Articles | Volume 23, issue 1
https://doi.org/10.5194/hess-23-171-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-171-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions
Department of Computational Hydrosystems, UFZ – Helmholtz Centre
for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany
Institute of Geosciences, Friedrich Schiller University Jena,
Burgweg 11, 07749 Jena, Germany
Falk Heße
CORRESPONDING AUTHOR
Department of Computational Hydrosystems, UFZ – Helmholtz Centre
for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany
Rohini Kumar
Department of Computational Hydrosystems, UFZ – Helmholtz Centre
for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany
Olaf Kolditz
Department of Environmental
Informatics, UFZ – Helmholtz Centre for Environmental Research, Permoserstr.
15, 04318 Leipzig, Germany
Applied Environmental Systems Analysis,
Technische Universität Dresden, Dresden, Germany
Thomas Kalbacher
Department of Environmental
Informatics, UFZ – Helmholtz Centre for Environmental Research, Permoserstr.
15, 04318 Leipzig, Germany
Sabine Attinger
Department of Computational Hydrosystems, UFZ – Helmholtz Centre
for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany
Institute of
Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str.
24–25, 14476 Potsdam, Germany
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- Assessing the contribution of groundwater to catchment travel time distributions through integrating conceptual flux tracking with explicit Lagrangian particle tracking M. Jing et al. 10.1016/j.advwatres.2021.103849
- Revealing the positive influence of young water fractions derived from stable isotopes on the robustness of karst water resources predictions K. Çallı et al. 10.1016/j.jhydrol.2023.129549
- Lagrange tracking-based long-term drift trajectory prediction method for Autonomous Underwater Vehicle S. Zheng et al. 10.3934/mbe.2023932
Latest update: 16 Nov 2024
Short summary
We evaluated the uncertainty propagation from the inputs (forcings) and parameters to the predictions of groundwater travel time distributions (TTDs) using a fully distributed numerical model (mHM-OGS) and the StorAge Selection (SAS) function. Through detailed numerical and analytical investigations, we emphasize the key role of recharge estimation in the reliable predictions of TTDs and the good interpretability of the SAS function.
We evaluated the uncertainty propagation from the inputs (forcings) and parameters to the...