Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Spatiotemporal assessment of multi hazard risk using graph based analysis for case studies in India
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 20 January 2026

Spatiotemporal assessment of multi hazard risk using graph based analysis for case studies in India

  • Hari Chandana Ekkirala1,2 &
  • Maneesha Vinodini Ramesh1,2 

Scientific Reports , Article number:  (2026) Cite this article

  • 1006 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Environmental sciences
  • Hydrology
  • Natural hazards

Abstract

Over the years, India has experienced numerous rainfall-triggered landslides that initiate complex multi-hazard events, resulting in substantial human loss. This study presents a graph-based risk assessment of multi-hazards for two case studies in India: The South Lhonak Lake Glacial Lake Outburst Flood which impacted North Sikkim in October 2023 and the Wayanad Landslides in July 2024, which collectively claimed over 600 lives. This is achieved through a multidimensional methodology which integrates dynamic rainfall and discharge thresholds, stakeholder-informed hazard sequences, spatiotemporal hazard progression, and elements at risk. Heterogeneous data sources including remote sensing, field surveys, hydro-meteorological observations, and gray literature such as government reports and official situation bulletins, are synthesized to construct weighted, directed hazard networks. Graph-theory metrics, including degree centrality, betweenness centrality, and cascade depth, are used to compute sub-basin-level risk scores. Results highlighted critical sequences present in both regions, particularly the transition from extreme rainfall to landslides and subsequent flooding. Also, they identified high-risk zones influenced by both topography and infrastructure exposure. The findings emphasize the need for real-time threshold monitoring and alert systems, hazard-sequence-based operational protocols, and spatiotemporally phased response planning to support coordinated evacuations and early warning. The proposed framework offers actionable guidance for dynamic risk monitoring and multi-hazard governance in vulnerable mountain ecosystems.

Data availability

Daily rainfall data were obtained from rain gauge stations of the Indian Meteorological Department (available at: [https://mausam.imd.gov.in/](https:/mausam.imd.gov.in)) and the Central Water Commission (available at: [https://indiawris.gov.in/wris/](https:/indiawris.gov.in/wris)). The ALOS PALSAR DEM was obtained from the Alaska Satellite Facility DAAC (ASF DAAC) (available at: [https://search.asf.alaska.edu/](https:/search.asf.alaska.edu)).

References

  1. Froude, M. J. & Petley, D. N. Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci. 18, 2161–2181. https://doi.org/10.5194/nhess-18-2161-2018 (2018).

    Google Scholar 

  2. GSI. Landslide Susceptibility, Early Warning and Seismic Hazard Zonation. National Meet on Disaster Management by NRSC, Hyderabad, 27–28 Feb. (2022). Available at: https://www.nrsc.gov.in/sites/default/files/pdf/ebooks/drm/S-IV_GeologicalDisasters.pdf (Accessed: 29 March 2025).

  3. Sharma, S. et al. Increasing risk of cascading hazards in the central Himalayas. Nat. Hazards. 119, 1117–1126. https://doi.org/10.1007/s11069-022-05462-0 (2023).

    Google Scholar 

  4. Youssef, K. et al. Landslide susceptibility modeling by interpretable neural network. Commun. Earth Environ. 4, 162. https://doi.org/10.1038/s43247-023-00806-5 (2023).

    Google Scholar 

  5. Rusk, J. et al. Multi-hazard susceptibility and exposure assessment of the Hindu Kush Himalaya. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2021.150039 (2022).

    Google Scholar 

  6. Sekhri, S., Kumar, P., Fürst, C. & Pandey, R. Mountain specific multi-hazard risk management framework (MSMRMF): Assessment and mitigation of multi-hazard and climate change risk in the Indian Himalayan Region. Ecol. Indic. 118, 106700. https://doi.org/10.1016/j.ecolind.2020.106700 (2020).

    Google Scholar 

  7. Maraun, D. et al. A severe landslide event in the alpine foreland under possible future climate and land-use changes. Commun. Earth Environ. 3, 87. https://doi.org/10.1038/s43247-022-00408-7 (2022).

    Google Scholar 

  8. Sun, Q. et al. Runup of landslide-generated tsunamis controlled by paleogeography and sea-level change. Commun. Earth Environ. 3, 244. https://doi.org/10.1038/s43247-022-00572-w (2022).

    Google Scholar 

  9. Nazimul Islam & Priyank Pravin Patel. : Inventory and GLOF hazard assessment of glacial lakes in the Sikkim Himalayas, India, Geocarto International, (2021). https://doi.org/10.1080/10106049.2020.1869332

  10. Dahal, A. et al. Quantifying the influence of topographic amplification on the landslides triggered by the 2015 Gorkha earthquake. Commun. Earth Environ. 5, 678. https://doi.org/10.1038/s43247-024-01822-9 (2024).

    Google Scholar 

  11. De Angeli, S. et al. A multi-hazard framework for spatial-temporal impact analysis. Int. J. Disaster Risk Reduct. 73, 102829. https://doi.org/10.1016/j.ijdrr.2022.102829 (2022).

    Google Scholar 

  12. Forzieri, G. et al. Multi-hazard assessment in Europe under climate change. Clim. Change. 137, 105–119. https://doi.org/10.1007/s10584-016-1661-x (2016).

    Google Scholar 

  13. Minimo, L. G. Spatiotemporal analysis of the interaction of decentralization, development, and disaster cascades in Mindanao, Philippines. (2021).

  14. Laino, E., Paranunzio, R. & Iglesias, G. Scientometric review on multiple climate-related hazards indices. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2024.174004 (2024).

    Google Scholar 

  15. Sattar, A. et al. Future glacial lake outburst flood (GLOF) hazard of the South Lhonak Lake, Sikkim Himalaya. Geomorphology 388, 107783. https://doi.org/10.1016/j.geomorph.2021.107783 (2021).

    Google Scholar 

  16. Gill, J. C. & Malamud, B. D. Reviewing and visualizing the interactions of natural hazards. Rev. Geophys. 52 (4), 680–722. https://doi.org/10.1002/2013RG000445 (2014).

    Google Scholar 

  17. Gill, J. C. & Malamud, B. D. Hazard interactions and interaction networks (cascades) within multi-hazard methodologies. Earth Sys. Dyn. 7 (3), 659–679. https://doi.org/10.5194/esd-7-659-2016 (2016).

    Google Scholar 

  18. Liu, B., Siu, Y. L. & Mitchell, G. Hazard interaction analysis for multi-hazard risk assessment: a systematic classification based on hazard-forming environment. Nat. Hazards Earth Syst. Sci. Discuss. 3 (12), 7203–7229. https://doi.org/10.5194/nhess-16-629-2016 (2015).

    Google Scholar 

  19. Pescaroli, G. & Alexander, D. Critical infrastructure, panarchies and the vulnerability paths of cascading disasters. Nat. Hazards. 82, 175–192. https://doi.org/10.1007/s11069-016-2186-3 (2016).

    Google Scholar 

  20. Arosio, M., Martina, M. L. & Figueiredo, R. The whole is greater than the sum of its parts: a holistic graph-based assessment approach for natural hazard risk of complex systems. Nat. Hazards Earth Syst. Sci. 20 (2), 521–547. https://doi.org/10.5194/nhess-20-521-2020 (2020).

    Google Scholar 

  21. Dunant, A., Bebbington, M. & Davies, T. Probabilistic cascading multi-hazard risk assessment methodology using graph theory, a new Zealand trial. Int. J. Disaster Risk Reduct. 54, 102018. https://doi.org/10.1016/j.ijdrr.2020.102018 (2021).

    Google Scholar 

  22. Bakhtiari, S. Multi-Hazard Risk Assessment of the Interconnected Infrastructure Systems. (2024).

  23. Zhong, M. et al. A copula-based multivariate probability analysis for flash flood risk under the compound effect of soil moisture and rainfall. Water Resour. Manage. 35, 83–98. https://doi.org/10.1007/s11269-020-02709-y (2021).

    Google Scholar 

  24. Lam, C. Y. & Shimizu, T. A network analytical framework to analyze infrastructure damage based on earthquake cascades: A study of earthquake cases in Japan. Int. J. Disaster Risk Reduct. 54, 102025. https://doi.org/10.1016/j.ijdrr.2020.102025 (2021).

    Google Scholar 

  25. Gailleton, B., Steer, P., Davy, P., Schwanghart, W. & Bernard, T. GraphFlood 1.0: an efficient algorithm to approximate 2D hydrodynamics for landscape evolution models. Earth Surf. Dyn. 12 (6), 1295–1313. https://doi.org/10.5194/esurf-12-1295-2024 (2024).

    Google Scholar 

  26. Alabbad, Y. & Demir, I. Comprehensive flood vulnerability analysis in urban communities: Iowa case study. Int. J. Disaster Risk Reduct. https://doi.org/10.1016/j.ijdrr.2022.102955 (2022).

    Google Scholar 

  27. Bugert, N. & Lasch, R. Analyzing upstream and downstream risk propagation in supply networks by combining Agent-based modeling and bayesian networks. J. Bus. Econ. 93 (5), 859–889. https://doi.org/10.1007/s11573-022-01128-2 (2023).

    Google Scholar 

  28. Sattar, A. et al. The Sikkim flood of October 2023: Drivers, causes, and impacts of a multihazard cascade. Science 387 (6740), eads2659. https://doi.org/10.1126/science.ads2659 (2025).

    Google Scholar 

  29. Yunus, A. P. et al. Documenting the most disastrous Meppadi landslide of 30th July 2024 Wayanad, India. https://doi.org/10.22541/au.172677269.94602265/v1

  30. Achu, A. L. et al. Decoding the dynamics of July 2024 Mundakkai-Chooralmala landslide in Kerala (India): an analysis of formation mechanisms, impacts and lessons learned. Landslides https://doi.org/10.1007/s10346-024-02454-y (2025).

    Google Scholar 

  31. Caine, N. The rainfall intensity-duration control of shallow landslides and debris flows. Geogr. Annaler: Ser. Phys. Geogr. 62 (1-2), 23–27. https://doi.org/10.1080/04353676.1980.11879996 (1980).

    Google Scholar 

  32. Harilal, G. T., Madhu, D., Ramesh, M. V. & Pullarkatt, D. Towards Establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India. Landslides 16 (12), 2395–2408. https://doi.org/10.1007/s10346-019-01244-1 (2019).

    Google Scholar 

  33. Remya & Nandan A ticking bomb: Understanding the 2023 Glacial Lake Outburst Flood (GLOF) in Sikkim Himalaya. [online] (2023). Available at: https://blogs.egu.eu/divisions/cr/2023/11/02/a-ticking-bomb-understanding-the-2023-glacial-lake-outburst-flood-glof-in-sikkim-himalaya/ [Accessed 8 Dec. 2024].

  34. Geospatial & World Cloud burst triggers flash floods in Sikkim, India. [online] (2023). Available at: https://geospatialworld.net/prime/cloud-burst-triggers-flash-floods-sikkim-india/ [Accessed 8 Dec. 2024].

  35. Down To & Earth Nepal earthquakes might have triggered an outburst of Sikkim Lake that expanded rapidly in 11 days, shows data. [online] (2023). Available at: https://www.downtoearth.org.in/natural-disasters/nepal-earthquakes-might-have-triggered-outburst-of-sikkim-lake-that-expanded-rapidly-in-11-days-shows-data-92158 [Accessed 31 October 2024].

  36. AmritaWNA, W. & Report Report. [online] (2024). Available at: https://online.pubhtml5.com/ppfdc/icdr/ [Accessed 8 Dec. 2024].

  37. Valagussa, A., Frattini, P., Valbuzzi, E. & Crosta, G. B. Role of landslides on the volume balance of the Nepal 2015 earthquake sequence. Sci. Rep. https://doi.org/10.1038/s41598-021-83037-y (2021).

    Google Scholar 

  38. Woodhouse, M. J., Hogg, A. J., Phillips, J. C. & Sparks, R. S. J. Interaction between volcanic plumes and wind during the 2010 Eyjafjallajökull eruption, Iceland. J. Geophys. Research: Solid Earth. 118 (1), 92–109. https://doi.org/10.1029/2012JB009592 (2013).

    Google Scholar 

  39. Kos, A. et al. (2016). https://doi.org/10.1002/2016GL071708

  40. Cevasco, A. et al. Storminess and geo-hydrological events affecting small coastal basins in a terraced mediterranean environment. Sci. Total Environ. 532, 208–219. https://doi.org/10.1016/j.scitotenv.2015.06.017 (2015).

    Google Scholar 

  41. IndiaSpend ‘Teesta Dam Breach: Disregard For Green Norms, Irregularities In Focus’, IndiaSpend, 9 November. (2023). Available at: https://www.indiaspend.com/investigations/teesta-dam-breach-disregard-for-green-norms-irregularities-in-focus-882600 (Accessed: 01 June 2024).

  42. Ming, X., Liang, Q., Dawson, R., Xia, X. & Hou, J. A quantitative multi-hazard risk assessment framework for compound flooding considering hazard inter-dependencies and interactions. J. Hydrol. 607, 127477. https://doi.org/10.1016/j.jhydrol.2022.127477 (2022).

    Google Scholar 

  43. Hochrainer-Stigler, S. et al. Toward a framework for systemic multi-hazard and multi-risk assessment and management. IScience https://doi.org/10.1016/j.isci.2023.106736 (2023).

    Google Scholar 

  44. Budimir, M. et al. Opportunities and challenges for people-centered multi-hazard early warning systems: Perspectives from the Global South. iScience https://doi.org/10.1016/j.isci.2025.112353 (2025).

    Google Scholar 

  45. Bogaard, T. & Greco, R. Invited perspectives: hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds. Nat. Hazards Earth Syst. Sci. 18 (1), 31–39. https://doi.org/10.5194/nhess-18-31-2018 (2018).

    Google Scholar 

  46. Guzzetti, F., Peruccacci, S., Rossi, M. & Stark, C. P. The rainfall intensity–duration control of shallow landslides and debris flows: an update Vol. 5 (Landslides, 2008). https://doi.org/10.1007/s10346-007-0112-1.

    Google Scholar 

  47. Brigandì, G., Aronica, G. T., Bonaccorso, B., Gueli, R. & Basile, G. Flood and landslide warning based on rainfall thresholds and soil moisture indexes: the HEWS (Hydrohazards early warning System) for Sicily. Adv. Geosci. 44, 79–88. https://doi.org/10.5194/adgeo-44-79-2017 (2017).

    Google Scholar 

  48. Nájera, J. Z., Luna, C. C. & Upegui, J. J. V. Performance assessment of indicators of a multi-hazards early warning system in an urban mountain region. Int. J. Disaster Risk Reduct. 112, 104767. https://doi.org/10.1016/j.ijdrr.2024.104767 (2024).

    Google Scholar 

  49. Thompson, H. E., Gill, J. C., Šakić Trogrlić, R., Taylor, F. E. & Malamud, B. D. A methodology to compile multi-hazard interrelationships in a data-scarce setting: an application to Kathmandu Valley, Nepal. Nat. Haz. Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-25-353-2025 (2024).

    Google Scholar 

  50. Menteşe, E. Y. et al. Stakeholder Perceptions of Multi-hazards and Implications for Urban Disaster Risk Reduction in Istanbul (No. EGU22-10895). Copernicus Meetings. (2022). https://doi.org/10.5194/egusphere-egu22-10895

  51. Ybañez, R., Lagmay, A. M. & Malamud, B. A Systematic Overview of Hazard Interrelationships in the Philippines. InEGU General Assembly Conference Abstracts. Apr (p. 4275). (2024). https://doi.org/10.5194/egusphere-egu24-4275

  52. Kappes, M. S., Keiler, M., von Elverfeldt, K. & Glade, T. Challenges of analyzing multi-hazard risk: a review. Nat. Hazards. 64, 1925–1958. https://doi.org/10.1007/s11069-012-0294-2 (2012).

    Google Scholar 

  53. Silva, R. F., Marques, R. & Zêzere, J. L. Spatial distribution, Temporal trends and impact of landslides on São Miguel Island from 1900 to 2020 based on an analysis of the Azores historical natural hazards database. Nat. Hazards. 120 (3), 2617–2638. https://doi.org/10.1007/s11069-023-06296-0 (2024).

    Google Scholar 

  54. QGIS.org. QGIS Geographic Information System (Version 3.28). Open Source Geospatial Foundation Project. (2024). Available at: https://qgis.org

  55. Freeman, L. C., Borgatti, S. P. & White, D. R. Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks. 13 (2), 141–154. https://doi.org/10.1016/0378-8733(91)90017-N (1991).

    Google Scholar 

  56. Opsahl, T. Triadic closure in two-mode networks: redefining the global and local clustering coefficients. Soc. Netw. 35(2), 159–167. https://doi.org/10.1016/j.socnet.2011.07.001 (2013).

    Google Scholar 

  57. Brandes, U. A faster algorithm for betweenness centrality. J. Math. Sociol. 25 (2), 163–177. https://doi.org/10.1080/0022250X.2001.9990249 (2001).

    Google Scholar 

  58. Fischer, E. & Newman, I. May. Testing versus estimation of graph properties. In Proceedings of the thirty-seventh annual ACM symposium on Theory of computing (pp. 138–146). (2005). https://doi.org/10.1145/1060590.1060612

  59. Buzna, L., Peters, K. & Helbing, D. Modelling the dynamics of disaster spreading in networks. Phys. A: Stat. Mech. Its Appl. 363 (1), 132–140. https://doi.org/10.1016/j.physa.2006.01.059 (2006).

    Google Scholar 

  60. Alexander, D. A magnitude scale for cascading disasters. Int. J. Disaster Risk Reduct. 30, 180–185. https://doi.org/10.1016/j.ijdrr.2018.03.006 (2018).

    Google Scholar 

  61. Singh, A. et al. Unveiling the catastrophic landslide-induced flash flood in Teesta River, sikkim: insight from South Lhonak glacial lake. Landslides 22, 837–855. https://doi.org/10.1007/s10346-024-02378-7 (2025).

    Google Scholar 

  62. Saha, S. et al. Multiple drivers of the recent South Lhonak glacial lake outburst flood in Sikkim himalaya and its aftermath on Teesta river Valley. Geosyst. Geoenvironment. 4 (2), 100375. https://doi.org/10.1016/j.geogeo.2025.100375 (2025).

    Google Scholar 

  63. Vinodini Ramesh, M. et al. Mundakkai-Chooralmala landslide: assessment of initiation, progression, and impact. Sci. Rep. 15, 26961. https://doi.org/10.1038/s41598-025-07828-3 (2025).

    Google Scholar 

  64. South Asia Network on Dams, Rivers and People (SANDRP). Glacial lake flood destroys Teesta-3 dam in Sikkim, brings widespread destruction. 4 October. (2023). Available at: https://sandrp.in/2023/10/04/glacial-lake-flood-destroys-teesta-3-dam-in-sikkim-brings-wide-spread-destruction / (Accessed: 5th December 2025).

  65. Mittal, S. C., Sayeed, I., Hegde, U. V. & Raja, S. Challenges in execution of concrete face Rock-Fill dams in emerging economies. INCOLD J. (A Half Yrly. Tech. J. Indian Comm. Large Dams). 11 (1), 10–18 (2022).

    Google Scholar 

  66. Bureau of Indian Standards (BIS). IS 5225:1992: Meteorology, Rain Gauge, Non recording Specification. New Delhi: Bureau of Indian Standards. (1992). https://law.resource.org/pub/in/bis/S01/is.5225.1992.pdf

  67. India Meteorological Department (IMD). Standard Operating Procedure for Hydrometeorological Services and Rainfall Observation Networks (Ministry of Earth Sciences, 2021). https://mausam.imd.gov.in/imd_latest/contents/pdf/hydrology_sop.pdf#page=10.09Government of India.

  68. Malczewski, J. GIS-based multicriteria decision analysis: a survey of the literature. Int. J. Geogr. Inf. Sci. 20 (7), 703–726. https://doi.org/10.1080/13658810600661508 (2006).

    Google Scholar 

  69. de Brito, M. M. & Evers, M. Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Nat. Hazards Earth Syst. Sci. 16, 1019–1033. https://doi.org/10.5194/nhess-16-1019-2016 (2016).

    Google Scholar 

Download references

Acknowledgements

I thank our Chancellor, Sri Mata Amritanandamayi Devi (AMMA), for her exemplary vision of selfless service and compassion in addressing complex problems in communities worldwide. I would like to thank Dr. Aadityan Sridharan, Dr. Dhanya Madhu, Mr. Nitin Kumar M., Mr. Balmukund Singh, and Dr. Sabari Ramesh for their assistance during the field visits in Sikkim and Wayanad. I would like to thank Dr. Bhavani Rao and the entire team from the Amrita School of Social and Behavioral Sciences for their assistance in conducting the stakeholder consultations in Wayanad. I am grateful to the Amrita Live-in-Labs® academic program for providing all the support. Lastly, I sincerely thank the editors and reviewers for their careful evaluation and constructive comments, which have significantly improved the clarity, rigor, and overall quality of the manuscript.

Funding

Open access funding provided by Amrita Vishwa Vidyapeetham. This project has been funded by the E4LIFE International Ph.D. Fellowship program offered by Amrita Vishwa Vidyapeetham.

Author information

Authors and Affiliations

  1. Amrita School for Sustainable Futures, Amrita Vishwa Vidyapeetham, Amritapuri, India

    Hari Chandana Ekkirala & Maneesha Vinodini Ramesh

  2. Center for Wireless Networks and Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India

    Hari Chandana Ekkirala & Maneesha Vinodini Ramesh

Authors
  1. Hari Chandana Ekkirala
    View author publications

    Search author on:PubMed Google Scholar

  2. Maneesha Vinodini Ramesh
    View author publications

    Search author on:PubMed Google Scholar

Contributions

M.V.R. conceived the study, supervised the research, and provided critical feedback. H.C.E. contributed to data analysis, interpretation and wrote the manuscript. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Maneesha Vinodini Ramesh.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

All stakeholder interaction methods were carried out according to relevant guidelines and regulations, including institutional and national ethical standards. The study protocols were reviewed and approved by the ethical committee of Amrita Vishwa Vidyapeetham (Ref No: IHEC/2025/198). Local language-speaking volunteers were used to conduct stakeholder interactions, and informed consent was obtained from all participants involved in the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ekkirala, H.C., Ramesh, M.V. Spatiotemporal assessment of multi hazard risk using graph based analysis for case studies in India. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35925-4

Download citation

  • Received: 29 June 2025

  • Accepted: 08 January 2026

  • Published: 20 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-35925-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Rainfall-Triggered landslides
  • Multi-hazard sequences
  • Spatiotemporal progression
  • Hazard impact
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing