Publications

  1. 2023 (submitted)

    1. F. Ejaz, T. Wöhling, A. Guthke, and W. Nowak, “Comprehensive uncertainty analysis for surface water and groundwater forecasts under climate change based on a lumped geo-hydrological model,” Journal of Hydrology.
    2. H.-F. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions,” Water Resources Research.
    3. I. Banerjee, A. Guthke, C. J. C. Van de Ven, K. G. Mumford, and W. Nowak, “Comparison of Four Competing Invasion Percolation Models for Gas Flow in Porous Media,” Water Resources Research.

  2. 2023

    1. I. Banerjee, P. Walter, A. Guthke, K. G. Mumford, and W. Nowak, “The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence,” Computational Geosciences, vol. 27, pp. 45–62, 2023, doi: 10.1007/s10596-022-10179-x.

  3. 2022

    1. H. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Diagnosis of model-structural errors with a sliding time-window Bayesian analysis,” Water Resources Research, vol. 58, p. e2021WR030590, 2022, doi: doi:10.1029/2021WR030590.
    2. A. Schäfer Rodrigues Silva et al., “Diagnosing Similarities in Probabilistic Multi-Model Ensembles - an Application to Soil-Plant-Growth-Modeling,” Modeling Earth Systems and Environment, vol. 8, pp. 5143–5175, 2022, doi: 10.1007/s40808-022-01427-1.

  4. 2021

    1. S. Reuschen, A. Guthke, and W. Nowak, “The Four Ways to Consider Measurement Noise in Bayesian Model Selection - And Which One to Choose,” Water Resources Research, vol. 57, p. e2021WR030391, 2021, doi: 10.1029/2021WR030391.
    2. I. Banerjee, A. Guthke, C. J. C. Van de Ven, K. G. Mumford, and W. Nowak, “Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data,” Water Resources Research, vol. 57, no. 7, Art. no. 7, 2021, doi: 10.1029/2021WR029986.

  5. 2020

    1. A. Guthke, “The Unified Risk Equation - An Attempt to Unify Risk Assessment across Disciplines,” Munich, Germany: Engineering Risk Analysis Group, Technical University of Munich, Apr. 2020.
    2. A. Guthke et al., “A unified framework for quantitative interdisciplinary flood risk assessment,” online: AGU Fall Meeting 2020, Dec. 2020.
    3. I. Banerjee, A. Guthke, C. J. C. V. D. Ven, K. Mumford, and W. Nowak, “Overcoming the Model-to-Experimental Data Fit Problem in Porous Media: a New Quantitative Method to Evaluate and Compare Models,” online: AGU Fall Meeting 2020, Dec. 2020.
    4. C. Jackisch, A. Schibalski, B. Schröder, W. Nowak, and A. Guthke, “Providing relevant uncertainty information to decision makers: Subjective post-processing of rigorous Bayesian uncertainty assessment of model projections,” online: AGU Fall Meeting 2020, Dec. 2020.
    5. H.-F. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Diagnosing Model-structural Errors with a Sliding Time-window Bayesian Analysis,” online: AGU Fall Meeting 2020, Dec. 2020.
    6. M. Höge, A. Guthke, and W. Nowak, “Bayesian Model Weighting: The Many Faces of Model Averaging,” Water, vol. 12, no. 2, Art. no. 2, 2020, doi: 10.3390/w12020309.
    7. A. Schäfer Rodrigues Silva, A. Guthke, M. Höge, O. A. Cirpka, and W. Nowak, “Strategies for simplifying reactive transport models - a Bayesian model comparison,” Water Resources Research, vol. 56, p. e2020WR028100, 2020, doi: 10.1029/2020WR028100.

  6. 2019

    1. A. Guthke, “Justifiability is key - Bayesian analysis of system and model complexity,” Esch-sur-Alzette, Luxembourg: 10th EGU Leonardo Conference on Earth’s hydrological cycle: Global change, landscape ageing and the pulse of catchments, Oct. 2019.
    2. M. Höge, A. Guthke, and W. Nowak, “The Hydrologist’s Guide to Bayesian Model Selection, Averaging and Combination,” Journal of Hydrology, vol. 572, pp. 96–107, May 2019, doi: https://doi.org/10.1016/j.jhydrol.2019.01.072.
    3. D. F. Motavita, R. Chow, A. Guthke, and W. Nowak, “The Comprehensive Differential Split-Sample Test: A stress-test for hydrological model robustness under climate variability,” Journal of Hydrology, vol. 573, pp. 501–515, 2019, doi: 10.1016/j.jhydrol.2019.03.054.

  7. 2018

    1. A. Guthke, “A Bayesian take on model choice uncertainty: Statistical tools for model evaluation, selection and combination,” Karlsruhe, Germany: KIT, Institut für Wasser und Gewässerentwicklung, Lehrstuhl für Hydrologie, Jul. 2018.
    2. A. Schäfer-Rodrigues-Silva, T. Seitz, Anneli. Guthke, and W. Nowak, “Working with multi-model ensembles - what makes models differ and how can we visualize ensembles?,” Tübingen, Germany: Seminar of the Research Training Group “Integrated Hydrosystem Modelling,” Jun. 2018.
    3. S. Oladyshkin, A. Guthke, F. Mohamadi, R. Kopmann, and W. Nowak, “Model selection under computational time constraints: application to river engineering,” Saint-Malo, France: XXII. International Conference on Computational Methods in Water Resources (CMWR), Jun. 2018.
    4. Anneli. Guthke, “Model selection on solid ground: rigorous comparison of nine ways to evaluate Bayesian evidence,” Adelaide, Australia: STAHY 2018 Best Paper Award Session, Sep. 2018.
    5. A. Guthke, M. Höge, and W. Nowak, “How model selection and averaging strategies help us improve hydrological models,” in General Assembly 2018, Geophysical Research Abstracts 20: EGU2018-12797, 2018, in General Assembly 2018, Geophysical Research Abstracts 20: EGU2018-12797, 2018. Vienna, Austria: European Geosciences Union (EGU), Apr. 2018.
    6. A. Schäfer-Rodrigues-Silva, Anneli. Guthke, and W. Nowak, “Quantifying similarity in multi-model ensembles,” Tübingen, Germany: International Conference on Integrated Hydrosystem Modelling, Apr. 2018.
    7. A. Guthke and W. Nowak, “Entropy-based experimental design for optimal model discrimination in the Geosciences,” Santander, Spain: Second Workshop on Information Theory and the Earth Sciences, May 2018.
    8. A. Schäfer-Rodrigues-Silva, A. Guthke, and W. Nowak, “The importance of model similarity in multi-model problems,” Stuttgart, Germany: Meeting of the international doctoral program “Environment Water,” Feb. 2018.
    9. A. Schäfer-Rodrigues-Silva, T. Seitz, Anneli. Guthke, and W. Nowak, “Quantifying and visualizing similarity in multi-model ensembles,” Cargese, France: Summer school on “Flow and Transport in Porous and Fractured media,” Jun. 2018.
    10. A. Guthke, S. Oladyshkin, F. Mohammadi, R. Kopmann, and W. Nowak, “Bayesian model selection under computational time constraints: application to river modeling,” in Fall Meeting 2018, in Fall Meeting 2018. Washington, D.C., USA: American Geophysical Union (AGU), Dec. 2018.
    11. P. Darscheid, A. Guthke, and U. Ehret, “A Maximum-Entropy Method to Estimate Discrete Distributions  from Samples Ensuring Nonzero Probabilities,” Entropy, vol. 20, no. 8, Art. no. 8, 2018, doi: 10.3390/e20080601.
    12. F. Mohammadi, R. Kopmann, A. Guthke, S. Oladyshkin, and W. Nowak, “Bayesian selection of hydro-morphodynamic models under computational  time constraints.,” Advances in Water Resources, vol. 117, pp. 53–64, 2018, doi: 10.1016/j.advwatres.2018.05.007.

  8. 2017

    1. A. Guthke, M. Höge, and W. Nowak, “Bayesian model evidence as a model evaluation metric,” in General Assembly 2017, Geophysical Research Abstracts 19: EGU2017-13390-1, 2017, in General Assembly 2017, Geophysical Research Abstracts 19: EGU2017-13390-1, 2017. Vienna, Austria: European Geosciences Union (EGU), Apr. 2017.
    2. A. Guthke, “Defensible Model Complexity: A Call for Data-Based and Goal-Oriented Model Choice,” Groundwater, vol. 55, no. 5, Art. no. 5, 2017, doi: 10.1111/gwat.12554.

  9. 2016

    1. A. Guthke, “Bayesian assessment of conceptual uncertainty in hydrosystem modeling,” Doctoral dissertation, Universität Tübingen, 2016. [Online]. Available: https://bibliographie.uni-tuebingen.de/xmlui/handle/10900/71412
    2. W. Nowak and A. Guthke, “Entropy-based experimental design for optimal model discrimination in the geosciences,” Entropy, vol. 18, no. 11, Art. no. 11, 2016, doi: 10.3390/e18110409.
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