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

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  • 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

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  • 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
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    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.
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    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.

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