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HESS | Articles | Volume 23, issue 1
Hydrol. Earth Syst. Sci., 23, 171–190, 2019
https://doi.org/10.5194/hess-23-171-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 171–190, 2019
https://doi.org/10.5194/hess-23-171-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 et al.
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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
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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...
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