PROSLIDE

Integration of static and dynamic landslide controls at multiple-scales using data-driven and physically-based methods – exploring new opportunities for the prediction of shallow landslides

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PROSLIDE


Landslides are widespread phenomena in mountain areas of the world and play a key role in landscape evolution. These potentially hazardous geomorphological processes pose a serious threat to human lives, settlements and infrastructure. The efficiency of measures that strive for a pro-active reduction of landslide impacts (e.g. land management, early warning), heavily depends on the knowledge of where and when future slope instability may occur. However, the prediction of landslides still constitutes a scientific challenge, mainly due to a complex interplay of their static and dynamic causes and triggers (e.g. topography, lithology, snow melt, heavy rainfalls) and associated spatio-temporal data required for modelling.

PROSLIDE focuses on (primary non-human caused) shallow slope instability in the province of South Tyrol (Italy). The main objective is to improve the predictability of shallow landslides by integrating innovative environmental data and novel modelling designs at multiple spatial and temporal scales. Main innovations of the project include (i) the exploitation of remote sensing techniques and data for the spatio-temporal characterization of landslide controls (e.g. meteorological RADAR; satellite derived snow and soil moisture; unmanned aerial vehicle based laser scanning), (ii) the development of novel modelling strategies that allow to integrate heterogeneous model inputs, data-driven approaches and physically-based models, (iii) the explicit elaboration of optimal investigation scales (e.g. dependency of the models on spatial and temporal data aggregation strategies).

The iterative methodical workflow comprises the gathering, processing and analysis of landslide-related ground truth information (incl. field campaigns) and newly generated remote sensing data (WP1 and WP2), the province-wide data-driven modelling of critical threshold conditions and spatio-temporal landslide likelihoods (WP3), dynamic physically-based modelling of slope stability at catchment scale (WP4) as well as the method integration and in-depth result evaluation (WP5). The results are expected to provide new insights into underlying geomorphic processes, landslide predictability in space and time and associated opportunities for the purpose of landslide forecasting in the context of the provincial Civil Protection Warning Centre.

The interdisciplinary PROSLIDE group consists of researchers and stakeholders with complementary expertise in Geomorphology, Geology, Soil science, Hydrology, Meteorology and Remote sensing. The institute for Earth Observation of Eurac and the Institute of Geography of the University of Innsbruck are supported by local stakeholders (the office for Geology and Building Material Testing and the Civil Protection Agency) and renowned research institutions (University of Padova; Italian National Research Council (CNR); Austrian Academy of Sciences).

Contact person: Stefan Steger

stefan.steger@eurac.edu

Website: https://www.mountainresearch.at/proslide/

Project funded by

In the Proslide project, researchers exploit a variety of data sources, such as information derived from laserscanning (see this photograph from Passeiertal Valley), to predict the occurrence of shallow landslides.Credit: Eurac Research | Stefan Steger

Credit: Eurac Research
Publications
Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy
Moreno M, Steger S, Lombardo L, Opitz T, Crespi A, Marra F, de Vugt L, Zieher T, Rutzinger M, Mair V, Pittore M, van Westen C (2023)
Presentation/Speech

Conference: EGU23 General Assembly | Vienna | 23.4.2023 - 28.4.2023

More information: https://doi.org/10.5194/egusphere-egu23-9538

Improving the performance of a dynamic slope stability model (TRIGRS) with integrated spatio-temporal precipitation data
de Vugt L, Zieher T, Schneider-Muntau B, Moreno M, Steger S, Rutzinger M (2023)
Presentation/Speech

Conference: EGU23 General Assembly | Vienna | 23.4.2023 - 28.4.2023

More information: https://doi.org/10.5194/egusphere-egu23-7845

Das Projekt „Proslide“ - Methoden zur raum-zeitlichen Vorhersage flachgründiger Rutschungen
Steger S, de Vugt L (2023)
Other contribution
Final PROSLIDE webinar 2023 - Exploring new opportunities for the PRediction Of shallow landSLIDEs
Steger S, Moreno M, de Vugt L (2023)
Presentation/Speech

Conference: Final Proslide webinar 2023 | Online event | 29.6.2023 - 29.6.2023

More information: https://www.landaware.org/2023/06/05/proslide-project-webina ...

A data-driven approach to derive spatially explicit dynamic "thresholds" for shallow landslide occurrence in South Tyrol (Italy)
Steger S, Moreno M, Crespi A, Gariano SL, Brunetti MT, Melillo M, Peruccacci S, Marra F, Borga M, de Vugt L, Zieher T, Rutzinger M, Mair V, Campalani P, Pittore M (2023)
Conference proceedings article

Conference: EGU23 General Assembly | Vienna | 23.4.2023 - 28.4.2023

More information: https://doi.org/10.5194/egusphere-egu23-1353

https://doi.org/10.5194/egusphere-egu23-1353

Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
Steger S, Moreno M, Crespi A, Zellner PJ, Gariano SL, Brunetti MT, Melillo M, Peruccacci S, Marra F, Kohrs R, Goetz J, Mair V, Pittore M (2023)
Journal article
Natural Hazards and Earth System Sciences

More information: https://nhess.copernicus.org/articles/23/1483/2023/

https://doi.org/10.5194/nhess-23-1483-2023

A data-driven approach to establish prediction surfaces for rainfall-induced shallow landslides in South Tyrol, Italy
Steger S, Kohrs R, Crespi A, Moreno M, Zellner PJ, Goetz J, Mair V, Gariano SL, Brunetti MT, Melillo M, Peruccacci S, Pittore M (2022)
Presentation/Speech

Conference: EGU General Assembly 2022 | Vienna | 22.5.2022 - 27.5.2022

More information: https://meetingorganizer.copernicus.org/EGU22/session/43376

Applying a hierarchical Generalized Additive Model to integrate predisposing, preparatory and triggering factors for landslide prediction
Steger S, Moreno M, Crespi A, Zellner PJ, Kohrs R, Goetz J, Gariano SL, Brunetti MT, Melillo M, Peruccacci S, de Vugt L, Zieher T, Rutzinger M, Mair V, Pittore M (2022)
Presentation/Speech

Conference: International Conference of the International Association of Geomorphologists (IAG) | Coimbra | 12.9.2022 - 16.9.2022

More information: https://meetingorganizer.copernicus.org/ICG2022/meetingprogr ...

Space-time modeling of rainfall-induced shallow landslides in South Tyrol, Italy
Moreno M, Steger S, Lombardo L, Crespi A, Zellner PJ, Pittore M, Mair V, Westen C (2022)
Presentation/Speech

Conference: EGU General Assembly 2022 | Vienna | 22.5.2022 - 27.5.2022

More information: https://meetingorganizer.copernicus.org/EGU22/EGU22-9175.htm ...

Comparing different strategies to incorporate the effectively surveyed area into landslide susceptibility modeling
Moreno M, Steger S, Lombardo L, Vugt L, Zieher T, Rutzinger M, Pittore M, Mair V, Westen C (2022)
Presentation/Speech

Conference: International Conference of the International Association of Geomorphologists (IAG) | Coimbra | 12.9.2022 - 16.9.2022

More information: https://meetingorganizer.copernicus.org/ICG2022/ICG2022-563. ...

Analyse von Rutschungsereignissen in der Provinz Bozen - Südtirol = Analisi dei fenomeni franosi della Provincia di Bolzano – Alto Adige
Steger S (2021)
Presentation/Speech

Conference: CivilProtect2021 | Bolzano | 17.9.2021 - 19.9.2021

More information: https://www.fierabolzano.it/de/civil-protect/event/online-in ...

https://hdl.handle.net/10863/18816

Our partners
Project Team
1 - 6
Stefan Steger

Stefan Steger

Project Manager
Peter James Zellner

Peter James Zellner

Team Member
Mateo Moreno Zapata

Mateo Moreno Zapata

Team Member