/455f0dd48613dada7bfb0ccfcb7a7d41, last access: 6 March 2024.
Miller, A., Sirguey, P., Morris, S., Bartelt, P., Cullen, N., Redpath, T., Thompson, K., and Bühler, Y.: The impact of terrain model source and resolution on snow avalanche modeling, Nat. Hazards Earth Syst. Sci., 22, 2673–2701, https://doi.org/10.5194/nhess-22-2673-2022, 2022.
NASA JPL: NASA Shuttle Radar Topography Mission Global 30 Arc Second, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MEASURES/SRTM/SRTMGL30.002, 2013.
Okolie, C. J. and Smit, J. L.: A systematic review and meta-analysis of Digital elevation model (DEM) fusion: pre-processing, methods and applications, ISPRS J. Photogramm., 188, 1–29, https://doi.org/10.1016/j.isprsjprs.2022.03.016, 2022.
Paul, S. and Gupta, A.: High-resolution Multi-spectral Image Guided DEM Super-resolution using Sinkhorn Regularized Adversarial Network, arXiv [preprint], https://doi.org/10.48550/arXiv.2311.16490, 20 September 2024.
Rahman, M. A. and Wang, Y.: Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation, in: Advances in Visual Computing, Cham, 234–244, https://doi.org/10.1007/978-3-319-50835-1_22, 2016.
Rees, W. G.: The accuracy of Digital Elevation Models interpolated to higher resolutions, Int. J. Remote Sens., 21, 7–20, https://doi.org/10.1080/014311600210957, 2000.
Sanders, B. F., Wing, O. E. J., and Bates, P. D.: Flooding is Not Like Filling a Bath, Earth's Future, 12, e2024EF005164, https://doi.org/10.1029/2024EF005164, 2024.
Shang, C., Li, X., Foody, G. M., Du, Y., and Ling, F.: Superresolution Land Cover Mapping Using a Generative Adversarial Network, IEEE Geosci. Remote S., 19, 1–5, https://doi.org/10.1109/LGRS.2020.3020395, 2022.
Shen, R., Huang, A., Li, B., and Guo, J.: Construction of a drought monitoring model using deep learning based on multi-source remote sensing data, Int. J. Appl. Earth Obs., 79, 48–57, https://doi.org/10.1016/j.jag.2019.03.006, 2019.
Spoto, F., Sy, O., Laberinti, P., Martimort, P., Fernandez, V., Colin, O., Hoersch, B., and Meygret, A.: Overview Of Sentinel-2, in: 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012, IEEE, 1707–1710, https://doi.org/10.1109/IGARSS.2012.6351195, 2012.
Tabari, H.: Climate change impact on flood and extreme precipitation increases with water availability, Sci. Rep., 10, 13768, https://doi.org/10.1038/s41598-020-70816-2, 2020.
Tan, W., Qin, N., Zhang, Y., McGrath, H., Fortin, M., and Li, J.: A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling, Remote Sens. Environ., 301, 113956, https://doi.org/10.1016/j.rse.2023.113956, 2024.
Tang, C. S. C. and Cheung, S. P. Y.: Frequency Analysis of Extreme Rainfall Values (GEO Report No. 261), Geotechnical Engineering Office, Hong Kong, 2011.
UK Environment Agency: LIDAR Composite DTM 2019 – 10 m, https://environment.data.gov.uk/dataset/ce8fe7e7-bed0-4889-8825-19b042e128d2 (last access: 7 July 2025), 2023.
Wang, P., Bayram, B., and Sertel, E.: A comprehensive review on deep learning based remote sensing image super-resolution methods, Earth-Sci. Rev., 232, 104110, https://doi.org/10.1016/j.earscirev.2022.104110, 2022.
Wang, Y., Chen, A. S., Fu, G., Djordjević, S., Zhang, C., and Savić, D. A.: An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features, Environ. Modell. Softw., 107, 85–95, https://doi.org/10.1016/j.envsoft.2018.06.010, 2018.
Wang, Y., Meng, F., Liu, H., Zhang, C., and Fu, G.: Assessing catchment scale flood resilience of urban areas using a grid cell based metric, Water Res., 163, 114852, https://doi.org/10.1016/j.watres.2019.114852, 2019.
Wang, Y., Zhang, C., Chen, A. S., Wang, G., and Fu, G.: Exploring the relationship between urban flood risk and resilience at a high-resolution grid cell scale, Sci. Total Environ., 893, 164852, https://doi.org/10.1016/j.scitotenv.2023.164852, 2023.
Wang, Z., Chen, J., and Hoi, S. C. H.: Deep Learning for Image Super-Resolution: A Survey, IEEE T. Pattern Anal., 43, 3365–3387, https://doi.org/10.1109/TPAMI.2020.2982166, 2021.
Wu, J., Zhong, B., Tian, S., Yang, A., and Wu, J.: Downscaling of Urban Land Surface Temperature Based on Multi-Factor Geographically Weighted Regression, IEEE J. Sel. Top. Appl., 12, 2897–2911, https://doi.org/10.1109/JSTARS.2019.2919936, 2019.
Xie, J., Fang, L., Zhang, B., Chanussot, J., and Li, S.: Super Resolution Guided Deep Network for Land Cover Classification From Remote Sensing Images, IEEE T. Geosci. Remote, 60, 1–12, https://doi.org/10.1109/TGRS.2021.3120891, 2022.
Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.-H., and Liao, Q.: Deep Learning for Single Image Super-Resolution: A Brief Review, IEEE T. Multimedia, 21, 3106–3121, https://doi.org/10.1109/TMM.2019.2919431, 2019.
Yue, L., Shen, H., Yuan, Q., and Zhang, L.: Fusion of multi-scale DEMs using a regularized super-resolution method, Int. J. Geogr. Inf. Sci., 29, 2095–2120, https://doi.org/10.1080/13658816.2015.1063639, 2015.
Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., and Zhang, L.: Image super-resolution: The techniques, applications, and future, Signal Process., 128, 389–408, https://doi.org/10.1016/j.sigpro.2016.05.002, 2016.
Zhang, X., Zhang, W., Guo, S., Zhang, P., Fang, H., Mu, H., and Du, P.: UnTDIP: Unsupervised neural network for DEM super-resolution integrating terrain knowledge and deep prior, Int. J. Appl. Earth Obs., 122, 103430, https://doi.org/10.1016/j.jag.2023.103430, 2023.
Zhang, Y. and Yu, W.: Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks, Sensors, 22, 745, https://doi.org/10.3390/s22030745, 2022.
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y.: Image Super-Resolution Using Very Deep Residual Channel Attention Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1807.02758, 12 July 2018.
Zhou, A., Chen, Y., Wilson, J. P., Su, H., Xiong, Z., and Cheng, Q.: An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs, Remote Sensing, 13, 3089, https://doi.org/10.3390/rs13163089, 2021.
Zhou, A., Chen, Y., Wilson, J. P., Chen, G., Min, W., and Xu, R.: A multi-terrain feature-based deep convolutional neural network for constructing super-resolution DEMs, Int. J. Appl. Earth Obs., 120, 103338, https://doi.org/10.1016/j.jag.2023.103338, 2023.
Zhu, Y.: Improving Pluvial Flood Simulations with Multi-source DEM Super-Resolution, Version v3, Zenodo [code/data set], https://doi.org/10.5281/zenodo.15212783, 2025.
Zhu, Y., Geiß, C., and So, E.: Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification, Int. J. Appl. Earth Obs., 104, 102543, https://doi.org/10.1016/j.jag.2021.102543, 2021.
Zhu, Y., Burlando, P., Tan, P. Y., Blagojevic, J., and Fatichi, S.: Investigating the influence of urban morphology on pluvial flooding: Insights from urban catchments in England (UK), Sci. Total Environ., 953, 176139, https://doi.org/10.1016/j.scitotenv.2024.176139, 2024.
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.
This study addresses the challenge of accurately predicting floods in regions with limited...
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