This image shows Anneli Guthke

Anneli Guthke

Dr. Dipl.-Ing.

Statistical Model-Data Integration

Contact

+49 711 685 60157
Website
Business card (VCF)

Universitätsstraße 32
70569 Stuttgart
Room: 227c

Office Hours

Consultation by appointment

Subject

  • Fundamental methodological advances in
    • model evaluation, uncertainty and error quantification
    • stochastic simulation & information-theoretic concepts
    • hybrid machine-learning/simulation approaches
  • Philosophical contributions to
    • perspectives of modeling
    • new standards towards sustainable use of resources and knowledge
  • Domain-specific advances through geoscientific modeling, e.g. in
    • surface hydrology -> flood forecasting
    • soil-plant systems -> yield stability, diversity
    • groundwater flow and solute transport in the subsurface -> protection and remediation of environmental systems

  1. 2026

    1. S. Scheurer, P. Reiser, T. Brünette, W. Nowak, A. Guthke, and P.-C. Bürkner, “Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models,” Transactions on Machine Learning Research, vol. accepted, 2026.

  2. 2025

    1. M. Alvarez Chaves, “Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models.” 2025. doi: 10.18419/darus-4920.
    2. M. Álvarez Chaves, E. Acuña Espinoza, U. Ehret, and A. Guthke, “When Physics Gets in the Way: An Entropy-based Evaluation of Conceptual Constraints in Hybrid Hydrological Models,” Hydrology and Earth System Sciences, vol. accepted, 2025.
    3. P. Reiser, P.-C. Bürkner, and A. Guthke, “Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy,” Statistics and Computing, vol. under review, 2025.
    4. P. Reiser, J. E. Aguilar, A. Guthke, and P.-C. Bürkner, “Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference,” Statistics and Computing, vol. 35, Art. no. 3, 2025, doi: 10.1007/s11222-025-10597-8.
    5. T. Wöhling, A. O. Crespo Delgadillo, M. Kraft, and A. Guthke, “Comparing Physics-based, Conceptual and Machine-Learning Models to Predict Groundwater Levels by Bayesian Model Averaging,” Groundwater, vol. 63, pp. 484–505, 2025, doi: 10.1111/gwat.13487.
    6. I. Banerjee, A. Guthke, C. J. Van De Ven, K. G. Mumford, and W. Nowak, “A Framework for Objectively Comparing Competing Invasion Percolation Models based on Highly-Resolved Image Data,” PLOS One, vol. (under review), 2025.
    7. A. Guthke et al., “Building Bridges Between Disciplines: A Generalized Mathematical Framework for Talking Quantitative Risk Assessment,” Environmental Research: Infrastructure and Sustainability, vol. under review, 2025.
    8. S. Scheurer, P. Reiser, T. Brünette, W. Nowak, A. Guthke, and P.-C. Bürkner, “Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models,” Transactions on Machine Learning Research, vol. under review, 2025.

  3. 2024

    1. A. Guthke, P. L. Reiser, and P. Bürkner, “Data-Integrated Training of Surrogates as a Bayesian Hybrid Modeling Strategy,” 2024.
    2. P. L. Reiser, J. E. Aguilar, A. Guthke, and P.-C. Bürkner, “Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference.” 2024. doi: 10.18419/darus-4093.
    3. M. Alvarez Chaves, H. Gupta, U. Ehret, and A. Guthke, “Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data.” 2024. doi: 10.18419/darus-4087.
    4. M. Alvarez Chaves, U. Ehret, and A. Guthke, “UNITE Toolbox.” 2024. doi: 10.18419/darus-4188.
    5. A. Guthke, P. Reiser, and P.-C. Bürkner, “Quantifying Uncertainty in Surrogate-based Bayesian Inference,” 2024.
    6. M. Á. Chaves, E. A. Espinoza, U. Ehret, and A. Guthke, “Evaluating physics-based representations of hydrological systems through hybrid models and information theory,” 2024.
    7. A. Guthke, “Verbesserte Unsicherheitsabschätzung für (fehlerbehaftete) Grundwassermodelle,” in 29. Tagung der Fachsektion Hydrogeologie e.V. in der DGGV e.V., 2024.
    8. M. Álvarez Chaves, H. V. Gupta, U. Ehret, and A. Guthke, “On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data,” Entropy, vol. 26, Art. no. 5, 2024, doi: 10.3390/e26050387.
    9. H.-F. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions,” Water Resources Research, vol. 60, Art. no. 5, 2024, doi: 10.1029/2022WR033280.

  4. 2023

    1. M. Álvarez Chaves, A. Guthke, U. Ehret, and H. Gupta, “UNITE: A toolbox for unified diagnostic evaluation of physics-based, data-driven and hybrid models based on information theory,” in EGU General Assembly Conference Abstracts, 2023, pp. EGU––4039.
    2. A. Guthke, “Modified Bayesian Calibration Approaches to Tackle the Erroneous-Model Problem,” in AGU23, AGU, 2023.
    3. J. T. White, M. N. Fienen, C. R. Moore, and A. Guthke, “Editorial: Rapid, reproducible, and robust environmental modeling for decision support: worked examples and open-source software tools,” Frontiers in Earth Science, vol. 11, 2023, doi: 10.3389/feart.2023.1260581.
    4. F. Ejaz, A. Guthke, T. Wöhling, and W. Nowak, “Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model,” Journal of Hydrology, vol. 626, 2023, doi: https://doi.org/10.1016/j.jhydrol.2023.130323.
    5. 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, Art. no. 1, 2023, doi: 10.1007/s10596-022-10179-x.

  5. 2022

    1. A. Guthke, H.-F. Hsueh, T. Wöhling, and W. Nowak, “Bayesian updating despite model errors? A sliding time-window approach to rescue,” in EGU General Assembly Conference Abstracts, 2022, pp. EGU22––12525.
    2. M. Viswanathan, T. K. Weber, and A. Guthke, “An alternative strategy for combining likelihood values in Bayesian calibration to improve model predictions,” in EGU General Assembly Conference Abstracts, 2022, pp. EGU22––1210.
    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. H.-F. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Diagnosis of Model Errors With a Sliding Time-Window Bayesian Analysis,” Water resources research, vol. 58, Art. no. 2, 2022, doi: 10.1029/2021WR030590.

  6. 2021

    1. I. Banerjee, A. Guthke, C. Van De Ven, K. Mumford, and W. Nowak, “A Quantitative Method to Evaluate the Performance of Competing (Stochastic) Invasion Percolation Models under Different Gas-Flow Regimes,” in AGU Fall Meeting Abstracts, 2021, pp. H12E––05.
    2. 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, Art. no. 11, 2021, doi: 10.1029/2021WR030391.
    3. 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, Art. no. 7, 2021.

  7. 2020

    1. A. Guthke et al., “A unified framework for quantitative interdisciplinary flood risk assessment,” online: AGU Fall Meeting 2020, Dec. 2020.
    2. M. Höge, A. Guthke, and W. Nowak, “Bayesian Model Weighting: The Many Faces of Model Averaging,” Water, vol. 12, Art. no. 2, 2020, doi: 10.3390/w12020309.
    3. 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, Art. no. 11, 2020.

  8. 2019

    1. 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: https://doi.org/10.1016/j.jhydrol.2019.03.054.
    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, 2019, doi: 10.1016/j.jhydrol.2019.01.072.

  9. 2018

    1. P. Darscheid, A. Guthke, and U. Ehret, “A maximum-entropy method to estimate discrete distributions from samples ensuring nonzero probabilities,” Entropy, vol. 20, Art. no. 8, 2018.
    2. 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.

  10. 2017

    1. A. Guthke, “Defensible Model Complexity: A Call for Data-Based and Goal-Oriented Model Choice,” Groundwater, vol. 55, Art. no. 5, 2017, doi: 10.1111/gwat.12554.

  11. 2016

    1. W. Nowak and A. Guthke, “Entropy-based experimental design for optimal model discrimination in the geosciences,” Entropy, vol. 18, Art. no. 11, 2016.
    2. O. Lötgering-Lin, A. Schöniger, W. Nowak, and J. Groß, “Bayesian Model Selection Helps To Choose Objectively between Thermodynamic Models: A Demonstration of Selecting a Viscosity Model Based on Entropy Scaling,” Industrial & engineering chemistry research, vol. 55, Art. no. 38, 2016, doi: 10.1021/acs.iecr.6b02671.

  12. 2015

    1. A. Schöniger, W. Illman, T. Wöhling, and W. Nowak, “Finding the Right Balance Between Groundwater Model Complexity and Experimental Effort via Bayesian Model Selection,” Journal of Hydrology, vol. 531, Art. no. 1, 2015, doi: 10.1016/j.jhydrol.2015.07.047.
    2. A. Schöniger, T. Wöhling, and W. Nowak, “A statistical concept to assess the uncertainty in Bayesian model weights and its impact on model ranking,” Water Resources Research, vol. 51, Art. no. 9, 2015, doi: 10.1002/2015WR016918.
    3. T. Wöhling, A. Schöniger, S. Gayler, and W. Nowak, “Bayesian model averaging to explore the worth of data for soil-plant model selection and prediction,” Water Resources Research, vol. 51, Art. no. 4, 2015, doi: 10.1002/2014WR016292.

  13. 2014

    1. A. Schöniger, T. Wöhling, L. Samaniego, and W. Nowak, “Model selection on solid ground: rigorous comparison of nine ways to evaluate Bayesian evidence,” Water Resources Research, vol. 50, Art. no. 12, 2014, doi: 10.1002/2014WR016062.

  14. 2012

    1. A. Schöniger, W. Nowak, and H. J. H. Franssen, “Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography,” Water Resources Research, vol. 48, Art. no. W04502, 2012, doi: 10.1029/2011WR010462 (was the top cited 2012 WRR article in 2013).
  • Since Oct. 2021: Independent Junior Research Group Leader with SimTech, University of Stuttgart/Germany
  • 2017 - 2021: Postdoctoral researcher with the Department of Stochastic Simulation and Safety Research for Hydrosystems (IWS / LS³), University of Stuttgart/Germany
  • 2015 - 2016: Postdoctoral researcher with the Center for Applied Geoscience (ZAG), University of Tübingen/Germany
  • 2012 - 2015: Doctoral researcher within the International Research Training Group "Integrated Hydrosystem Modelling" (GRK 1829), University of Tübingen/Germany and University of Waterloo/ON, Canada
  • 2010 - 2018: Environmental consultant with BoSS Consult Gmbh, Stuttgart/Germany
  • 2004 - 2010: Studies of environmental engineering (Dipl.-Ing.) with a major in hydrosystem modelling, University of Stuttgart/Germany

EXC

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