Department Systems Analysis, Integrated Assessment and Modelling

Distributed model development in the Thur catchment (Switzerland)

Globally, water supply and flood protection are expected to become a tremendous challenge in the near future. Modifications in the amount and timing of precipitation (rain and snow) and elevated temperatures induced by climate change are modifying the hydrological cycle, leading to more severe floods and droughts. Therefore, being able to understand and predict future system’s behavior will be a key advantage in water resources management.

The “Water distribution” project (funded by SNF) aims to investigate how river restoration efforts and the construction of retention areas can affect groundwater systems in the Thur catchment and can be used to help mitigate floods and droughts. To achieve this objective, monitoring and modeling are coupled together, in the attempt to better understand the system’s behavior. The goal is to construct an adaptive and real-time monitoring system which is triggered by the model and streams data into it.

As part of the “Water distribution” project, this Ph.D. program focuses explicitly on the development of a semi-distributed hydrological model in the Thur catchment. The model development follows a “flexible” approach, where different model structures are tested and compared. Moreover, modeling and experimenting combine into a learning process, aimed at understanding dominant hydrological processes, and at predicting water flow and substance transport in the river.

The Thur catchment (~ 1700 km2) is located in north-eastern Switzerland and it is part of the Rhine basin. The strong climatic gradient and complex geologies result in a strong variability in hydrological responses. This project will elucidate the relationships between landscape properties, climate, and hydrological signatures, and develop strategies to use these relationships to inform key model decisions.


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.). doi:10.1029/2022WR031929, Institutional Repository
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.). doi:10.1016/j.jhydrol.2022.128057, Institutional Repository
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.). doi:10.1029/2021WR030619, Institutional Repository
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. doi:10.5194/gmd-14-7047-2021, Institutional Repository
Jansen, K. F., Teuling, A. J., Craig, J. R., Dal Molin, M., Knoben, W. J. M., Parajka, J., … Melsen, L. A. (2021). Mimicry of a conceptual hydrological model (HBV): what's in a name?. Water Resources Research, 57(5), e2020WR029143 (31 pp.). doi:10.1029/2020WR029143, Institutional Repository
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.). doi:10.1016/j.watres.2021.117050, Institutional Repository
Dal Molin, M., Schirmer, M., Zappa, M., & Fenicia, F. (2020). Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment. Hydrology and Earth System Sciences, 24(3), 1319-1345. doi:10.5194/hess-24-1319-2020, Institutional Repository