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	Metamorphic testing of machine learning and conceptual hydrologic models Reichert, P., Ma, K., Höge, M., Fenicia, F., Baity-Jesi, M., Feng, D., & Shen, C. (2024). Metamorphic testing of machine learning and conceptual hydrologic models. Hydrology and Earth System Sciences, 28(11), 2505-2529. https://doi.org/10.5194/hess-28-2505-2024 Detailed Record Published Version
	EStreams: an integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe do Nascimento, T. V. M., Rudlang, J., Höge, M., van der Ent, R., Chappon, M., Seibert, J., … Fenicia, F. (2024). EStreams: an integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe. Scientific Data, 11(1), 879 (19 pp.). https://doi.org/10.1038/s41597-024-03706-1 Detailed Record Published Version
	CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland Höge, M., Kauzlaric, M., Siber, R., Schönenberger, U., Horton, P., Schwanbeck, J., … Fenicia, F. (2023). CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland. Earth System Science Data, 15(12), 5755-5784. https://doi.org/10.5194/essd-15-5755-2023 Detailed Record Published Version
	Differentiable modelling to unify machine learning and physical models for geosciences Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., … Lawson, K. (2023). Differentiable modelling to unify machine learning and physical models for geosciences. Nature Reviews Earth & Environment, 4, 552-567. https://doi.org/10.1038/s43017-023-00450-9 Detailed Record Published Version Accepted Version
	Lumped geohydrological modelling for long-term predictions of groundwater storage and depletion Ejaz, F., Wöhling, T., Höge, M., & Nowak, W. (2022). Lumped geohydrological modelling for long-term predictions of groundwater storage and depletion. Journal of Hydrology, 606, 127347 (22 pp.). https://doi.org/10.1016/j.jhydrol.2021.127347 Detailed Record Published Version Accepted Version
	Improving hydrologic models for predictions and process understanding using neural ODEs Höge, M., Scheidegger, A., Baity-Jesi, M., Albert, C., & Fenicia, F. (2022). Improving hydrologic models for predictions and process understanding using neural ODEs. Hydrology and Earth System Sciences, 26(19), 5085-5102. https://doi.org/10.5194/hess-26-5085-2022 Detailed Record Published Version
	Diagnosing similarities in probabilistic multi-model ensembles: an application to soil–plant-growth-modeling Schäfer Rodrigues Silva, A., Weber, T. K. D., Gayler, S., Guthke, A., Höge, M., Nowak, W., & Streck, T. (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 Detailed Record Published Version