Active Filters

  • (-) Eawag Authors = Fenicia, Fabrizio
Search Results 1 - 20 of 43
Select Page
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