IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v121y2025i13d10.1007_s11069-025-07384-z.html
   My bibliography  Save this article

Study on the temporal pattern and county-scale comprehensive risk assessment of wildfires in Sichuan Province

Author

Listed:
  • Weiting Yue

    (Southwest Jiaotong University)

  • Yunji Gao

    (Southwest Jiaotong University)

  • Yao Xiao

    (Southwest Jiaotong University)

  • Ziqun Ye

    (Southwest Jiaotong University)

  • Qian Zhao

    (Southwest Jiaotong University)

  • Yuchun Zhang

    (Southwest Jiaotong University)

Abstract

Climate change and increased human activity have resulted in an increase in the frequency and intensity of wildfires. Effective wildfire risk assessment is essential for disaster prevention, resource protection, and regional stability. Existing studies often overlook spatial heterogeneity and temporal patterns of wildfires, with limited county-scale quantitative assessments. To address these gaps, multidimensional wildfire risk assessment framework for Sichuan Province was proposed, combining temporal characterization with county-scale spatial modeling. Temporal trends and mutation patterns of wildfires from 2001 to 2023 were analyzed using the Mann–Kendall test. Additionally, county-scale wildfire risk assessment model in Sichuan Province was constructed by combining hazard and vulnerability assessments. Specifically, wildfire hazard was assessed using Multiscale Geographically Weighted Regression (MGWR) model and capturing the spatial heterogeneity of driving factors. Vulnerability was assessed through Multi-Criteria Decision Analysis approach to identify areas of high vulnerability and their factor importance. The results indicated a significant rise in wildfires, particularly during winter and non-fire prevention periods. The MGWR model effectively captured spatial heterogeneity, identifying the highest hazard levels in southwestern Sichuan, particularly in Liangshan Prefecture and Panzhihua City. High vulnerability areas were scattered, mainly across southwestern, southern, and northern Sichuan. The integrated risk assessment revealed that Liangshan Prefecture and its surrounding counties exhibited significantly higher wildfire risk levels than other regions, while the eastern and northeastern regions demonstrated the lowest risk.

Suggested Citation

  • Weiting Yue & Yunji Gao & Yao Xiao & Ziqun Ye & Qian Zhao & Yuchun Zhang, 2025. "Study on the temporal pattern and county-scale comprehensive risk assessment of wildfires in Sichuan Province," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(13), pages 15201-15238, July.
  • Handle: RePEc:spr:nathaz:v:121:y:2025:i:13:d:10.1007_s11069-025-07384-z
    DOI: 10.1007/s11069-025-07384-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-025-07384-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-025-07384-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Anastasia Zabaniotou & Anastasia Pritsa & E-A Kyriakou, 2021. "Observational Evidence of the Need for Gender-Sensitive Approaches to Wildfires Locally and Globally: Case Study of 2018 Wildfire in Mati, Greece," Sustainability, MDPI, vol. 13(3), pages 1-25, February.
    2. Pedcris M. Orencio & Masahiko Fujii, 2014. "A spatiotemporal approach for determining disaster-risk potential based on damage consequences of multiple hazard events," Journal of Risk Research, Taylor & Francis Journals, vol. 17(7), pages 815-836, August.
    3. Shailja Mamgain & Arijit Roy & Harish Chandra Karnatak & Prakash Chauhan, 2023. "Satellite-based long-term spatiotemporal trends of wildfire in the Himalayan vegetation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3779-3796, April.
    4. Hamid Reza Pourghasemi & Soheila Pouyan & Mojgan Bordbar & Foroogh Golkar & John J. Clague, 2023. "Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3797-3816, April.
    5. Hamid Reza Pourghasemi & Soheila Pouyan & Mojgan Bordbar & Foroogh Golkar & John J. Clague, 2023. "Correction to: Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 871-874, August.
    6. Saeedeh Eskandari & Mahdis Amiri & Nitheshnirmal Sãdhasivam & Hamid Reza Pourghasemi, 2020. "Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 305-327, October.
    7. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    8. Zühal Özcan & İnci Caglayan & Özgür Kabak & Fatmagül Kılıç Gül, 2025. "Integrated risk mapping for forest fire management using the analytical hierarchy process and ordered weighted average: a case study in southern Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(1), pages 959-1001, January.
    9. Marcos Rodrigues & Adrián Jiménez & Juan de la Riva, 2016. "Analysis of recent spatial–temporal evolution of human driving factors of wildfires in Spain," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 2049-2070, December.
    10. Maombi Mbusa Masinda & Fei Li & Liu Qi & Long Sun & Tongxin Hu, 2022. "Forest fire risk estimation in a typical temperate forest in Northeastern China using the Canadian forest fire weather index: case study in autumn 2019 and 2020," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 1085-1101, March.
    11. Deniz Arca & Mercan Hacısalihoğlu & Ş. Hakan Kutoğlu, 2020. "Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 73-89, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Miqueias Lima Duarte & Tatiana Acácio Silva & Jocy Ana Paixão Sousa & Amazonino Lemos Castro & Roberto Wagner Lourenço, 2025. "Application of a hybrid fuzzy inference system to map the susceptibility to fires," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(1), pages 1117-1141, January.
    2. Chuanrong Zhang & Xinba Li, 2025. "AI-Enhanced Remote Sensing of Land Transformations for Climate-Related Financial Risk Assessment in Housing Markets: A Review," Land, MDPI, vol. 14(8), pages 1-39, August.
    3. Rufai Yusuf Zakari & Owais Ahmed Malik & Ong Wee-Hong, 2025. "Machine learning-driven wildfire susceptibility mapping in New South Wales, Australia using remote sensing and explainable artificial intelligence," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(13), pages 15331-15357, July.
    4. Mojgan Bordbar & Gianluigi Busico & Stefania Stevenazzi & Micòl Mastrocicco, 2025. "How do hydrogeological and socio-economic parameters influence the likelihood of NO3− pollution and Cl− salinization? An application within the campania region (Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(11), pages 12887-12907, June.
    5. Fathima Nuzla Ismail & Brendon J. Woodford & Sherlock A. Licorish & Aubrey D. Miller, 2024. "An assessment of existing wildfire danger indices in comparison to one-class machine learning models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(15), pages 14837-14868, December.
    6. Fanfan Huang & Dan Zhu & Yichen Zhang & Jiquan Zhang & Ning Wang & Zhennan Dong, 2024. "Urban Flooding Disaster Risk Assessment Utilizing the MaxEnt Model and Game Theory: A Case Study of Changchun, China," Sustainability, MDPI, vol. 16(19), pages 1-23, October.
    7. Katharina Hecht & Abraham Ortega Reboso & Michelle van der Vegt & Jaco Appelman & Maibritt Pedersen Zari, 2024. "Ecologically Regenerative Building Systems through Exergy Efficiency: Designing for Structural Order and Ecosystem Services," Land, MDPI, vol. 13(9), pages 1-18, August.
    8. Mohd Amin Khan & Pritee Sharma & Mohanasundari Thangavel & Mashkoor Ahmad, 2024. "Spatio-temporal dynamics of wildfires in Hoshangabad Forest Division of Central India: a geospatial and statistical investigation," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-23, December.
    9. Reza Banai, 2010. "Evaluation of land use-transportation systems with the Analytic Network Process," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(1), pages 85-112.
    10. Pishchulov, Grigory & Trautrims, Alexander & Chesney, Thomas & Gold, Stefan & Schwab, Leila, 2019. "The Voting Analytic Hierarchy Process revisited: A revised method with application to sustainable supplier selection," International Journal of Production Economics, Elsevier, vol. 211(C), pages 166-179.
    11. Seung-Jin Han & Won-Jae Lee & So-Hee Kim & Sang-Hoon Yoon & Hyunwoong Pyun, 2022. "Assessing Expected Long-term Benefits for the Olympic Games: Delphi-AHP Approach from Korean Olympic Experts," SAGE Open, , vol. 12(4), pages 21582440221, December.
    12. Kamila Hodasová & Dávid Krčmář & Ivana Ondrejková, 2025. "Satellite-based drought assessment: integrating AHP method and fuzzy logic for comprehensive vulnerability and risk analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(10), pages 11609-11632, June.
    13. Seyed Rakhshan & Ali Kamyad & Sohrab Effati, 2015. "Ranking decision-making units by using combination of analytical hierarchical process method and Tchebycheff model in data envelopment analysis," Annals of Operations Research, Springer, vol. 226(1), pages 505-525, March.
    14. V. Srinivasan & G. Shainesh & Anand K. Sharma, 2015. "An approach to prioritize customer-based, cost-effective service enhancements," The Service Industries Journal, Taylor & Francis Journals, vol. 35(14), pages 747-762, October.
    15. Mónica García-Melón & Blanca Pérez-Gladish & Tomás Gómez-Navarro & Paz Mendez-Rodriguez, 2016. "Assessing mutual funds’ corporate social responsibility: a multistakeholder-AHP based methodology," Annals of Operations Research, Springer, vol. 244(2), pages 475-503, September.
    16. Suraj Das, 2024. "Women's experiences and sustainable adaptation: a socio-ecological study of climate change in the Himalayas," Climatic Change, Springer, vol. 177(4), pages 1-25, April.
    17. Panagiotis Ravanos & Giannis Karagiannis, 2023. "Correction: A VEA Benefit-of-the-Doubt Model for the HDI," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 170(2), pages 793-796, November.
    18. Luis Pérez-Domínguez & Luis Alberto Rodríguez-Picón & Alejandro Alvarado-Iniesta & David Luviano Cruz & Zeshui Xu, 2018. "MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making," Complexity, Hindawi, vol. 2018, pages 1-10, April.
    19. Paul L. G. Vlek & Asia Khamzina & Hossein Azadi & Anik Bhaduri & Luna Bharati & Ademola Braimoh & Christopher Martius & Terry Sunderland & Fatemeh Taheri, 2017. "Trade-Offs in Multi-Purpose Land Use under Land Degradation," Sustainability, MDPI, vol. 9(12), pages 1-19, November.
    20. Kumar B, Pradeep, 2021. "Changing Objectives of Firms and Managerial Preferences: A Review of Models in Microeconomics," MPRA Paper 106967, University Library of Munich, Germany, revised 13 Mar 2021.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:121:y:2025:i:13:d:10.1007_s11069-025-07384-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.