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.
Research article
| 
15 Jan 2019
Research article |
| 15 Jan 2019
| 15 Jan 2019
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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25 citations as recorded by crossref.
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- Accelerating the Lagrangian simulation of water ages on distributed, multi-GPU platforms: The importance of dynamic load balancing C. Yang et al. 10.1016/j.cageo.2022.105189
- Multimodal water age distributions and the challenge of complex hydrological landscapes N. Rodriguez et al. 10.1002/hyp.13770
- Prediction of Water Quality Index in Drinking Water Distribution System Using Activation Functions Based Ann S. Vijay & K. Kamaraj 10.1007/s11269-020-02729-8
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- Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco L. El Mezouary et al. 10.1007/s13201-024-02162-x
- An analytical approach for urban groundwater transit time distributions accounting for the effect of stormwater infiltration system M. Jing et al. 10.1016/j.jhydrol.2021.127413
- Modeling groundwater transit time distributions and means across a Nebraska watershed: Effects of heterogeneity in the aquifer, riverbed, and recharge parameters C. Zeyrek et al. 10.1016/j.jhydrol.2022.128891
- Integrating of Bayesian model averaging and formal likelihood function to enhance groundwater process modeling in arid environments A. Jafarzadeh et al. 10.1016/j.asej.2024.103127
- Assessing the response of groundwater quantity and travel time distribution to 1.5, 2, and 3 °C global warming in a mesoscale central German basin M. Jing et al. 10.5194/hess-24-1511-2020
- Towards the construction of representative regional hydro(geo)logical numerical models: Modelling the upper Danube basin as a starting point E. Pujades et al. 10.3389/feart.2023.1061420
- A Steady-State Model to Simulate Groundwater Flow in Unconfined Aquifer M. Pagnozzi et al. 10.3390/app10082708
- Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales C. Yang et al. 10.1016/j.cageo.2021.104760
- On the interplay between hillslope and drainage network flow dynamics in the catchment travel time distribution A. Zarlenga et al. 10.1002/hyp.14530
- Effect of data length, spin-up period and spatial model resolution on fully distributed hydrological model calibration in the Moselle basin Ö. Ekmekcioğlu et al. 10.1080/02626667.2022.2046754
- Variability of Water Transit Time Distributions at the Strengbach Catchment (Vosges Mountains, France) Inferred Through Integrated Hydrological Modeling and Particle Tracking Algorithms S. Weill et al. 10.3390/w11122637
- Coupled effects of observation and parameter uncertainty on urban groundwater infrastructure decisions M. Mautner et al. 10.5194/hess-26-1319-2022
- Analytic solutions for quasi-3D seepage in a shallow unconfined aquifer as a plane composed of a transpiration-inducing park and its hydraulically commingled exterior A. Kacimov 10.1016/j.jhydrol.2023.130302
- Performance Assessment of Model Averaging Techniques to Reduce Structural Uncertainty of Groundwater Modeling A. Jafarzadeh et al. 10.1007/s11269-021-03031-x
- LM4-SHARC v1.0: resolving the catchment-scale soil–hillslope aquifer–river continuum for the GFDL Earth system modeling framework M. Hong et al. 10.5194/gmd-18-2275-2025
- Transport and Water Age Dynamics in Soils: A Comparative Study of Spatially Integrated and Spatially Explicit Models M. Asadollahi et al. 10.1029/2019WR025539
- Residence time distributions in non-uniform aquifer recharge and thickness conditions – An analytical approach based on the assumption of Dupuit-Forchheimer S. Leray et al. 10.1016/j.jhydrol.2019.04.032
- 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: 02 Jul 2025
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...
