IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v79y2015i1p291-314.html
   My bibliography  Save this article

Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations

Author

Listed:
  • Álvaro Gómez-Gutiérrez
  • Christian Conoscenti
  • Silvia Angileri
  • Edoardo Rotigliano
  • Susanne Schnabel

Abstract

Empirical multivariate predictive models represent an important tool to estimate gully erosion susceptibility. Topography, lithology, climate, land use and vegetation cover are commonly used as input for these approaches. In this paper, two multivariate predictive models were generated for two gully erosion processes in San Giorgio basin (Italy) and Mula River basin (Spain) using only topographical attributes as independent variables. Initially, nine models (five for San Giorgio and four for Mula) with pixel sizes ranging from 2 to 50 m were generated, and validation statistics were calculated to estimate the optimal pixel size. The best models were selected based on model performance using the area under the receiver operating characteristic (AUC) curve and the generalized cross-validation. The best pixel size was 4 m in the San Giorgio basin and 20 m in the Mula basin. The finest resolution was not necessarily the best; rather, the relationship between digital elevation model resolution and size of the landform was important. The two selected models showed an excellent performance with AUC values of 0.859 and 0.826 for San Giorgio and Mula, respectively. The Topographic Wetness Index and the general curvature were identified as key topographical attributes in San Giorgio and Mula basins, respectively. Both attributes were related to the processes observed in the field and described in the literature. Finally, maps of gully erosion susceptibility were produced for each basin. These maps showed that 22 and 20 % of San Giorgio and Mula basins, respectively, present favourable conditions for the development of gullies. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Álvaro Gómez-Gutiérrez & Christian Conoscenti & Silvia Angileri & Edoardo Rotigliano & Susanne Schnabel, 2015. "Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations," 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. 79(1), pages 291-314, November.
    • Handle: RePEc:spr:nathaz:v:79:y:2015:i:1:p:291-314
    DOI: 10.1007/s11069-015-1703-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-015-1703-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11069-015-1703-0?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. Massimo Conforti & Pietro Aucelli & Gaetano Robustelli & Fabio Scarciglia, 2011. "Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, 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. 56(3), pages 881-898, March.
    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. Omid Rahmati & Ali Haghizadeh & Hamid Reza Pourghasemi & Farhad Noormohamadi, 2016. "Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison," 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. 82(2), pages 1231-1258, June.
    2. Shahab S. Band & Saeid Janizadeh & Sunil Saha & Kaustuv Mukherjee & Saeid Khosrobeigi Bozchaloei & Artemi Cerdà & Manouchehr Shokri & Amirhosein Mosavi, 2020. "Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data," Land, MDPI, vol. 9(10), pages 1-23, September.
    3. Hone-Jay Chu & Yi-Chin Chen & Muhammad Zeeshan Ali & Bernhard Höfle, 2019. "Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland," IJERPH, MDPI, vol. 16(7), pages 1-12, March.
    4. Hamed Ahmadpour & Ommolbanin Bazrafshan & Elham Rafiei-Sardooi & Hossein Zamani & Thomas Panagopoulos, 2021. "Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection," Sustainability, MDPI, vol. 13(18), pages 1-24, September.
    5. Paschalis Koutalakis & Georgios Gkiatas & Michael Xinogalos & Valasia Iakovoglou & Iordanis Kasapidis & Georgios Pagonis & Anastasia Savvopoulou & Konstantinos Krikopoulos & Theodoros Klepousniotis & , 2024. "Estimating Stream Bank and Bed Erosion and Deposition with Innovative and Traditional Methods," Land, MDPI, vol. 13(2), pages 1-29, February.
    6. Ahmad Rajabi & Saeid Shabanlou & Fariborz Yosefvand & Afshin Kiani, 2021. "Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, 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. 109(1), pages 871-901, October.
    7. Ali Azedou & Said Lahssini & Abdellatif Khattabi & Modeste Meliho & Nabil Rifai, 2021. "A Methodological Comparison of Three Models for Gully Erosion Susceptibility Mapping in the Rural Municipality of El Faid (Morocco)," Sustainability, MDPI, vol. 13(2), pages 1-30, January.

    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. Jinzhao Li & Meilan Qi, 2015. "Local scour induced by upstream riverbed level lowering," 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. 77(3), pages 1811-1827, July.
    2. Rui-Xuan Tang & E-Chuan Yan & Tao Wen & Xiao-Meng Yin & Wei Tang, 2021. "Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 13(7), pages 1-25, March.
    3. Ali Azedou & Said Lahssini & Abdellatif Khattabi & Modeste Meliho & Nabil Rifai, 2021. "A Methodological Comparison of Three Models for Gully Erosion Susceptibility Mapping in the Rural Municipality of El Faid (Morocco)," Sustainability, MDPI, vol. 13(2), pages 1-30, January.
    4. Hamed Ahmadpour & Ommolbanin Bazrafshan & Elham Rafiei-Sardooi & Hossein Zamani & Thomas Panagopoulos, 2021. "Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection," Sustainability, MDPI, vol. 13(18), pages 1-24, September.
    5. Kennedy Were & Syphyline Kebeney & Harrison Churu & James Mumo Mutio & Ruth Njoroge & Denis Mugaa & Boniface Alkamoi & Wilson Ng’etich & Bal Ram Singh, 2023. "Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya," Land, MDPI, vol. 12(4), pages 1-19, April.
    6. Silvija Šiljeg & Rina Milošević & Marica Mamut, 2024. "Pluvial Flood Susceptibility in the Local Community of the City of Gospić (Croatia)," Sustainability, MDPI, vol. 16(4), pages 1-20, February.
    7. Anna Małka, 2021. "Landslide susceptibility mapping of Gdynia using geographic information system-based statistical 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. 107(1), pages 639-674, May.
    8. Francesca Vergari, 2015. "Assessing soil erosion hazard in a key badland area of Central 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. 79(1), pages 71-95, November.
    9. Sandipta Debanshi & Swades Pal, 2020. "Assessing gully erosion susceptibility in Mayurakshi river basin of eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 883-914, February.
    10. Marta Jurchescu & Florina Grecu, 2015. "Modelling the occurrence of gullies at two spatial scales in the Olteţ Drainage Basin (Romania)," 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. 79(1), pages 255-289, November.
    11. Rabin Chakrabortty & Subodh Chandra Pal & Mehebub Sahana & Ayan Mondal & Jie Dou & Binh Thai Pham & Ali P. Yunus, 2020. "Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern 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. 104(2), pages 1259-1294, November.
    12. Swades Pal & Sandipta Debanshi, 2018. "Influences of soil erosion susceptibility toward overloading vulnerability of the gully head bundhs in Mayurakshi River basin of eastern Chottanagpur Plateau," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(4), pages 1739-1775, August.
    13. Omid Rahmati & Ali Haghizadeh & Stefanos Stefanidis, 2016. "Assessing the Accuracy of GIS-Based Analytical Hierarchy Process for Watershed Prioritization; Gorganrood River Basin, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1131-1150, February.
    14. Hang Ha & Chinh Luu & Quynh Duy Bui & Duy-Hoa Pham & Tung Hoang & Viet-Phuong Nguyen & Minh Tuan Vu & Binh Thai Pham, 2021. "Flash flood susceptibility prediction mapping for a road network using hybrid 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. 109(1), pages 1247-1270, October.
    15. José Chávez Hernandez & Jiři Šebesta & Lubomir Kopecky & Reynaldo Landaverde, 2014. "Application of geomorphologic knowledge for erosion hazard mapping," 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. 71(3), pages 1323-1354, April.
    16. Narges Javidan & Ataollah Kavian & Christian Conoscenti & Zeinab Jafarian & Mahin Kalehhouei & Raana Javidan, 2024. "Development of risk maps for flood, landslide, and soil erosion using machine learning 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. 120(13), pages 11987-12010, October.
    17. Michael R. Coughlan & Donald R. Nelson & Michael Lonneman & Ashley E. Block, 2017. "Historical Land Use Dynamics in the Highly Degraded Landscape of the Calhoun Critical Zone Observatory," Land, MDPI, vol. 6(2), pages 1-20, 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:79:y:2015:i:1:p:291-314. 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.