DOI: 10.1002/esp.5770 ISSN: 0197-9337

Spatial transferability of the physically based model TRIGRS using parameter ensembles

Lotte de Vugt, Thomas Zieher, Barbara Schneider‐Muntau, Mateo Moreno, Stefan Steger, Martin Rutzinger
  • Earth and Planetary Sciences (miscellaneous)
  • Earth-Surface Processes
  • Geography, Planning and Development

Abstract

The development of better, more reliable and more efficient susceptibility assessments for shallow landslides is becoming increasingly important. Physically based models are well‐suited for this, due to their high predictive capability. However, their demands for large, high‐resolution and detailed input datasets make them very time‐consuming and costly methods. This study investigates if a spatially transferable model calibration can be created with the use of parameter ensembles and with this alleviate the time‐consuming calibration process of these methods. To investigate this, the study compares the calibration of the model TRIGRS in two different study areas. The first study area was taken from a previous study where the dynamic physically based model TRIGRS was calibrated for the Laternser valley in Vorarlberg, Austria. The calibrated parameter ensemble and its performance from this previous study are compared with a calibrated parameter ensemble of the model TRIGRS for the Passeier valley in South Tyrol, Italy. The comparison showed very similar model performance and large similarities in the calibrated geotechnical parameter values of the best model runs in both study areas. There is a subset of calibrated geotechnical parameter values that can be used successfully in both study areas and potentially other study areas with similar lithological characteristics. For the hydraulic parameters, the study did not find a transferable parameter subset. These parameters seem to be more sensitive to different soil types. Additionally, the results of the study also showed the importance of the inclusion of detailed information on the timing of landslide initiation in the calibration of the model.

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