Publications

Álvarez Chaves, M., Acuña Espinoza, E., Klotz, D., Gupta, H., Ehret, U., & Guthke, A. (2026). A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling. Machine Learning: Earth, under review.

Álvarez Chaves, M., Acuña Espinoza, E., Ehret, U., & Guthke, A. (2026). When Physics Gets in the Way: An Entropy-based Evaluation of Conceptual Constraints in Hybrid Hydrological Models. Hydrology and Earth System Sciences, 30, Article 3. https://doi.org/10.5194/hess-30-629-2026

Reiser, P., Aguilar, J. E., Guthke, A., & Bürkner, P.-C. (2025). Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. Statistics and Computing, 35, Article 3. https://doi.org/10.1007/s11222-025-10597-8

Reiser, P., Bürkner, P.-C., & Guthke, A. (2025). Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy. Statistics and Computing, under review.

Guthke, A., Bakhshipour, A. E., de Barros, F. P. J., Class, H., Daniell, J. E., Dittmer, U., Friedrich, M., Haas, J., Kropp, C., Merz, B., Oladyshkin, S., Schäfer, A., Sinsbeck, M., Straub, D., Terheiden, K., Wieprecht, S., & Nowak, W. (2025). Building Bridges Between Disciplines: A Generalized Mathematical Framework for Talking Quantitative Risk Assessment. Environmental Research: Infrastructure and Sustainability, under review.

Wöhling, T., Crespo Delgadillo, A. O., Kraft, M., & Guthke, A. (2025). Comparing Physics-based, Conceptual and Machine-Learning Models to Predict Groundwater Levels by Bayesian Model Averaging. Groundwater, 63, 484–505. https://doi.org/10.1111/gwat.13487

Banerjee, I., Guthke, A., Van De Ven, C. J., Mumford, K. G., & Nowak, W. (2025). A Framework for Objectively Comparing Competing Invasion Percolation Models based on Highly-Resolved Image Data. PLOS One, (under review).

Álvarez Chaves, M., Gupta, H. V., Ehret, U., & Guthke, A. (2024). On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data. Entropy, 26, Article 5. https://doi.org/10.3390/e26050387

Hsueh, H.-F., Guthke, A., Wöhling, T., & Nowak, W. (2024). Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions. Water Resources Research, 60, Article 5. https://doi.org/10.1029/2022WR033280

Ejaz, F., Guthke, A., Wöhling, T., & Nowak, W. (2023). Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model. Journal of Hydrology, 626. https://doi.org/10.1016/j.jhydrol.2023.130323

White, J. T., Fienen, M. N., Moore, C. R., & Guthke, A. (2023). Editorial: Rapid, reproducible, and robust environmental modeling for decision support: worked examples and open-source software tools. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1260581

Banerjee, I., Walter, P., Guthke, A., Mumford, K. G., & Nowak, W. (2023). The method of forced probabilities : a computation trick for Bayesian model evidence. Computational Geosciences, 27, Article 1. https://doi.org/10.1007/s10596-022-10179-x

Hsueh, H.-F., Guthke, A., Wöhling, T., & Nowak, W. (2022). Diagnosis of Model Errors With a Sliding Time-Window Bayesian Analysis. Water resources research, 58, Article 2. https://doi.org/10.1029/2021WR030590

Schäfer Rodrigues Silva, A., Weber, T. K., Gayler, S., Guthke, A., Höge, M., Streck, T., & Nowak, W. (2022). Diagnosing Similarities in Probabilistic Multi-Model Ensembles - an Application to Soil-Plant-Growth-Modeling. Modeling Earth Systems and Environment, 8, 5143–5175. https://doi.org/10.1007/s40808-022-01427-1

Alvarez Chaves, M. (2025). Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models. https://doi.org/10.18419/darus-4920

Reiser, P. L., Aguilar, J. E., Guthke, A., & Bürkner, P.-C. (2024). Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. https://doi.org/10.18419/darus-4093

Alvarez Chaves, M., Ehret, U., & Guthke, A. (2024). UNITE Toolbox. https://doi.org/10.18419/darus-4188

Alvarez Chaves, M., Gupta, H., Ehret, U., & Guthke, A. (2024). Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data. https://doi.org/10.18419/darus-4087

Guthke, A. (2026). Model-Data Integration via Bayesian Surrogate Modelling. Seminar Series of the Earth System Modeling Group at the Technical University of Munich, Germany (Online).

Guthke, A. (2025). Formal Treatment of Uncertainties in Earth Science Modelling: Merits & Pitfalls of Bayesian Model-Data Integration. Seminar Series of the Chair of Methods for Model-Based Development in Computational Engineering, RWTH Aachen University, Germany.

Álvarez Chaves, M., Gupta, H., Ehret, U., & Guthke, A. (2025). Evaluating uncertainty in probabilistic deep learning models using Information Theory. General Assembly of the European Geoscience Union.

Guthke, A. (2025). Uncertainties in Earth Science Modelling: Diagnostics, Quantification, and Implications for (Hybrid) Model Development. Seminar Series of the Institute of Earth Surface Dynamics and the Institute of Earth Sciences, University of Lausanne, Switzerland.

Guthke, A., Reiser, P. L., & Bürkner, P. (2025). Training Surrogates with Knowledge and Data: A Bayesian Hybrid Modelling Strategy. General Assembly of the European Geoscience Union.

Guthke, A. (2025). Bayesian and Information-Theoretic Tools for Diagnosing Earth Science Models. Seminar Series of the Chair of Hydrogeology at the Technical University of Munich, Germany.

Álvarez Chaves, M., Acuña Espinoza, E., Ehret, U., & Guthke, A. (2024). Evaluating the Balance of Physics-Based and Data-Driven Components in Hybrid Hydrological Models Using Information Theory. Annual Meeting of the American Geophysical Union.

Guthke, A. (2024). Diagnostic Model Evaluation and Selection: Bayesian and Information-Theoretic Concepts for the Environmental Sciences. Soil-Scientific Colloquium, University of Hohenheim, Germany.

Chaves, M. Á., Espinoza, E. A., Ehret, U., & Guthke, A. (2024). Evaluating physics-based representations of hydrological systems through hybrid models and information theory. EGU General Assembly Conference Abstracts, EGU24.

Guthke, A. (2024). Verbesserte Unsicherheitsabschätzung für (fehlerbehaftete) Grundwassermodelle. 29. Tagung Der Fachsektion Hydrogeologie E.V. In Der DGGV E.V.

Guthke, A., Reiser, P. L., & Bürkner, P. (2024). Data-Integrated Training of Surrogates as a Bayesian Hybrid Modeling Strategy (U. Annual Meeting of AGU, Washington/DC, Ed.).

Chaves, M. Á., Guthke, A., Ehret, U., & Gupta, H. (2023). UNITE: A Toolbox for Unified Diagnostic Evaluation of Physics-based, Data-driven and Hybrid Models based on Information Theory. EGU General Assembly Conference Abstracts, EGU23.

Guthke, A. (2023). Modified Bayesian Calibration Approaches to Tackle the Erroneous-Model Problem. AGU Fall Meeting Conference Abstracts, AGU23.

Guthke, A., Hsueh, H.-F., Wöhling, T., & Nowak, W. (2022). Bayesian updating despite model errors? A sliding time-window approach to rescue. EGU General Assembly Conference Abstracts, EGU22–12525.

Viswanathan, M., Weber, T. K., & Guthke, A. (2022). An alternative strategy for combining likelihood values in Bayesian calibration to improve model predictions. EGU General Assembly Conference Abstracts, EGU22–1210.

Guthke, A., Reiser, P., & Bürkner, P.-C. (2024). Quantifying Uncertainty in Surrogate-based Bayesian Inference. EGU General Assembly Conference Abstracts, EGU24.

Guthke, A., Hsueh, H.-F., Wöhling, T., & Nowak, W. (2024). Making Bayesian Inference and Predictions More Realistic: A Sliding Time-Window Approach. Annual Meeting of the American Geophysical Union.

Reiser, P., Aguilar, J. E., Guthke, A., & Bürkner, P.-C. (2024). Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. SIAM Conference on Uncertainty Quantification (UQ24).

Reiser, P., Aguilar, J. E., Guthke, A., & Bürkner, P.-C. (2023). Quantifying Uncertainty in Surrogate-based Bayesian Inference. International Conference on Data-Integrated Simulation Science (SimTech2023).

Reiser, P. (2023). Quantifying Uncertainty in Surrogate-based Bayesian Inference. Bayes Comp 2023.