| Methods comparison for detecting trends in herbicide monitoring time-series in streams
Chow, R., Spycher, S., Scheidegger, R., Doppler, T., Dietzel, A., Fenicia, F., & Stamm, C. (2023). Methods comparison for detecting trends in herbicide monitoring time-series in streams. Science of the Total Environment, 891, 164226 (11 pp.). https://doi.org/10.1016/j.scitotenv.2023.164226 |
| Exploring signature-based model calibration for streamflow prediction in ungauged basins
Dal Molin, M., Kavetski, D., Albert, C., & Fenicia, F. (2023). Exploring signature-based model calibration for streamflow prediction in ungauged basins. Water Resources Research, 59(7), e2022WR031929 (32 pp.). https://doi.org/10.1029/2022WR031929 |
| Challenges of spatially extrapolating aquatic pesticide pollution for policy evaluation
Fabre, C., Doppler, T., Chow, R., Fenicia, F., Scheidegger, R., Dietzel, A., & Stamm, C. (2023). Challenges of spatially extrapolating aquatic pesticide pollution for policy evaluation. Science of the Total Environment, 875, 162639 (11 pp.). https://doi.org/10.1016/j.scitotenv.2023.162639 |
| HESS opinions: are soils overrated in hydrology?
Gao, H., Fenicia, F., & Savenije, H. H. G. (2023). HESS opinions: are soils overrated in hydrology? Hydrology and Earth System Sciences, 27(14), 2607-2620. https://doi.org/10.5194/hess-27-2607-2023 |
| 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 |
| 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 |
| Application of stochastic time dependent parameters to improve the characterization of uncertainty in conceptual hydrological models
Bacci, M., Dal Molin, M., Fenicia, F., Reichert, P., & Šukys, J. (2022). Application of stochastic time dependent parameters to improve the characterization of uncertainty in conceptual hydrological models. Journal of Hydrology, 612, 128057 (19 pp.). https://doi.org/10.1016/j.jhydrol.2022.128057 |
| Correspondence between model structures and hydrological signatures: a large-sample case study using 508 Brazilian catchments
David, P. C., Chaffe, P. L. B., Chagas, V. B. P., Dal Molin, M., Oliveira, D. Y., Klein, A. H. F., & Fenicia, F. (2022). Correspondence between model structures and hydrological signatures: a large-sample case study using 508 Brazilian catchments. Water Resources Research, 58(3), e2021WR030619 (20 pp.). https://doi.org/10.1029/2021WR030619 |
| Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis
Fenicia, F., & McDonnell, J. J. (2022). Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis. Journal of Hydrology, 605, 127287 (18 pp.). https://doi.org/10.1016/j.jhydrol.2021.127287 |
| Modeling streamflow variability at the regional scale: (2) development of a bespoke distributed conceptual model
Fenicia, F., Meißner, D., & McDonnell, J. J. (2022). Modeling streamflow variability at the regional scale: (2) development of a bespoke distributed conceptual model. Journal of Hydrology, 605, 127286 (18 pp.). https://doi.org/10.1016/j.jhydrol.2021.127286 |
| Frozen soil hydrological modeling for a mountainous catchment northeast of the Qinghai-Tibet Plateau
Gao, H., Han, C., Chen, R., Feng, Z., Wang, K., Fenicia, F., & Savenije, H. (2022). Frozen soil hydrological modeling for a mountainous catchment northeast of the Qinghai-Tibet Plateau. Hydrology and Earth System Sciences, 26(15), 4187-4208. https://doi.org/10.5194/hess-26-4187-2022 |
| 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 |
| An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments
Prieto, C., Le Vine, N., Kavetski, D., Fenicia, F., Scheidegger, A., & Vitolo, C. (2022). An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments. Water Resources Research, 58(3), e2021WR030705 (28 pp.). https://doi.org/10.1029/2021WR030705 |
| Hydrologic impacts of cascading reservoirs in the middle and lower Hanjiang River basin under climate variability and land use change
Zhang, X., Yang, H., Zhang, W., Fenicia, F., Peng, H., & Xu, G. (2022). Hydrologic impacts of cascading reservoirs in the middle and lower Hanjiang River basin under climate variability and land use change. Journal of Hydrology: Regional Studies, 44, 101253 (22 pp.). https://doi.org/10.1016/j.ejrh.2022.101253 |
| Quantifying the uncertainty of a conceptual herbicide transport model with time‐dependent, stochastic parameters
Ammann, L., Stamm, C., Fenicia, F., & Reichert, P. (2021). Quantifying the uncertainty of a conceptual herbicide transport model with time‐dependent, stochastic parameters. Water Resources Research, 57(8), e2020WR028311 (27 pp.). https://doi.org/10.1029/2020WR028311 |
| Behind the scenes of streamflow model performance
Bouaziz, L. J. E., Fenicia, F., Thirel, G., De Boer-Euser, T., Buitink, J., Brauer, C. C., … Hrachowitz, M. (2021). Behind the scenes of streamflow model performance. Hydrology and Earth System Sciences, 25(2), 1069-1095. https://doi.org/10.5194/hess-25-1069-2021 |
| SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models
Dal Molin, M., Kavetski, D., & Fenicia, F. (2021). SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models. Geoscientific Model Development, 14(11), 7047-7072. https://doi.org/10.5194/gmd-14-7047-2021 |
| Behind every robust result is a robust method: perspectives from a case study and publication process in hydrological modelling
Fenicia, F., & Kavetski, D. (2021). Behind every robust result is a robust method: perspectives from a case study and publication process in hydrological modelling. Hydrological Processes, 35(8), e14266 (9 pp.). https://doi.org/10.1002/hyp.14266 |
| Understanding the information content in the hierarchy of model development decisions: learning from data
Gharari, S., Gupta, H. V., Clark, M. P., Hrachowitz, M., Fenicia, F., Matgen, P., & Savenije, H. H. G. (2021). Understanding the information content in the hierarchy of model development decisions: learning from data. Water Resources Research, 57(6), e2020WR027948 (35 pp.). https://doi.org/10.1029/2020WR027948 |
| Unraveling the riverine antibiotic resistome: the downstream fate of anthropogenic inputs
Lee, J., Ju, F., Maile-Moskowitz, A., Beck, K., Maccagnan, A., McArdell, C. S., … Bürgmann, H. (2021). Unraveling the riverine antibiotic resistome: the downstream fate of anthropogenic inputs. Water Research, 197, 117050 (12 pp.). https://doi.org/10.1016/j.watres.2021.117050 |