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Kristin Vogel

Researcher

Profile Picture Kristin
Kristin Vogel joined the BAM e-Science group as permanent researcher in October 2021. Her research interest is in the application of statistics and machine learning tools for the prediction of material properties and the quantification of uncertainties. After finishing her diploma studies in mathematics, she worked for several years at the University of Potsdam on the development of data-driven methods for the risk assessments of natural hazards. In this framework she did her PhD in General Geophysics and developed a high expertise in probabilistic graphical models.
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    Publications:

  • Vogel, K., Sieg, T., Veh, G., Fiedler, B., Moran, T., Peter, M., Rottler, E., Bronstert, A. (2024). Natural hazards in a changing world: Methods for analyzing trends and non-linear changes. Earth's Future, 12, e2023EF003553. https://doi.org/10.1029/2023EF003553
  • Lisec, J., Recknagel, S., Prinz, C., Vogel, K., Koch, M., Becker, R. (2024). eCerto - versatile software for interlaboratory data evaluation and documentation during reference material production. Analytical and Bioanalytical Chemistry, 416, 1139–1147. https://doi.org/10.1007/s00216-023-05099-3
  • Sieg, T.; Kienzler, S.; Rözer, V.; Vogel, K.; Rust, H.; Bronstert, A.; Kreibich, H.; Merz, B.; Thieken, A.H. (2023). Toward an adequate level of detail in flood risk assessments. Journal of Flood Risk Management, e12889. http://doi.org/10.1111/jfr3.12889
  • Veh, G.; Lützow, N.; Tamm, J.; Luna, L.V.; Hugonnet, R.; Vogel, K.; Geertsema, M.; Clague, J.J.; Korup, O. (2023). Less extreme and earlier outbursts of ice-dammed lakes since 1900. Nature, 614(7949), 701-707. http://doi.org/10.1038/s41586-022-05642-9
  • Esfahani, R.D.D.; Vogel, K.; Cotton, F.; Ohrnberger, M.; Scherbaum, F.; Kriegerowski, M. (2021). Exploring the Dimensionality of Ground-Motion Data by Applying Autoencoder Techniques. Bulletin of the Seismological Society of America.
  • Schoppa, L.; Kreibich, H.; Sieg, T.; Vogel, K.; Zöller, G. (2021): Developing multivariable probabilistic flood loss models for companies. In FLOODrisk 2020 - 4rt European Conference on Flood Risk Management
  • Schoppa, L.; Sieg, T.; Vogel, K.; Zöller, G.; Kreibich, H. (2020). Probabilistic flood loss models for companies. Water Resources Research, 56(9), e2020WR027649.
  • Bronstert, A.; Crisologo, I.; Heistermann, M.; Ozturk, U.; Vogel, K.; Wendi, D. (2020): Flash-Floods: More often, More severe, More damaging? An Analysis of Hydro-Geo-Environmental Conditions and Anthropogenic Impacts. In Climate Change, Hazards and Adaptation Options, 225-244.
  • Sieg, T.; Schinko, T.; Vogel, K.; Mechler, R.; Merz, B.; Kreibich, H. (2019): Integrated Assessment of short-term direct and indirect economic Flood Impacts including Uncertainty Quantification. PLOS One, 14(4), e0212932.
  • Sieg, T.; Vogel, K.; Merz, B.; Kreibich, H. (2019): Seamless Estimation of Hydro-meteorological Risk Across Spatial Scales. Earthťs Future, 7(4), 574-581.
  • Ozturk, U.; Wendi, D.; Crisologo, I.; Riemer, A.; Agarwal, A.; Vogel, K.; López-Tarazón, J.A.; Korup, O. (2018): Rare flash floods and debris flows in southern Germany. Science of the Total Environment, 626, 941-952.
  • Petrow, Th.; Bronstert, A.; Thieken, A.H.; Vogel, K. (Eds.) (2018): Book of Abstracts of the International Conference on Natural Hazards and Risks in a Changing World, Universität Potsdam (Publisher), 118 pages, URN: urn:nbn:de:kobv:517-opus4-416613
  • Vogel, K.; Weise, L.; Schröter, K.; Thieken, A.H. (2018): Identifying Driving Factors in Flood Damaging Processes Using Graphical Models. Water Resources Research, 54, 8864-8889.
  • Laudan, J.; Rözer, V.; Sieg, T.; Vogel, K.; Thieken, A.H. (2017): Damage assessment in Braunsbach 2016: Data collection and analysis for an improved understanding of damaging processes during flash floods. Natural Hazards and Earth System Sciences, 17(12),2163-2179.
  • Sieg, T.; Vogel, K.; Kreibich, H.; Merz, B. (2017): Tree-based flood damage modeling of companies: Damage processes and model performance Water Resources Research, 53, 1-19.
  • Vogel, K. (2017): Learning Markov Blankets - Extending on a MAP Criterion for Bayesian Network Learning. In Safety, Reliability, Risk, Resilience and Sustainability of Structures and Infrastructure, 12th International Conference on Structural Safety & Reliability, Vienna, Austria, 6-10 August 2017, 2868-2877.
  • Vogel, K.; Laudan, J.; Sieg, T.; Rözer, V.; Winter, B.; Thieken, A.H. (2017): Data collection for a damage assessment after the flash flood in Braunsbach (Germany) in May 2016. GFZ Data Services. http://doi.org/10.5880/fidgeo.2017.015
  • Vogel, K.; Ozturk, U.; Riemer, A.; Laudan, J.; Sieg, T.; Wendi, D.; Agarwal, A.; Rözer, V.; Korup, O.; Thieken, A.H. (2017): Die Sturzflut von Braunsbach am 29. Mai 2016 - Entstehung, Ablauf und Schäden eines “Jahrhundertereignisses”. Teil 2: Geomorphologische Prozesse und Schadensanalyse. Hydrologie & Wasserbewirtschaftung, 61(3), 163-175.
  • Schroeter, K.; Luedtke, S.; Vogel, K.; Kreibich, H.; Merz, B. (2016): Tracing the value of data for flood loss modelling. In FLOODrisk 2016 - 3rd European Conference on Flood Risk Management.
  • Schroeter, K.; Kreibich, H.; Vogel, K.; Riggelsen, C.; Scherbaum, F.; Merz, B. (2014): How useful are complex flood damage models?. Water Resources Research, 50(4), 3378-3395. 2014 WRR Editors’ Choice Award
  • Vogel, K. (2014): Applications of Bayesian networks in natural hazard assessments. Diss. Universitätsbibliothek, 2014. http://opus.kobv.de/ubp/volltexte/2014/6977/
  • Vogel, K.; Riggelsen, C.; Korup, O.; Scherbaum, F. (2014): Bayesian network learning for natural hazard analyses. Natural Hazards and Earth System Science, 14(9), 2605-2626.
  • Vogel, K.; Riggelsen, C.; Scherbaum, F.; Schroeter, K.; Kreibich, H.; Merz, B. (2013): Challenges for Bayesian Network Learning in a Flood Damage Assessment Application. In 11th International Conference on Structural Safety & Reliability.
  • Vogel, K.; Riggelsen, C.; Merz, B.; Kreibich, H.; Scherbaum, F. (2012): Flood damage and influencing factors: A Bayesian network perspective. In 6th European Workshop on Probabilistic Graphical Models (PGM 2012), University of Granada, Spain, September 2012.
  • Vogel, K.; Riggelsen, C.; Kuehn, N.; Scherbaum, F. (2012): Graphical models as surrogates for complex ground motion models. In Artificial Intelligence and Soft Computing (pp. 188-195). Springer Berlin Heidelberg.