Overview Research Projects Contact
My research focuses on natural hazard modeling, particularly landslides and wildfires.
Expertise Earth and Planetary Sciences Landslide Model Datum Italy Prediction Modeling Time Area Organisations Publications Comprehensive multi-hazard risk assessment in data-scarce regions: A study focused on Burundi (2025) [Working paper › Preprint]. European Geosciences Union (E-pub ahead of print/First online). Delves, J., Renner, K., Campalani, P., Piñón, J., Schneiderbauer, S., Steger, S., Moreno, M. , Oterino, M. B. B., Perez, E. & Pittore, M. https://doi.org/10.5194/egusphere-2024-3445 Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy (2024) [Working paper › Preprint]. Earth ArXiv. Moreno, M. , Lombardo, L., Steger, S., de Vugt, L., Zieher, T., Crespi, A., Marra, F., van Westen, C. & Opitz, T. https://doi.org/10.31223/X5VB0M A benchmark dataset and workflow for landslide susceptibility zonation (2024) Earth-science reviews, 258 . Article 104927. Alvioli, M., Loche, M., Jacobs, L., Grohmann, C. H., Abraham, M. T., Gupta, K., Satyam, N., Scaringi, G., Bornaetxea, T., Rossi, M., Marchesini, I., Lombardo, L., Moreno, M. , Steger, S., Camera, C. A. S., Bajni, G., Samodra, G., Wahyudi, E. E., Susyanto, N., … Rivera-Rivera, J.https://doi.org/10.1016/j.earscirev.2024.104927 Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds (2024) Geoscience Frontiers, 15 (5). Article 101822. Steger, S., Moreno, M. , Crespi, A., Luigi Gariano, S., Brunetti, M. T., Melillo, M., Peruccacci, S., Marra, F., de Vugt, L., Zieher, T., Rutzinger, M., Mair, V. & Pittore, M.https://doi.org/10.1016/j.gsf.2024.101822 Spatial transferability of the physically based model TRIGRS using parameter ensembles (2024) Earth surface processes and landforms, 49 (4), 1330-1347. de Vugt, L., Zieher, T., Schneider‐Muntau, B., Moreno, M. , Steger, S. & Rutzinger, M.https://doi.org/10.1002/esp.5770 Application of beta regression for the prediction of landslide areal density in South Tyrol, Italy (2024) [Contribution to conference › Abstract] EGU General Assembly 2024 . Moreno, M. , Opitz, T., Steger, S., Westen, C. v. & Lombardo, L. https://doi.org/10.5194/egusphere-egu24-17785 Incorporating climate change projections into operational debris flow hazard mapping: Initial insights from the Toverino River Basin in South Tyrol (Eastern Italian Alps). (2024) [Contribution to conference › Abstract] EGU General Assembly 2024 . Bozzoli, L., Crespi, A., Steger, S. & Moreno, M. https://doi.org/10.5194/egusphere-egu24-19520 Development of a data-driven space-time model to predict precipitation-induced geomorphic impact events at the Alpine Scale (2024) [Contribution to conference › Abstract] EGU General Assembly 2024 . Spiekermann, R., Lehner, S., Steger, S., Moreno, M. , Enigl, K., Imgrüth, D., Schlögl, M. & Pistotnik, G. https://doi.org/10.5194/egusphere-egu24-10552 Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy (2024) Science of the total environment, 912 (169166), 1-17. Article 169166. Moreno, M. , Lombardo, L., Crespi, A., Zellner, P. J., Mair, V., Pittore, M., van Westen, C. J. & Steger, S.https://doi.org/10.1016/j.scitotenv.2023.169166 Towards multi-hazard, border-independent exposure analysis for operational climate and disaster risk preparedness applications (2023) [Contribution to conference › Abstract] SISC 11th Annual Conference 2023 . Campalani, P., Renner, K., Crespi, A., Steger, S., Moreno, M. & Pittore, M. Research profiles PROSLIDE: Integration of static and dynamic landslide controls at multiple-scales using data-driven and physically-based methods – exploring new opportunities for the PR ediction of shallO w land SLIDE s. The overarching aim of PROSLIDE is to exploit the potential of innovative input data, available ground truth data and novel modelling designs (i.e. data-driven and physically-based) at different scales to improve the predictability of where and when landslides will occur.
EO4MULTIHA: E arth O bservation for Multi -Ha zard Risk Assessments The EO4MULTIHA is a European Space Agency funded project aiming to explore the EO technology potential to advance the scientific understanding of high impact multi-hazard events to better identify, characterise and assess their associated risk, vulnerability and impacts on society and ecosystems.