IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i24p11273-d1550274.html
   My bibliography  Save this article

Exploring the Interaction Between Landslides and Carbon Stocks in Italy

Author

Listed:
  • Jibran Qadri

    (Department of Civil, Environmental and Architectural Engineering (DICEA), University of Padova, Via Ognissanti, 39, 35129 Padova, Italy)

  • Francesca Ceccato

    (Department of Civil, Environmental and Architectural Engineering (DICEA), University of Padova, Via Ognissanti, 39, 35129 Padova, Italy)

Abstract

Landslides, as natural hazards, have far-reaching impacts beyond their immediate effects on human lives and infrastructure; landslides disrupt both carbon storage and ecosystem stability, and their role in the global carbon cycle cannot be underestimated. This study delves into the complex relationship between landslides and carbon stocks such as, in particular, soil organic carbon (SOC) and above-ground biomass (AGB), and outlines the spatial relationship between different types of landslides, soil organic carbon (SOC), and the carbon cycle, underscoring the importance of understanding these interconnections for environmental sustainability and climate change mitigation efforts. By employing machine learning algorithms on the Google Earth Engine platform, landslide susceptibility maps were created for different landslide types across Italy, and their spatial patterns with SOC accumulation were analyzed using the Python environment. The findings reveal a nuanced relationship between landslide hazard levels and SOC dynamics, with varying trends observed for different landslide types. In addition, this study investigates the potential impact of large-scale landslide events on carbon sequestration in the short term via a case study of the May 2023 landslide event in the Emilia Romagna region of Italy. The analysis reveals a substantial reduction in above-ground biomass by 35%, which approximately accounts for the loss of 0.133 MtC, and a decrease in SOC accumulation in 72% of the affected areas, indicating that landslides can transform carbon sinks into carbon sources, at least in the short term, and suggested that carbon released from extreme landslide events at a larger scale needs to be accounted for in regional or national carbon emissions. This research underscores the importance of considering landslides in carbon cycle assessments and emphasizes the need for sustainable land management strategies to protect and enhance carbon sinks, such as forests and healthy soils, in the face of increasing natural hazards and climate change impacts.

Suggested Citation

  • Jibran Qadri & Francesca Ceccato, 2024. "Exploring the Interaction Between Landslides and Carbon Stocks in Italy," Sustainability, MDPI, vol. 16(24), pages 1-27, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:11273-:d:1550274
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/24/11273/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/24/11273/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Taskin Kavzoglu & Emrehan Kutlug Sahin & Ismail Colkesen, 2015. "An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district," 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. 76(1), pages 471-496, March.
    2. C. Freeman & N. Fenner & N. J. Ostle & H. Kang & D. J. Dowrick & B. Reynolds & M. A. Lock & D. Sleep & S. Hughes & J. Hudson, 2004. "Export of dissolved organic carbon from peatlands under elevated carbon dioxide levels," Nature, Nature, vol. 430(6996), pages 195-198, July.
    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. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    2. Di Wang & Mengmeng Hao & Shuai Chen & Ze Meng & Dong Jiang & Fangyu Ding, 2021. "Assessment of landslide susceptibility and risk factors in 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. 108(3), pages 3045-3059, September.
    3. J Timothy Wootton & Catherine A Pfister, 2012. "Carbon System Measurements and Potential Climatic Drivers at a Site of Rapidly Declining Ocean pH," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-11, December.
    4. Farrelly, Damien J. & Everard, Colm D. & Fagan, Colette C. & McDonnell, Kevin P., 2013. "Carbon sequestration and the role of biological carbon mitigation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 712-727.
    5. Jutras, Marie-France & Nasr, Mina & Castonguay, Mark & Pit, Christopher & Pomeroy, Joseph H. & Smith, Todd P. & Zhang, Cheng-fu & Ritchie, Charles D. & Meng, Fan-Rui & Clair, Thomas A. & Arp, Paul A., 2011. "Dissolved organic carbon concentrations and fluxes in forest catchments and streams: DOC-3 model," Ecological Modelling, Elsevier, vol. 222(14), pages 2291-2313.
    6. Siyuan Ma & Chong Xu, 2019. "Assessment of co-seismic landslide hazard using the Newmark model and statistical analyses: a case study of the 2013 Lushan, China, Mw6.6 earthquake," 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. 96(1), pages 389-412, March.
    7. Yang, Yuangen & He, Zhenli & Wang, Yanbo & Fan, Jinghua & Liang, Zhanbei & Stoffella, Peter J., 2013. "Dissolved organic matter in relation to nutrients (N and P) and heavy metals in surface runoff water as affected by temporal variation and land uses – A case study from Indian River Area, south Florid," Agricultural Water Management, Elsevier, vol. 118(C), pages 38-49.
    8. Ianis Delpla & Donald T. Monteith & Chris Freeman & Joris Haftka & Joop Hermens & Timothy G. Jones & Estelle Baurès & Aude-Valérie Jung & Olivier Thomas, 2014. "A Decision Support System for Drinking Water Production Integrating Health Risks Assessment," IJERPH, MDPI, vol. 11(7), pages 1-22, July.
    9. Binh Thai Pham & Ataollah Shirzadi & Himan Shahabi & Ebrahim Omidvar & Sushant K. Singh & Mehebub Sahana & Dawood Talebpour Asl & Baharin Bin Ahmad & Nguyen Kim Quoc & Saro Lee, 2019. "Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
    10. Quang-Khanh Nguyen & Dieu Tien Bui & Nhat-Duc Hoang & Phan Trong Trinh & Viet-Ha Nguyen & Isık Yilmaz, 2017. "A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS," Sustainability, MDPI, vol. 9(5), pages 1-24, May.
    11. 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.
    12. Wamba Danny Love Djukem & Anika Braun & Armand Sylvain Ludovic Wouatong & Christian Guedjeo & Katrin Dohmen & Pierre Wotchoko & Tomas Manuel Fernandez-Steeger & Hans-Balder Havenith, 2020. "Effect of Soil Geomechanical Properties and Geo-Environmental Factors on Landslide Predisposition at Mount Oku, Cameroon," IJERPH, MDPI, vol. 17(18), pages 1-27, September.
    13. Jihye Han & Jinsoo Kim & Soyoung Park & Sanghun Son & Minji Ryu, 2020. "Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    14. Rahim Tavakolifar & Himan Shahabi & Mohsen Alizadeh & Sayed M. Bateni & Mazlan Hashim & Ataollah Shirzadi & Effi Helmy Ariffin & Isabelle D. Wolf & Saman Shojae Chaeikar, 2023. "Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran," Land, MDPI, vol. 12(6), pages 1-19, May.
    15. Fatiha Debiche & Mohammed Amin Benbouras & Alexandru-Ionut Petrisor & Lyes Mohamed Baba Ali & Abdelghani Leghouchi, 2024. "Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach," Land, MDPI, vol. 13(6), pages 1-29, June.
    16. Marcin Becher & Dorota Kalembasa & Stanisław Kalembasa & Barbara Symanowicz & Dawid Jaremko & Adam Matyszczak, 2023. "A New Method for Sequential Fractionation of Nitrogen in Drained Organic (Peat) Soils," IJERPH, MDPI, vol. 20(3), pages 1-16, January.
    17. Yue Wang & Deliang Sun & Haijia Wen & Hong Zhang & Fengtai Zhang, 2020. "Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)," IJERPH, MDPI, vol. 17(12), pages 1-39, June.
    18. Francisco Parra & Jaime González & Max Chacón & Mauricio Marín, 2023. "Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile," Sustainability, MDPI, vol. 15(24), pages 1-31, December.
    19. L. Lombardo & G. Fubelli & G. Amato & M. Bonasera, 2016. "Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, 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. 84(1), pages 565-588, October.
    20. Xudong Hu & Hongbo Mei & Han Zhang & Yuanyuan Li & Mengdi Li, 2021. "Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest 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. 105(2), pages 1663-1689, January.

    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:gam:jsusta:v:16:y:2024:i:24:p:11273-:d:1550274. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.