Natural Hazards

, Volume 79, Supplement 1, pp 291–314 | Cite as

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

  • Álvaro Gómez-Gutiérrez
  • Christian Conoscenti
  • Silvia Eleonora Angileri
  • Edoardo Rotigliano
  • Susanne Schnabel
Original Paper


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.


Gully erosion susceptibility Topography Topographical attributes Empirical multivariate models Digital elevation models (DEMs) 



Á. Gómez-Gutiérrez, C. Conoscenti, S. E. Angileri, E. Rotigliano and S. Schnabel have commonly shared all the parts of the research. Clare Hampton has linguistically edited the final version of this text. Finally, thanks to the Spanish Ministry of Science for economically supporting this work by AMID research project (CGL2011-23361).


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Álvaro Gómez-Gutiérrez
    • 1
  • Christian Conoscenti
    • 2
  • Silvia Eleonora Angileri
    • 2
  • Edoardo Rotigliano
    • 2
  • Susanne Schnabel
    • 1
  1. 1.Geoenvironmental Research GroupUniversity of ExtremaduraCáceresSpain
  2. 2.Department of Earth and Sea SciencesUniversity of PalermoPalermoItaly

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