Abstract
Global climate change has increased flood frequency worldwide, yet many urban and flash floods remain poorly captured by satellite remote sensing. Here we integrate Global Satellite Mapping of Precipitation data with 92.98 million Sina Weibo posts to trace spatiotemporal flood footprints across China from 2012 to 2024. Using a cascading threshold method, we identify 6,018 rainstorm events across 370 cities. Topic modeling of posts within rainstorm affected areas detects 1,094 flood events, far exceeding the 114 and 45 events recorded in Emergency Events Database and Dartmouth Flood Observatory. Comparison with satellite imagery on Google Earth Engine shows that approximately 50% of these events were unobservable by remote sensing. While satellites capture riverine flooding in open areas, social media fills critical gaps in densely populated urban settings. This social sensing framework reveals previously undocumented flood events and their propagation patterns, offering a complementary approach that enhances traditional flood monitoring capabilities.
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Data availability
The datasets generated during the current study are available from the figshare repository (https://doi.org/10.6084/m9.figshare.29561732.v3). GSMaP precipitation data used in the current study are available via the website (http://sharaku.eorc.jaxa.jp/GSMaP/index.htm). Sina Weibo data used in the current study are available via the website (https://www.weibo.com).
Code availability
The code used for the current study is available from the figshare repository (https://doi.org/10.6084/m9.figshare.29561918.v4).
References
Haslinger, K. et al. Increasing hourly heavy rainfall in Austria reflected in flood changes. Nature 639, 667–672 (2025).
Van Dijk, A. I. J. M. et al. Global Water Monitor 2024, Summary Report, 2025 (Global Water Monitor Consortium, 2025).
Jongman, B., Ward, P. J. & Aerts, J. C. J. H. Global exposure to river and coastal flooding: Long term trends and changes. Glob. Environ. Change 22, 823–835 (2012).
Gao, S. et al. Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. J. Hydrol. 589, 125188 (2020).
Kellermann, P., Schröter, K., Thieken, A., Haubrock, S.-N. & Kreibich, H. The object-specific flood damage database HOWAS21. https://doi.org/10.5194/nhess-2019-420 (2020).
Wang, Y., Yang, S., Zhang, L., Cao, Y. & Yin, Y. Comparative analysis and outlook of three global databases for meteorological disasters. Clim Change Res. 18, 253–260 (2022).
Moriyama, K., Sasaki, D. & Ono, Y. Comparison of global databases for disaster loss and damage data. J. Disaster Res. 13, 1007–1014 (2018).
Shen, G. & Hwang, S. N. Spatial–temporal snapshots of global natural disaster impacts Revealed from EM-DAT for 1900-2015. Geomat. Nat. Hazards Risk 10, 912–934 (2019).
Tellman, B. et al. Satellite imaging reveals increased proportion of population exposed to floods. Nature 596, 80–86 (2021).
Linderson, S. et al. The wider the gap between rich and poor the higher the flood mortality. Nat. Sustain. 6, 995–1005 (2023).
De Groeve, T. Global Disaster Alert and Coordination System: More Effective and Efficient Humanitarian Response (Hydrographic Institute of the Republic of Croatia, Split (Croatia), 2007).
Kuenzer, C. et al. Flood mapping and flood dynamics of the Mekong Delta: ENVISAT-ASAR-WSM based time series analyses. Remote Sens. 5, 687–715 (2013).
Tong, X. et al. An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery. ISPRS J. Photogramm. Remote Sens. 136, 144–153 (2018).
Klemas, V. Remote sensing of floods and flood-prone areas: an overview. J. Coast. Res. 31, 1005–1013 (2015).
Yan, K., Di Baldassarre, G., Solomatine, D. & Schumann, G. A review of low-cost space-born data for flood modelling: topography, flood extent and water level. Hydrol. Processes 29, 3368–3387 (2015).
Sanyal, J. & Lu, X. X. Application of remote sensing in flood management with special reference to monsoon Asia: a review. Nat. Hazards 33, 283–301 (2004).
Schumann, G., Bates, P., Horritt, M., Matgen, P. & Pappenberger, F. Progress in integration of remote sensing-derived flood extent and stage and hydraulic models. Rev. Geophys. 47, https://doi.org/10.1029/2008RG000274 (2009).
Brodzik, M., Long, D. & Hardman, M. Best practices in crafting the calibrated, enhanced-resolution passive-microwave EASE-Grid 2.0 brightness temperature earth system data record. Remote Sens. 10, 1793 (2018).
Zeng, Z. et al. Towards high resolution flood monitoring: an integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery. J. Hydrol. 582, 124377 (2019).
Misra, A., White, K., Nsutezo, S., Straka III, W. & Lavista Ferres, J. Mapping global floods with 10 years of satellite radar data. Nat. Commun. 16, https://doi.org/10.1038/s41467-025-60973-1 (2025).
Matosak, B., Gella, G. & Lang, S. SenForFlood: a new global dataset for flooded area detection. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-7-2025, 97–102 Copernicus Publications, (2025).
Liu, Y. et al. Social sensing: a new approach to understanding our socioeconomic environments. Ann. Assoc. Am. Geographers 105, 1–19 (2015).
Li, J. et al. Social media: new perspectives to improve remote sensing for emergency response. In Proceedings of the IEEE, 1–13 (IEEE, 2017).
Zhao, Q., Chen, Z., Liu, C. & Luo, N. Extracting and classifying typhoon disaster information based on volunteered geographic information from Chinese Sina microblog. Concurrency Comput. 31, e4910 (2018).
Chae, J. et al. Public behavior response analysis in disaster events utilizing visual analytics of microblog data. Comput Graph. 38, 51–60 (2014).
Hao, H. & Wang, Y. Leveraging multimodal social media data for rapid disaster damage assessment. Int. J. Disaster Risk Reduct. 51, 101760 (2020).
Sadiq, R., Akhtar, Z., Imran, M. & Ofli, F. Integrating remote sensing and social sensing for flood mapping. Remote Sens. Appl. Soc. Environ. 25, 100697 (2022).
Saleem, H., Xu, Y. & Ruths, D. Effects of disaster characteristics on twitter event signature. Procedia Eng. 78, 165–172 (2014).
Fu, S., Lyu, H., Wang, Z., Hao, X. & Zhang, C. Extracting historical flood locations from news media data by the named entity recognition (NER) model to assess urban flood susceptibility. J. Hydrol. 612, 128312 (2022).
Karmegam, D., Sivakumar, R. & Mappillairaju, B. Near real time flood inundation mapping using social media data as an information source: a case study of 2015 Chennai flood. Geoenviron. Disasters 8, https://doi.org/10.1186/s40677-021-00195-x (2021).
Li, Y., Osei, F., Hu, T. & Stein, A. Urban flood susceptibility mapping based on social media data in Chengdu city, China. Sustain. Cities Soc. 88, 104307 (2022).
Yang, Z., Nguyen, L., Stuve, J., Cao, G. & Jin, F. Harvey flooding rescue in social media, https://doi.org/10.1109/BigData.2017.8258166 (2017).
Scotti, V., Giannini, M. & Cioffi, F. Enhanced flood mapping using synthetic aperture radar (SAR) images, hydraulic modelling, and social media: a case study of Hurricane Harvey (Houston, TX). J. Flood Risk Manag. 13, https://doi.org/10.1111/jfr3.12647 (2020).
Shoyama, K., Cui, Q., Hanashima, M., Sano, H. & Usuda, Y. Emergency flood detection using multiple information sources: Integrated analysis of natural hazard monitoring and social media data. Sci. Total Environ. 767, 144371 (2020).
de Bruijn, J. et al. A global database of historic and real-time flood events based on social media. Sci. Data 6, 311 (2019).
Li, Y. & Zhao, S. Floods losses and hazards in China from 2001 to 2020. Climate Change. Research 18, 154–165 (2022).
Shen, D. et al. How many floods have occurred in China in the past decade? A perspective from social media. Earths Fut. 13, e2024EF004775 (2025).
Wang, Z. Analysis of extreme precipitation weather process in Henan during July 17-22, 2021. J. Geosci. Environ. Prot. 11, 72–85 (2023).
Stampoulis, D. & Anagnostou, E. Evaluation of global satellite rainfall products over continental Europe. J. Hydrometeorol. 13, 588–603 (2012).
Gu, H. et al. Evaluation of daily and hourly performance of multi-source satellite precipitation products in China’s Nine Water Resource Regions. Remote Sens. 16, https://doi.org/10.3390/rs16091516 (2024).
Kong, F., Liu, F., Wang, Y., Fang, J. & Lu, L. Spatial relationship between flooding frequency and climate change based on climate change regionalization in China from 1961 to 2010. J. Catastrophol. 32, 35–42 (2018).
Xie, P. et al. A gauge-based analysis of daily precipitation over east Asia. J. Hydrometeorol. 8, 607–626 (2007).
Smith, L., Liang, Q., James, P. & Lin, W. Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework: assessing the utility of social media for flood risk management. J. Flood Risk Manag. 10, 370–380 (2015).
Resch, B., Usländer, F. & Havas, C. Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment. Cartogr. Geographic Inf. Sci. 45, 1–15 (2017).
Huang, X., Wang, C. & Li, Z. A near real-time flood mapping approach by integrating social media and post-event satellite imagery. Ann. GIS 24, 113–123 (2018).
Yu, Z., Yang, X., Yu, X. & Jiang, X. Capability and biases of satellite precipitation products in monitoring extreme rainstorms along the northern slope of the Kunlun Mountains. Arid Zone. Research 42, 1777–1790 (2025).
Pang, Z. et al. A comprehensive assessment of multiple high-resolution precipitation grid products for monitoring heavy rainfall during the “7.20” extreme rainstorm event in China. Remote Sens. 15, 5255 (2023).
Skok, G., Bacmeister, J. & Tribbia, J. Analysis of tropical cyclone precipitation using an object-based algorithm. J. Clim. 26, 2563–2579 (2013).
Davis, C., Brown, B. & Bullock, R. Object-based verification of precipitation forecasts. Part I: methodology and application to mesoscale rain areas. Monthly Weather Rev. 134, 1772–1784 (2006).
Skok, G., Tribbia, J., Rakovec, J. & Brown, B. Object-based analysis of satellite-derived precipitation systems over the low and midlatitude Pacific Ocean. Monthly Weather Rev. 137, 3196–3218 (2009).
Devlin, J., Chang, M., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. https://doi.org/10.48550/arXiv.1810.04805 (2018).
Paul, P. C., Begum, A., Ahmed, M. T., Chakraborty, D. & Rahman, M. Combining BERT with LDA: improved topic modeling in Bengali language. IAENG Int. J. Comput. Sci. 52, 383–393 (2025).
Jawahar, G., Sagot, B. & Seddah, D. What does BERT learn about the structure of language? In Proceedings of the 57th annual meeting of the association for computational linguistics, 3651–3657 (Association for Computational Linguistics, 2019).
Fang, W., Luo, H., Xu, S., Lu, Z. & Ye, C. Automated text classification of near-misses from safety reports: an improved deep learning approach. Adv. Eng. Inform. 44, 101060 (2020).
Gardazi, N. et al. BERT applications in natural language processing: a review. Artif. Intell. Rev. 58, 166 (2025).
Xu, L., Xue, Z. & Huang, H. Short text semantic feature extension and classification based on LDA. IOP Conf. Ser. Mater. Sci. Eng. 715, 012110 (2020).
Acknowledgements
This research was funded by the National Natural Science Foundation of China (grant number 42077438) and the Fundamental Research Funds for the Central Universities (grant number CCNU25JC004, CCNU25JCPT028).
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H. Gu: Conceptualization, Writing–original draft, Investigation & Formal analysis. J. Xiao: Conceptualization, Writing–review & editing & Supervision. C. Zhang, S. Xiao, Z. Niu, & F. Yu: Resources & Writing–review &editing. D. Shen: Conceptualization, Writing–review & editing, Supervision, Project administration & Funding acquisition.
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Communications Earth and Environment thanks Chenghan Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Rajarshi Das Bhowmik and Martina Grecequet. A peer review file is available.
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Gu, H., Xiao, J., Shen, D. et al. A combination of social media and satellite data improves flood monitoring in China. Commun Earth Environ (2026). https://doi.org/10.1038/s43247-026-03403-4
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DOI: https://doi.org/10.1038/s43247-026-03403-4
