Articles | Volume 23, issue 4
https://doi.org/10.5194/nhess-23-1483-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-1483-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Apr 2023
Research article |
| 21 Apr 2023
| 21 Apr 2023
Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Center for Climate Change and Transformation, Eurac Research, Bolzano-Bozen, Italy
Mateo Moreno
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Center for Climate Change and Transformation, Eurac Research, Bolzano-Bozen, Italy
Alice Crespi
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Center for Climate Change and Transformation, Eurac Research, Bolzano-Bozen, Italy
Peter James Zellner
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Stefano Luigi Gariano
CNR IRPI, Perugia, Italy
Maria Teresa Brunetti
CNR IRPI, Perugia, Italy
Massimo Melillo
CNR IRPI, Perugia, Italy
Silvia Peruccacci
CNR IRPI, Perugia, Italy
Francesco Marra
Department of Geosciences, University of Padova, Padua, Italy
Institute of Atmospheric Sciences and Climate, National Research Council (CNR-ISAC), Bologna, Italy
Robin Kohrs
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Department of Geography, Friedrich Schiller University Jena, Jena, Germany
Jason Goetz
Department of Geography, Friedrich Schiller University Jena, Jena, Germany
Volkmar Mair
Office for Geology and Building Materials Testing, Autonomous Province of Bolzano – South Tyrol, Cardano, Italy
Massimiliano Pittore
Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
Center for Climate Change and Transformation, Eurac Research, Bolzano-Bozen, Italy
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Cited
18 citations as recorded by crossref.
- Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China Z. Xu et al. 10.3390/su16104044
- From spatio-temporal landslide susceptibility to landslide risk forecast T. Wang et al. 10.1016/j.gsf.2023.101765
- Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds S. Steger et al. 10.1016/j.gsf.2024.101822
- On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values N. Wang et al. 10.1016/j.gsf.2024.101800
- Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system M. Ahmed et al. 10.1016/j.jag.2023.103593
- Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning M. Di Napoli et al. 10.1016/j.catena.2024.108452
- Border-independent multi-functional, multi-hazard exposure modelling in Alpine regions M. Pittore et al. 10.1007/s11069-023-06134-3
- Enhancing post-seismic landslide susceptibility modeling in China through a time-variant approach: a spatio-temporal analysis X. Guo et al. 10.1080/17538947.2023.2265907
- PS‐InSAR post‐processing for assessing the spatio‐temporal differential kinematics of complex landslide systems: A case study of DeBeque Canyon Landslide (Colorado, USA) M. Zocchi et al. 10.1002/esp.6002
- Speech-recognition in landslide predictive modelling: A case for a next generation early warning system Z. Fang et al. 10.1016/j.envsoft.2023.105833
- The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. 10.1016/j.geomorph.2023.108795
- Historical Shifts in Seasonality and Timing of Extreme Precipitation G. Gründemann et al. 10.1029/2023GL105200
- Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction A. Dahal et al. 10.1038/s43247-024-01243-8
- Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway) N. Nocentini et al. 10.1007/s10346-024-02287-9
- Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy M. Moreno et al. 10.1016/j.scitotenv.2023.169166
- Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China H. Zheng & M. Ding 10.1016/j.ecoinf.2024.102862
- Contrasting Population Projections to Induce Divergent Estimates of Landslides Exposure Under Climate Change Q. Lin et al. 10.1029/2023EF003741
- Hydro-meteorological landslide triggering thresholds based on artificial neural networks using observed precipitation and ERA5-Land soil moisture P. Distefano et al. 10.1007/s10346-023-02132-5
17 citations as recorded by crossref.
- Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China Z. Xu et al. 10.3390/su16104044
- From spatio-temporal landslide susceptibility to landslide risk forecast T. Wang et al. 10.1016/j.gsf.2023.101765
- Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds S. Steger et al. 10.1016/j.gsf.2024.101822
- On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values N. Wang et al. 10.1016/j.gsf.2024.101800
- Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system M. Ahmed et al. 10.1016/j.jag.2023.103593
- Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning M. Di Napoli et al. 10.1016/j.catena.2024.108452
- Border-independent multi-functional, multi-hazard exposure modelling in Alpine regions M. Pittore et al. 10.1007/s11069-023-06134-3
- Enhancing post-seismic landslide susceptibility modeling in China through a time-variant approach: a spatio-temporal analysis X. Guo et al. 10.1080/17538947.2023.2265907
- PS‐InSAR post‐processing for assessing the spatio‐temporal differential kinematics of complex landslide systems: A case study of DeBeque Canyon Landslide (Colorado, USA) M. Zocchi et al. 10.1002/esp.6002
- Speech-recognition in landslide predictive modelling: A case for a next generation early warning system Z. Fang et al. 10.1016/j.envsoft.2023.105833
- The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. 10.1016/j.geomorph.2023.108795
- Historical Shifts in Seasonality and Timing of Extreme Precipitation G. Gründemann et al. 10.1029/2023GL105200
- Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction A. Dahal et al. 10.1038/s43247-024-01243-8
- Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway) N. Nocentini et al. 10.1007/s10346-024-02287-9
- Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy M. Moreno et al. 10.1016/j.scitotenv.2023.169166
- Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China H. Zheng & M. Ding 10.1016/j.ecoinf.2024.102862
- Contrasting Population Projections to Induce Divergent Estimates of Landslides Exposure Under Climate Change Q. Lin et al. 10.1029/2023EF003741
Latest update: 20 Nov 2024
Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
We present a novel data-driven modelling approach to determine season-specific critical...
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