M. Moreno Zapata (Mateo)

My research focuses on the field of natural hazards, particularly on landslide modeling. I'm currently working on the project "PROSLIDE - PRediction of ShallOw LandSLIDEs" in the province of South Tyrol, Italy.

Faculty of Geo-Information Science and Earth Observation (ITC), Department of Applied Earth Sciences (AES)

de Vugt, L., Zieher, T., Schneider‐Muntau, B. , Moreno, M., Steger, S. , & Rutzinger, M. (2024). Spatial transferability of the physically based model TRIGRS using parameter ensembles. Earth surface processes and landforms, 49(4), 1330-1347. https://doi.org/10.1002/esp.5770

Moreno, M. , Lombardo, L., Crespi, A., Zellner, P. J., Mair, V., Pittore, M. , van Westen, C. J., & Steger, S. (2024). Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy. Science of the total environment, 912(169166), 1-17. Article 169166. https://doi.org/10.1016/j.scitotenv.2023.169166

Lima, P. , Moreno, M., Steger, S., Camarinha, PI., Teixeira, C. LC., Mandarino, F., & Glade, T. (2023). Developing a spatiotemporal model to integrate landslide susceptibility and critical rainfall conditions. A practical model applied to Rio de Janeiro municipality. Abstract from 6th World Landslide Forum, WLF 2023, Florence, Italy.

Moreno, M., Opitz, T., Steger, S. , Lombardo, L., Crespi, A., Pittore, M., & van, W. C. (2023). Exploring functional regression for dynamic modeling of shallow landslides in South Tyrol, Italy. Abstract from 6th World Landslide Forum, WLF 2023, Florence, Italy.

Campalani, P., Renner, K., Crespi, A., Steger, S. , Moreno, M., & Pittore, M. (2023). Towards multi-hazard, border-independent exposure analysis for operational climate and disaster risk preparedness applications. Abstract from SISC 11th Annual Conference 2023, Milan, Italy.

Moreno, M. , Lombardo, L., Crespi, A., Zellner, P., Mair, V., Pittore, M. , van Westen, C., & Steger, S. (2023). Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy. Earth ArXiv. https://doi.org/10.31223/X59M3J

Moreno, M., Steger, S. , Tanyas, H. , & Lombardo, L. (2023). Modeling the area of co-seismic landslides via data-driven models: The Kaikōura example. Engineering geology, 320, Article 107121. https://doi.org/10.1016/j.enggeo.2023.107121

Steger, S. , Moreno, M., Crespi, A., Zellner, P. J., Gariano, S. L., Brunetti, M. T., Melillo, M., Peruccacci, S., Marra, F., Kohrs, R., Goetz, J., Mair, V., & Pittore, M. (2023). Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models. Natural hazards and earth system sciences, 23(4), 1483-1506. https://doi.org/10.5194/nhess-23-1483-2023

Steger, S. , Moreno, M., Crespi, A., Gariano, S. L., Brunetti, M. T., Melillo, M., Peruccacci, S., Marra, F., Borga, M., Vugt, L. D., Zieher, T. , Rutzinger, M., Mair, V., Campalani, P., & Pittore, M. (2023). A data-driven approach to derive spatially explicit dynamic "thresholds" for shallow landslide occurrence in South Tyrol (Italy). Abstract from EGU General Assembly 2023, Vienna, Austria. https://doi.org/10.5194/egusphere-egu23-1353

Vugt, L. D., Zieher, T., Schneider-Muntau, B. , Moreno, M., Steger, S. , & Rutzinger, M. (2023). Improving the performance of a dynamic slope stability model (TRIGRS) with integrated spatio-temporal precipitation data. Abstract from EGU General Assembly 2023, Vienna, Austria. https://doi.org/10.5194/egusphere-egu23-7845

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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 shall Ow land SLIDEs

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.

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+31534897516

+390471055387

m.morenozapata@utwente.nl

mateo.morenozapata@eurac.edu