Publications

  1. 2022 (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. 2022 (submitted)

    1. H.-F. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions,” Water Resources Research.
  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. 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, 2022, doi: 10.1007/s10596-022-10179-x.
    3. 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, Apr. 2020.
    2. H.-F. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Diagnosing Model-structural Errors with a Sliding Time-window Bayesian Analysis,” online, Dec. 2020.
    3. 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, Dec. 2020.
    4. 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, Dec. 2020.
    5. A. Guthke et al., “A unified framework for quantitative interdisciplinary flood risk assessment,” online, 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, 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. Anneli. Guthke, “Model selection on solid ground: rigorous comparison of nine ways to evaluate Bayesian evidence,” Adelaide, Australia, Sep. 2018.
    2. A. Guthke, “A Bayesian take on model choice uncertainty: Statistical tools for model evaluation, selection and combination,” Karlsruhe, Germany, Jul. 2018.
    3. A. Guthke and W. Nowak, “Entropy-based experimental design for optimal model discrimination in the Geosciences,” Santander, Spain, May 2018.
    4. A. Guthke, M. Höge, and W. Nowak, “How model selection and averaging strategies help us improve hydrological models,” Vienna, Austria, Apr. 2018.
    5. A. Schäfer-Rodrigues-Silva, A. Guthke, and W. Nowak, “The importance of model similarity in multi-model problems,” Stuttgart, Germany, Feb. 2018.
    6. A. Schäfer-Rodrigues-Silva, Anneli. Guthke, and W. Nowak, “Quantifying similarity in multi-model ensembles,” Tübingen, Germany, Apr. 2018.
    7. A. Guthke, S. Oladyshkin, F. Mohammadi, R. Kopmann, and W. Nowak, “Bayesian model selection under computational time constraints: application to river modeling,” Washington, D.C., USA, Dec. 2018.
    8. S. Oladyshkin, A. Guthke, F. Mohamadi, R. Kopmann, and W. Nowak, “Model selection under computational time constraints: application to river engineering,” Saint-Malo, France, Jun. 2018.
    9. A. Schäfer-Rodrigues-Silva, T. Seitz, Anneli. Guthke, and W. Nowak, “Quantifying and visualizing similarity in multi-model ensembles,” Cargese, France, Jun. 2018.
    10. 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, Jun. 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,” Vienna, Austria, 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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