Articles | Volume 23, issue 1
https://doi.org/10.5194/hess-23-171-2019
https://doi.org/10.5194/hess-23-171-2019
Research article
 | 
15 Jan 2019
Research article |  | 15 Jan 2019

Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions

Miao Jing, Falk Heße, Rohini Kumar, Olaf Kolditz, Thomas Kalbacher, and Sabine Attinger

Related authors

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
Miao Jing, Rohini Kumar, Falk Heße, Stephan Thober, Oldrich Rakovec, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 24, 1511–1526, https://doi.org/10.5194/hess-24-1511-2020,https://doi.org/10.5194/hess-24-1511-2020, 2020
Short summary
Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS)
Miao Jing, Falk Heße, Rohini Kumar, Wenqing Wang, Thomas Fischer, Marc Walther, Matthias Zink, Alraune Zech, Luis Samaniego, Olaf Kolditz, and Sabine Attinger
Geosci. Model Dev., 11, 1989–2007, https://doi.org/10.5194/gmd-11-1989-2018,https://doi.org/10.5194/gmd-11-1989-2018, 2018

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Uncertainty analysis
Data-driven estimates for the geostatistical characterization of subsurface hydraulic properties
Falk Heße, Sebastian Müller, and Sabine Attinger
Hydrol. Earth Syst. Sci., 28, 357–374, https://doi.org/10.5194/hess-28-357-2024,https://doi.org/10.5194/hess-28-357-2024, 2024
Short summary
Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020,https://doi.org/10.5194/hess-24-4971-2020, 2020
Short summary
Interpretation of multi-scale permeability data through an information theory perspective
Aronne Dell'Oca, Alberto Guadagnini, and Monica Riva
Hydrol. Earth Syst. Sci., 24, 3097–3109, https://doi.org/10.5194/hess-24-3097-2020,https://doi.org/10.5194/hess-24-3097-2020, 2020
Short summary
Spatially distributed sensitivity of simulated global groundwater heads and flows to hydraulic conductivity, groundwater recharge, and surface water body parameterization
Robert Reinecke, Laura Foglia, Steffen Mehl, Jonathan D. Herman, Alexander Wachholz, Tim Trautmann, and Petra Döll
Hydrol. Earth Syst. Sci., 23, 4561–4582, https://doi.org/10.5194/hess-23-4561-2019,https://doi.org/10.5194/hess-23-4561-2019, 2019
Short summary
Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios
Syed M. Touhidul Mustafa, M. Moudud Hasan, Ajoy Kumar Saha, Rahena Parvin Rannu, Els Van Uytven, Patrick Willems, and Marijke Huysmans
Hydrol. Earth Syst. Sci., 23, 2279–2303, https://doi.org/10.5194/hess-23-2279-2019,https://doi.org/10.5194/hess-23-2279-2019, 2019
Short summary

Cited articles

Aigner, T.: Calcareous Tempestites: Storm-dominated Stratification in Upper Muschelkalk Limestones (Middle Trias, SW-Germany), in: Cyclic and Event Stratification, edited by: Einsele, G. and Seilacher, A., 180–198, Springer Berlin Heidelberg, Berlin, Heidelberg, 1982. a
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, https://doi.org/10.1029/2005WR004745, 2007. a
Basu, N. B., Jindal, P., Schilling, K. E., Wolter, C. F., and Takle, E. S.: Evaluation of analytical and numerical approaches for the estimation of groundwater travel time distribution, J. Hydrol., 475, 65–73, https://doi.org/10.1016/j.jhydrol.2012.08.052, 2012. a, b, c
Benettin, P., Kirchner, J. W., Rinaldo, A., and Botter, G.: Modeling chloride transport using travel time distributions at Plynlimon, Wales, Water Resour. Res., 51, 3259–3276, https://doi.org/10.1002/2014WR016600, 2015. a, b, c
Benettin, P., Soulsby, C., Birkel, C., Tetzlaff, D., Botter, G., and Rinaldo, A.: Using SAS functions and high-resolution isotope data to unravel travel time distributions in headwater catchments, Water Resour. Res., 53, 1864–1878, https://doi.org/10.1002/2016WR020117, 2017. a
Download
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.