Fabio Brill is a postdoctoral researcher in the group “Applied Geoinformation Science” (Prof. Dr. Tobia Lakes) at Humboldt-Universität zu Berlin, currently working on water-related climate change risks within the project CliWaC. In this context, he focusses on mismatches between various methods of risk quantification and stakeholder needs as well as stakeholder risk perception, to identify potential conflicts and synergies in space.
A natural scientist by training, with a Bachelors in BioGeo-Sciences from the FSU Jena, and a Masters in Environmental Earth Sciences from the FU Berlin, Fabio turned towards risk estimation and data science during his doctoral studies as associated member of the research training group NatRiskChange – Natural Hazards and Risks in a Changing World, at the University of Potsdam. Before joining HU and IRI THESys, he has been working at the GFZ Potsdam in the group on flood risk and climate adaptation with PD Dr. Heidi Kreibich, and was involved in the project RIESGOS on the development of multi-risk information system components for the Andes region.
- Risk estimation of natural hazards and climate change
- Applied geospatial data science
- Comparisons of risk models and risk perception
Sairam, N.; Brill, F.; Sieg, T.; Kellermann, P.; Schröter, K.; Nguyen, D.V.; Merz, B.; Lüdtke, S.; Farrag, M.; Vorogushyn, S.; Kreibich, H. Process-based flood risk assessment for Germany. Earth’s Future, 9, e2021EF002259. https://doi.org/10.1029/2021EF002259
Brill, F.; Schlaffer, S.; Martinis, S.; Schröter, K.; Kreibich, H. Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston. Remote Sensing. 2021, 13, 2042. https://doi.org/10.3390/rs13112042
Brill, F.; Passuni Pineda, S.; Espichán Cuya, B.; Kreibich, H. A data-mining approach towards damage modelling for El Niño events in Peru. Geomatics, Natural Hazards and Risk, 2020, 11:1, 1966-1990, DOI: 10.1080/19475705.2020.1818636
Sairam, N.; Schröter, K.; Carisi, F.; Wagenaar, D.; Domeneghetti, A.; Molinari, D.; Brill, F.; Priest, S.; Viavattene, C.; Merz, B.; Kreibich, H. Bayesian Data-Driven approach enhances synthetic flood loss models. Environmental Modelling & Software. 2020, 132, 104798, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2020.104798.
Gomez-Zapata, J.C.; Parrado, C.; Frimberger, T.; Barragán-Ochoa, F.; Brill, F.; Büche, K.; Krautblatter, M.; Langbein, M.; Pittore, M.; Rosero-Velásquez, H.; Schoepfer, E.; Spahn, H.; Zapata-Tapia, C. Community Perception and Communication of Volcanic Risk from the Cotopaxi Volcano in Latacunga, Ecuador. Sustainability 2021, 13, 1714. https://doi.org/10.3390/su13041714