IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v105y2021i1d10.1007_s11069-020-04324-x.html
   My bibliography  Save this article

The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models

Author

Listed:
  • Frederico F. Ávila

    (National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN)
    National Institute for Space Research (INPE))

  • Regina C. Alvalá

    (National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN)
    National Institute for Space Research (INPE))

  • Rodolfo M. Mendes

    (National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN)
    Vale do Paraíba University (UNIVAP/IPeD))

  • Diogo J. Amore

    (National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN))

Abstract

The objective of this study was to use physically based models to carry out a back-analysis of the set of factors that may have influenced slope instability and the consequent development of 65 landslides in the Bengalar Stream basin, located in the Northern Region of São José dos Campos, São Paulo State, Brazil, associated with rainfall between March 7 and 8, 2016. Unlike other models, the FS FIORI model used in this study allowed extra variables to be added to the model that can influence hillslope stability and is associated with land use and land cover (LULC) variability. Analysis of intense short-term and accumulated long-term rainfall influence on slope instability was possible via a TRIGRS model. A comparative analysis was also carried out between a static model (FS FIORI) and a transient model (TRIGRS) which considered the factor of safety and pore pressure to be a function of precipitation and infiltration rates. Despite the differences in their hydrological components, both models were shown to present relatively similar and demonstrated stability rates coherence, according to the characteristics of each model. The FS FIORI model only classified 1.3% of the entire basin as unstable (FS ≤ 1), whereas the TRIGRS model classified 4.5% and 2.9% of the entire basin as unstable in scenarios 1 and 2, respectively. The validity and the accuracy of each model were tested via a receiver operating characteristic (ROC) curve and an area under the curve (AUC). AUC values were: 0.6552 for the FS FIORI model, and 0.7238 and 0.7186 for scenarios 1 and 2 of the TRIGRS model, respectively. The models performed well, with values considered to be acceptable. These results demonstrate an advancement in slope stability modeling studies, including conditioning factors associated with LULC for slope stability calculations.

Suggested Citation

  • Frederico F. Ávila & Regina C. Alvalá & Rodolfo M. Mendes & Diogo J. Amore, 2021. "The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based 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. 105(1), pages 1139-1161, January.
    • Handle: RePEc:spr:nathaz:v:105:y:2021:i:1:d:10.1007_s11069-020-04324-x
    DOI: 10.1007/s11069-020-04324-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-020-04324-x
    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/s11069-020-04324-x?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Gökçe Hasekioğulları & Murat Ercanoglu, 2012. "A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW 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. 63(2), pages 1157-1179, September.
    2. A. Carrara & F. Guzzetti & M. Cardinali & P. Reichenbach, 1999. "Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard," 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. 20(2), pages 117-135, November.
    3. Téhrrie König & Hermann J. H. Kux & Rodolfo M. Mendes, 2019. "Shalstab mathematical model and WorldView-2 satellite images to identification of landslide-susceptible areas," 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. 97(3), pages 1127-1149, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Frederico Fernandes Ávila & Regina C. Alvalá & Rodolfo M. Mendes & Diogo J. Amore, 2024. "Socio-geoenvironmental vulnerability index (SGeoVI) derived from hybrid modeling related to populations at-risk to landslides," 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(9), pages 8121-8151, July.
    2. Luca Schilirò & Gian Marco Marmoni & Matteo Fiorucci & Massimo Pecci & Gabriele Scarascia Mugnozza, 2023. "Preliminary insights from hydrological field monitoring for the evaluation of landslide triggering conditions over large areas," 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(2), pages 1401-1426, September.

    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. Moumita Palchaudhuri & Sujata Biswas, 2016. "Application of AHP with GIS in drought risk assessment for Puruliya district, India," 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 1905-1920, December.
    2. Gökhan Demir & Mustafa Aytekin & Aykut Akgün & Sabriye İkizler & Orhan Tatar, 2013. "A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process 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. 65(3), pages 1481-1506, February.
    3. Frederico Fernandes Ávila & Regina C. Alvalá & Rodolfo M. Mendes & Diogo J. Amore, 2024. "Socio-geoenvironmental vulnerability index (SGeoVI) derived from hybrid modeling related to populations at-risk to landslides," 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(9), pages 8121-8151, July.
    4. Md. Arif Chowdhury & Hasnat Sabrina & Rashed Uz Zzaman & Syed Labib Ul Islam, 2022. "Green building aspects in Bangladesh: A study based on experts opinion regarding climate change," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(7), pages 9260-9284, July.
    5. Amin Salehpour Jam & Jamal Mosaffaie & Faramarz Sarfaraz & Samad Shadfar & Rouhangiz Akhtari, 2021. "GIS-based landslide susceptibility mapping using hybrid MCDM 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. 108(1), pages 1025-1046, August.
    6. Marko Sinčić & Sanja Bernat Gazibara & Martin Krkač & Hrvoje Lukačić & Snježana Mihalić Arbanas, 2022. "The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments," Land, MDPI, vol. 11(8), pages 1-37, August.
    7. Mehrnoosh Jadda & Helmi Shafri & Shattri Mansor, 2011. "PFR model and GiT for landslide susceptibility mapping: a case study from Central Alborz, 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. 57(2), pages 395-412, May.
    8. Paul Sestraș & Ștefan Bilașco & Sanda Roșca & Sanda Naș & Mircea V. Bondrea & Raluca Gâlgău & Ioel Vereș & Tudor Sălăgean & Velibor Spalević & Sorin M. Cîmpeanu, 2019. "Landslides Susceptibility Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan Area," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    9. Qihang Huang & Shahid Azam, 2023. "A Numerical-Hierarchical Framework for Predicting Volume Changes in Expansive Soils under Variable Surface and Weather Conditions," Sustainability, MDPI, vol. 15(5), pages 1-19, February.
    10. I. Fustos & R. Abarca-del-Rio & P. Moreno-Yaeger & M. Somos-Valenzuela, 2020. "Rainfall-Induced Landslides forecast using local precipitation and global climate indexes," 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. 102(1), pages 115-131, May.
    11. Zhi-hua Yang & Heng-xing Lan & Xing Gao & Lang-ping Li & Yun-shan Meng & Yu-ming Wu, 2015. "Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China," 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. 75(3), pages 2467-2487, February.
    12. Erica Akemi Goto & Keith Clarke, 2021. "Using expert knowledge to map the level of risk of shallow landslides in Brazil," 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. 108(2), pages 1701-1729, September.
    13. Malcolm Anderson & Liz Holcombe & Rob Flory & Jean-Philippe Renaud, 2008. "Implementing low-cost landslide risk reduction: a pilot study in unplanned housing areas of the Caribbean," 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. 47(3), pages 297-315, December.
    14. Gerardo Grelle & Antonietta Rossi & Paola Revellino & Luigi Guerriero & Francesco Maria Guadagno & Giuseppe Sappa, 2019. "Assessment of Debris-Flow Erosion and Deposit Areas by Morphometric Analysis and a GIS-Based Simplified Procedure: A Case Study of Paupisi in the Southern Apennines," Sustainability, MDPI, vol. 11(8), pages 1-20, April.
    15. Jaydip Dey & Saurabh Sakhre & Ritesh Vijay & Hemant Bherwani & Rakesh Kumar, 2021. "Geospatial assessment of urban sprawl and landslide susceptibility around the Nainital lake, Uttarakhand, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 3543-3561, March.
    16. Mowen Xie & Tetsuro Esaki & Guoyun Zhou, 2004. "GIS-Based Probabilistic Mapping of Landslide Hazard Using a Three-Dimensional Deterministic Model," 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. 33(2), pages 265-282, October.
    17. Shah Naseer & Tanveer Ul Haq & Abdullah Khan & Javed Iqbal Tanoli & Nangyal Ghani Khan & Faizan-ur-Rehman Qaiser & Syed Tallataf Hussain Shah, 2021. "GIS-based spatial landslide distribution analysis of district Neelum, AJ&K, Pakistan," 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. 106(1), pages 965-989, March.
    18. Christos Polykretis & Christos Chalkias, 2018. "Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network 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. 93(1), pages 249-274, August.
    19. Guru Balamurugan & Veerappan Ramesh & Mangminlen Touthang, 2016. "Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India," 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(1), pages 465-488, October.
    20. Kyle W. Rowden & Mohamed H. Aly, 2018. "A novel triggerless approach for mass wasting susceptibility modeling applied to the Boston Mountains of Arkansas, USA," 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. 92(1), pages 347-367, May.

    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:105:y:2021:i:1:d:10.1007_s11069-020-04324-x. 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.