{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T13:02:35Z","timestamp":1789390955198,"version":"build-2803163510"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":84,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP25K17697"],"award-info":[{"award-number":["JP25K17697"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007330","name":"Ministry of Land, Infrastructure, Transport and Tourism","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007330","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate modelling of tropical cyclones (TCs) is essential for reliable storm surge simulations, as TCs provide the primary external forcing. While deep learning models have demonstrated effectiveness in TC modelling, a challenge remains regarding the limited diversity of TCs in reanalysis data used for training\u2014specifically, the scarcity of extreme TC events. This study proposes a physics-based data augmentation method that utilizes a numerical weather prediction (NWP) model to physically generate TCs lying beyond the range of reanalysis data. Subsequently, focusing on small-sized yet intense TCs as extreme cases, a model\u2014originally pre-trained solely on reanalysis data\u2014was fine-tuned using this augmented dataset to convert a parametric TC model (PM) field into an NWP-like field. Validation using test data mimicking extreme TCs and a storm surge hindcast of TC Faxai (a compact, intense TC that struck Tokyo Bay in 2019) revealed that the PM failed to simulate storm surges where topographic effects on the wind field are significant, and the pre-trained model underestimated wind speeds and storm surges. In contrast, the fine-tuned model successfully captured the spatiotemporal features of the extreme TCs and the peak storm surges upon TC landfall, achieving the lowest RMSE for storm surges across all TCs and tide gauges. These results suggest that physics-based data augmentation can effectively extend the applicability of deep learning models for TC and storm surge modelling to extreme events.<\/jats:p>","DOI":"10.1038\/s41598-026-58153-2","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T15:48:24Z","timestamp":1782143304000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-based data augmentation to enhance deep learning performance in tropical cyclone and storm surge modelling"],"prefix":"10.1038","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8125-2143","authenticated-orcid":false,"given":"Takumu","family":"Iwamoto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomohiro","family":"Takagawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"58153_CR1","doi-asserted-by":"publisher","first-page":"102250","DOI":"10.1016\/j.ocemod.2023.102250","volume":"185","author":"M Heidarzadeh","year":"2023","unstructured":"Heidarzadeh, M., Iwamoto, T., \u0160epi\u0107, J. & Mulia, I. E. Normal and reverse storm surges along the coast of Florida during the September 2022 Hurricane Ian: observations, analysis, and modelling. Ocean. Model. 185, 102250. https:\/\/doi.org\/10.1016\/j.ocemod.2023.102250 (2023).","journal-title":"Ocean. Model."},{"key":"58153_CR2","doi-asserted-by":"publisher","first-page":"3453","DOI":"10.1175\/2011JCLI4208.1","volume":"25","author":"BA Schenkel","year":"2012","unstructured":"Schenkel, B. A. & Hart, R. E. An examination of tropical cyclone position, intensity, and intensity life cycle within atmospheric reanalysis datasets. J. Clim. 25, 3453\u20133475. https:\/\/doi.org\/10.1175\/2011JCLI4208.1 (2012).","journal-title":"J. Clim."},{"key":"58153_CR3","doi-asserted-by":"publisher","first-page":"5181","DOI":"10.1002\/2016JC011777","volume":"121","author":"L Oey","year":"2016","unstructured":"Oey, L. & Chou, S. Evidence of rising and poleward shift of storm surge in western North Pacific in recent decades. J. Geophys. Res. Oceans. 121, 5181\u20135192. https:\/\/doi.org\/10.1002\/2016JC011777 (2016).","journal-title":"J. Geophys. Res. Oceans"},{"key":"58153_CR4","doi-asserted-by":"publisher","unstructured":"Liu, F. & Sasaki, J. Hybrid methods combining atmospheric reanalysis data and a parametric typhoon model to hindcast storm surges in Tokyo Bay. Sci. Rep. 9 https:\/\/doi.org\/10.1038\/s41598-019-48728-7 (2019).","DOI":"10.1038\/s41598-019-48728-7"},{"key":"58153_CR5","doi-asserted-by":"publisher","first-page":"102048","DOI":"10.1016\/j.apor.2019.102048","volume":"95","author":"PLN Murty","year":"2020","unstructured":"Murty, P. L. N. et al. Improved cyclonic wind fields over the Bay of Bengal and their application in storm surge and wave computations. Appl. Ocean. Res. 95, 102048. https:\/\/doi.org\/10.1016\/j.apor.2019.102048 (2020).","journal-title":"Appl. Ocean. Res."},{"key":"58153_CR6","doi-asserted-by":"publisher","first-page":"2003","DOI":"10.1007\/s11069-021-04525-y","volume":"106","author":"L Vijayan","year":"2021","unstructured":"Vijayan, L. et al. Evaluation of parametric wind models for more accurate modeling of storm surge: a case study of Hurricane Michael. Nat. Hazards. 106, 2003\u20132024. https:\/\/doi.org\/10.1007\/s11069-021-04525-y (2021).","journal-title":"Nat. Hazards"},{"key":"58153_CR7","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.wse.2016.02.002","volume":"9","author":"Y Pan","year":"2016","unstructured":"Pan, Y., Chen, Y. P., Li, J. X. & Ding, X. L. Improvement of wind field hindcasts for tropical cyclones. Water Sci. Eng. 9, 58\u201366. https:\/\/doi.org\/10.1016\/j.wse.2016.02.002 (2016).","journal-title":"Water Sci. Eng."},{"key":"58153_CR8","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1080\/21664250.2023.2228005","volume":"65","author":"T Iwamoto","year":"2023","unstructured":"Iwamoto, T., Takagawa, T., Shibayama, T., Esteban, M. & M\u00e4ll, M. A proposal of a semi-empirical method for modifying the atmospheric pressure and wind fields of tropical cyclones. Coast Eng. J. 65, 418\u2013432. https:\/\/doi.org\/10.1080\/21664250.2023.2228005 (2023).","journal-title":"Coast Eng. J."},{"key":"58153_CR9","doi-asserted-by":"publisher","first-page":"7918","DOI":"10.1038\/s41598-023-35093-9","volume":"13","author":"IE Mulia","year":"2023","unstructured":"Mulia, I. E., Ueda, N., Miyoshi, T., Iwamoto, T. & Heidarzadeh, M. A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields. Sci. Rep. 13, 7918. https:\/\/doi.org\/10.1038\/s41598-023-35093-9 (2023).","journal-title":"Sci. Rep."},{"key":"58153_CR10","unstructured":"Bi, K. et al. Pangu-weather: a 3D high-resolution model for fast and accurate global weather forecast. Preprint at (2022). https:\/\/arxiv.org\/abs\/2211.02556"},{"key":"58153_CR11","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1126\/science.adi233","volume":"382","author":"R Lam","year":"2023","unstructured":"Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 382, 1416\u20131421. https:\/\/doi.org\/10.1126\/science.adi233 (2023).","journal-title":"Science"},{"key":"58153_CR12","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/s41612-024-00769-0","volume":"7","author":"CC Liu","year":"2024","unstructured":"Liu, C. C. et al. Evaluation of five global AI models for predicting weather in eastern Asia and western Pacific. npj Clim. Atmos. Sci. 7, 221. https:\/\/doi.org\/10.1038\/s41612-024-00769-0 (2024).","journal-title":"npj Clim. Atmos. Sci."},{"key":"58153_CR13","unstructured":"Lang, S. et al. AIFS -- ECMWF\u2019s data-driven forecasting system. Preprint at (2024). https:\/\/arxiv.org\/abs\/2406.01465"},{"key":"58153_CR14","unstructured":"Leinonen, J., Nerini, D. & Berne, A. Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network. Preprint at (2020). https:\/\/arxiv.org\/abs\/2005.10374"},{"key":"58153_CR15","doi-asserted-by":"publisher","unstructured":"Sekiyama, T. T., Hayashi, S., Kaneko, R. & Fukui, K. I. Surrogate downscaling of mesoscale wind fields using ensemble superresolution convolutional neural networks. Artif. Intell. Earth Syst. 2, 3. https:\/\/doi.org\/10.1175\/aies-d-23-0007.1 (2023)","DOI":"10.1175\/aies-d-23-0007.1"},{"key":"58153_CR16","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1029\/2024jh000375","volume":"1","author":"JW Lockwood","year":"2024","unstructured":"Lockwood, J. W., Gori, A. & Gentine, P. A generative super-resolution model for enhancing tropical cyclone wind field intensity and resolution. J. Geophys. Res. Mach. Learn. Comput. 1, 4. https:\/\/doi.org\/10.1029\/2024jh000375 (2024).","journal-title":"J. Geophys. Res. Mach. Learn. Comput."},{"key":"58153_CR17","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1038\/s41612-025-01009-9","volume":"8","author":"J Pu","year":"2025","unstructured":"Pu, J. et al. A fast physics-based perturbation generator of machine learning weather model for efficient ensemble forecasts of tropical cyclone track. npj Clim. Atmos. Sci. 8, 128. https:\/\/doi.org\/10.1038\/s41612-025-01009-9 (2025).","journal-title":"npj Clim. Atmos. Sci."},{"key":"58153_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2151\/sola.2016-001","volume":"12","author":"N Kayaba","year":"2016","unstructured":"Kayaba, N. et al. Dynamical regional downscaling using the JRA-55 reanalysis (DSJRA-55). SOLA 12, 1\u20135. https:\/\/doi.org\/10.2151\/sola.2016-001 (2016).","journal-title":"SOLA"},{"key":"58153_CR19","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1175\/2009MWR3123.1","volume":"138","author":"KR Knapp","year":"2010","unstructured":"Knapp, K. R. & Kruk, M. C. Quantifying interagency differences in tropical cyclone best-track wind speed estimates. Mon Weather Rev. 138, 1459\u20131473. https:\/\/doi.org\/10.1175\/2009MWR3123.1 (2010).","journal-title":"Mon Weather Rev."},{"key":"58153_CR20","doi-asserted-by":"publisher","first-page":"2219","DOI":"10.1175\/WAF-D-19-0253.1","volume":"35","author":"BC Trabing","year":"2020","unstructured":"Trabing, B. C. & Bell, M. M. Understanding error distributions of hurricane intensity forecasts during rapid intensity changes. Weather Forecast. 35, 2219\u20132234. https:\/\/doi.org\/10.1175\/WAF-D-19-0253.1 (2020).","journal-title":"Weather Forecast."},{"key":"58153_CR21","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1038\/s41597-025-05789-w","volume":"12","author":"G Liu","year":"2025","unstructured":"Liu, G. et al. A global ERA5-based tropical cyclone wind field dataset enhanced by integrated parametric correction methods. Sci. Data. 12, 1429. https:\/\/doi.org\/10.1038\/s41597-025-05789-w (2025).","journal-title":"Sci. Data"},{"key":"58153_CR22","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1002\/qj.3803","volume":"146","author":"H Hersbach","year":"2020","unstructured":"Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R Meteorol. Soc. 146, 1999\u20132049. https:\/\/doi.org\/10.1002\/qj.3803 (2020).","journal-title":"Q. J. R Meteorol. Soc."},{"key":"58153_CR23","doi-asserted-by":"publisher","unstructured":"Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. Big Data 6, 1. https:\/\/doi.org\/10.1186\/s40537-019-0197-0 (2019).","DOI":"10.1186\/s40537-019-0197-0"},{"key":"58153_CR24","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1029\/2024jh000206","volume":"1","author":"G Ascenso","year":"2024","unstructured":"Ascenso, G., Palcic, G., Scoccimarro, E. & Castelletti, A. A systematic framework for data augmentation for tropical cyclone intensity estimation using deep learning. J. Geophys. Res. Mach. Learn. Comput. 1, 3. https:\/\/doi.org\/10.1029\/2024jh000206 (2024).","journal-title":"J. Geophys. Res. Mach. Learn. Comput."},{"key":"58153_CR25","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1175\/1520-0469(1987)044<0542:AAITFT>2.0.CO;2","volume":"44","author":"R Rotunno","year":"1987","unstructured":"Rotunno, R. & Emanuel, K. A. An air\u2013sea interaction theory for tropical cyclones. Part II: evolutionary study using a nonhydrostatic axisymmetric numerical model. J. Atmos. Sci. 44, 542\u2013561. 10.1175\/1520\u2009\u2013\u20090469(1987)044\u2009<\u20090542:AAITFT\u2009>\u20092.0.CO;2 (1987).","journal-title":"J. Atmos. Sci."},{"key":"58153_CR26","unstructured":"Skamarock, W. C. et al. A Description of the Advanced Research WRF Version 4. NCAR Tech. Note. NCAR\/TN-556+STR, 1-145. https:\/\/doi.org\/10.5065\/1dfh-6p97 (2019)."},{"key":"58153_CR27","doi-asserted-by":"publisher","first-page":"100229","DOI":"10.1016\/j.wace.2019.100229","volume":"26","author":"CL Bruy\u00e8re","year":"2019","unstructured":"Bruy\u00e8re, C. L. et al. Physically-based landfalling tropical cyclone scenarios in support of risk assessment. Weather Clim. Extrem. 26, 100229. https:\/\/doi.org\/10.1016\/j.wace.2019.100229 (2019).","journal-title":"Weather Clim. Extrem"},{"key":"58153_CR28","unstructured":"Isola, P., Zhu, J. Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. Preprint at (2016). https:\/\/arxiv.org\/abs\/1611.07004"},{"key":"58153_CR29","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1007\/s11707-020-0817-1","volume":"14","author":"MR Islam","year":"2020","unstructured":"Islam, M. R. & Takagi, H. Typhoon parameter sensitivity of storm surge in the semi-enclosed Tokyo Bay. Front. Earth Sci. 14, 553\u2013567. https:\/\/doi.org\/10.1007\/s11707-020-0817-1 (2020).","journal-title":"Front. Earth Sci."},{"key":"58153_CR30","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1080\/21664250.2020.1738620","volume":"62","author":"T Suzuki","year":"2020","unstructured":"Suzuki, T. et al. Post-event survey of locally concentrated disaster due to 2019 Typhoon Faxai along the western shore of Tokyo Bay, Japan. Coast Eng. J. 62, 146\u2013158. https:\/\/doi.org\/10.1080\/21664250.2020.1738620 (2020).","journal-title":"Coast Eng. J."},{"key":"58153_CR31","doi-asserted-by":"publisher","first-page":"5","DOI":"10.2151\/jmsj.2015-001","volume":"93","author":"S Kobayashi","year":"2015","unstructured":"Kobayashi, S. et al. The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Jpn. 93, 5\u201348. https:\/\/doi.org\/10.2151\/jmsj.2015-001 (2015).","journal-title":"J. Meteorol. Soc. Jpn"},{"key":"58153_CR32","unstructured":"Thomson, R. E. & Emery, W. J. Data Analysis Methods in Physical Oceanography (Newnes, 2014)."},{"key":"58153_CR33","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1029\/2019jd031796","volume":"125","author":"AN Ramos-Valle","year":"2020","unstructured":"Ramos-Valle, A. N., Curchitser, E. N. & Bruy\u00e8re, C. L. Impact of tropical cyclone landfall angle on storm surge along the mid-Atlantic bight. J. Geophys. Res. Atmos. 125, 4. https:\/\/doi.org\/10.1029\/2019jd031796 (2020).","journal-title":"J. Geophys. Res. Atmos."},{"key":"58153_CR34","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1175\/1520-0493(1977)105<1119:TSAEOT>2.0.CO;2","volume":"105","author":"W Frank","year":"1977","unstructured":"Frank, W. The structure and energetics of the tropical cyclone I. storm structure. Mon Weather Rev. 105, 1119\u20131135.\u00a0https:\/\/doi.org\/10.1175\/1520-0493(1977)105\\lt1119:TSAEOT>2.0.CO;2 (1977).","journal-title":"Mon Weather Rev."},{"key":"58153_CR35","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/BF01022525","volume":"56","author":"M Ueno","year":"1995","unstructured":"Ueno, M. A study on the impact of asymmetric components around tropical cyclone center on the accuracy of bogus data and the track forecast. Meteorol. Atmos. Phys. 56, 125\u2013134. https:\/\/doi.org\/10.1007\/BF01022525 (1995).","journal-title":"Meteorol. Atmos. Phys."},{"key":"58153_CR36","unstructured":"Hobbs, P. V. & Wallace, J. M. Atmospheric Science: An Introductory Survey (Academic, 2006)."},{"key":"58153_CR37","unstructured":"Akiba, T. et al. Optuna: a next-generation hyperparameter optimization framework. Preprint at (2019). https:\/\/arxiv.org\/abs\/1907.10902"},{"key":"58153_CR38","doi-asserted-by":"publisher","first-page":"49","DOI":"10.2151\/jmsj.2024-004","volume":"102","author":"Y Kosaka","year":"2024","unstructured":"Kosaka, Y. et al. The JRA-3Q reanalysis. J. Meteorol. Soc. Jpn. 102, 49\u2013109. https:\/\/doi.org\/10.2151\/jmsj.2024-004 (2024).","journal-title":"J. Meteorol. Soc. Jpn"},{"key":"58153_CR39","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1175\/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2","volume":"108","author":"GJ Holland","year":"1980","unstructured":"Holland, G. J. An analytic model of the wind and pressure profiles in hurricanes. Mon Weather Rev. 108, 1212\u20131218.\u00a0https:\/\/doi.org\/10.1175\/1520-0493(1980)108\\lt1212:AAMOTW>2.0.CO;2 (1980).","journal-title":"Mon Weather Rev."},{"key":"58153_CR40","first-page":"169","volume":"37","author":"Y Mitsuta","year":"1987","unstructured":"Mitsuta, Y. & Fujii, T. Analysis and synthesis of typhoon wind pattern over Japan. Bull. Disas Prev. Res. Inst. 37, 169\u2013185 (1987).","journal-title":"Bull. Disas Prev. Res. Inst."},{"key":"58153_CR41","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1175\/1520-0493(1998)126<1091:SAOTCO>2.0.CO;2","volume":"126","author":"T Fujii","year":"1998","unstructured":"Fujii, T. Statistical analysis of the characteristics of severe typhoons hitting the Japanese main islands. Mon Weather Rev. 126, 1091\u20131097. https:\/\/doi.org\/10.1175\/1520-0493(1998)126\\lt1091:SAOTCO>2.0.CO;2 (1998).","journal-title":"Mon Weather Rev."},{"key":"58153_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5359\/jawe.1986.28_1","volume":"28","author":"T Fujii","year":"1986","unstructured":"Fujii, T. & Mitsuta, Y. Simulation of winds in typhoons by a stochastic model. Wind Eng. JAWE. 28, 1\u201312. https:\/\/doi.org\/10.5359\/jawe.1986.28_1 (1986).","journal-title":"Wind Eng. JAWE"},{"key":"58153_CR43","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0079-7421(08)60536-8","volume":"24","author":"M McCloskey","year":"1989","unstructured":"McCloskey, M. & Cohen, N. J. Catastrophic interference in connectionist networks: the sequential learning problem. Psychol. Learn. Motivation. 24, 109\u2013165. https:\/\/doi.org\/10.1016\/S0079-7421(08)60536-8 (1989).","journal-title":"Psychol. Learn. Motivation"},{"key":"58153_CR44","unstructured":"Kirkpatrick, J. et al. Overcoming catastrophic forgetting in neural networks. Preprint at (2016). https:\/\/arxiv.org\/abs\/1612.00796"},{"key":"58153_CR45","doi-asserted-by":"publisher","unstructured":"Deguchi, S. & Asai, M. Dynamic & norm-based weights to normalize imbalance in back-propagated gradients of physics-informed neural networks. J. Phys. Commun. 7 https:\/\/doi.org\/10.1088\/2399-6528\/ace416 (2023).","DOI":"10.1088\/2399-6528\/ace416"},{"key":"58153_CR46","unstructured":"Seff, A., Beatson, A., Suo, D. & Liu, H. Continual learning in generative adversarial nets. Preprint at (2017). https:\/\/arxiv.org\/abs\/1705.08395"},{"key":"58153_CR47","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.ocemod.2004.08.002","volume":"9","author":"AF Shchepetkin","year":"2005","unstructured":"Shchepetkin, A. F. & McWilliams, J. C. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean. Model. 9, 347\u2013404. https:\/\/doi.org\/10.1016\/j.ocemod.2004.08.002 (2005).","journal-title":"Ocean. Model."},{"key":"58153_CR48","doi-asserted-by":"publisher","first-page":"705","DOI":"10.5194\/nhess-16-705-2016","volume":"16","author":"H Takagi","year":"2016","unstructured":"Takagi, H. & Wu, W. Maximum wind radius estimated by the 50 kt radius: improvement of storm surge forecasting over the western North Pacific. Nat. Hazards Earth Syst. Sci. 16, 705\u2013717. https:\/\/doi.org\/10.5194\/nhess-16-705-2016 (2016).","journal-title":"Nat. Hazards Earth Syst. Sci."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58153-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58153-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58153-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:04:23Z","timestamp":1789387463000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58153-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["58153"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-58153-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,22]]},"assertion":[{"value":"24 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"28558"}}