RETURN Project - PNRR
Published April 1, 2026 | Version v1
Dataset Open

RETURN landslide data: data for analysis and simulated scenarios

Authors/Creators

  • 1. ROR icon Sapienza University of Rome
  • 2. ROR icon University of Naples Federico II

Description

Basic data required for the simulation of PARFISAL landslide scenarios in terms of the factor of safety (FS), considering different soil saturation conditions and allowing the contribution of seismic loading to be evaluated, including an estimate of displacement.

-   2 Main folder, "VTB-Costal" & "VTB-Inland"
-   2 Sub-folder: "data" & "simulated-scenarios”.
-   "data": 2 Sub-folder: "dataextra" & "dataterre".
-   “dataextra” contains: “RETURN-Ville.shp”; “SU- Mask.shp” 
-   "dataterre" contains: “classes.tif”; “cohesion.tif”; “dem.tif”; “friction_angle.tif”; 
              “Grid-Parsifal-Case-01.tif”; “hor_pseudostatic_coef.tif”; 
              “interstitial_pressure.tif”; “natural_weight.tif”; “slope.tif”; “thickness.tif”.
-    "simulated-scenarios”: 3 Sub-folder: “Dry” & “Wet-Hist” & “Wet-Fut”
              all these folders contain 2 subfolders: "static" and "seismic". 
              Each of them contains the following files: 
              “FS_.tif”; “Displacement_.tif”; “FS_vsu.shp”; “DIS_vsu.shp”

Files

Landslide.zip

Files (194.9 MB)

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md5:6123201c4c53b2ca43cecdd75f669af3
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Additional details

References

  • Esposito, C., Martino, S., Pallone, F., Martini, G., & Romeo, R. W. (2016). A methodology for a comprehensive assessment of earthquake-induced landslide hazard, with an application to pilot sites in Central Italy. In Landslides and engineered slopes. Experience, theory and practice (pp. 869-877). CRC Press.
  • Martino, S., Battaglia, S., Delgado, J., Esposito, C., Martini, G., & Missori, C. (2018). Probabilistic approach to provide scenarios of earthquake-induced slope failures (PARSIFAL) applied to the alcoy basin (South Spain). Geosciences, 8(2), 57
  • Martino, S., Battaglia, S., D'alessandro, F., Della Seta, M., Esposito, C., Martini, G., ... & Troiani, F. (2020). Earthquake-induced landslide scenarios for seismic microzonation: Application to the Accumoli area (Rieti, Italy). Bulletin of Earthquake Engineering, 18(12), 5655-5673.
  • Schilirò, L., Poueme Djueyep, G., Esposito, C., Scarascia Mugnozza, G. (2019). The Role of Initial Soil Conditions in Shallow Landslide Triggering: Insights from Physically Based Approaches. Geofluids, 2453786, 14 pages. https://doi.org/10.1155/2019/2453786.
  • Giannini, L. M., Varone, C., Esposito, C., Marmoni, G. M., Scarascia Mugnozza, G., & Schilirò, L. (2022). Earthquake-induced reactivation of landslides under variable hydrostatic conditions: evaluation at regional scale and implications for risk assessment. Landslides, 19(8), 2005-2019.
  • Schlögel, R., Marchesini, I., Alvioli, M., Reichenbach, P., Rossi, M., & Malet, J. P. (2018). Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models. Geomorphology, 301, 10-20.
  • Tanyas, H., Rossi, M., Alvioli, M., van Westen, C. J., & Marchesini, I. (2019). A global slope unit-based method for the near real-time prediction of earthquake-induced landslides. Geomorphology, 327, 126-146.
  • Rollo, F., & Rampello, S. (2023). Influence of the displacement predictive relationships on the probabilistic seismic analysis of slopes. Journal of geotechnical and geoenvironmental engineering, 149(6), 04023033.