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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-383
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Jul 2018

Research article | 30 Jul 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

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

Miao Jing1,2, Falk Heße1, Rohini Kumar1, Olaf Kolditz3,4, and Sabine Attinger1,5 Miao Jing et al.
  • 1Department of Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany
  • 2Institute of Geosciences, Friedrich Schiller University Jena, Burgweg 11, 07749 Jena, Germany
  • 3Department of Environmental Informatics, UFZ – Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany
  • 4Applied Environmental Systems Analysis, Technische Universität Dresden, Dresden, Germany
  • 5Institute of Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany

Abstract. Groundwater travel time distributions (TTDs) provide a robust description of the subsurface mixing behavior and hydrological response of a subsurface system. Lagrangian particle tracking is often used to derive the groundwater TTDs. The reliability of this approach is subjected to the uncertainty of external forcings, internal hydraulic properties, and the interplay between them. Here, we evaluate the uncertainty of catchment groundwater TTDs in an agricultural catchment using a 3-D groundwater model with an overall focus on revealing the relationship between external forcing, internal hydraulic property, and TTD predictions. A stratigraphic aquifer model is applied to represent the spatial structure of the aquifer. Several recharge realizations are sampled from a high-resolution dataset of land surface fluxes and states. Constrained to expert knowledge and groundwater head observations, many realizations of hydraulic conductivity fields are stochastically generated using null-space Monte Carlo (NSMC) method for each recharge realization. The random walk particle tracking (RWPT) method is used to track the pathways of particles and compute travel times. Moreover, an analytical model under the random sampling (RS) assumption is fitted against the numerical solutions, serving as a reference of the mixing behavior of the model domain. The StorAge Selection (SAS) function is used to interpret the results in terms of quantifying the systematic preference for young/old water. The simulation results reveal the primary effect of recharge on the predicted mean travel time (MTT). The different realizations of calibration-constrained hydraulic conductivity fields moderately magnify or attenuate the predicted MTTs, provided that most parameters can be well constrained to the observations. The analytical solution under a random sampling assumption does not properly replicate the numerical solution, and underestimates the mean travel time. The SAS functions of ensemble simulations indicate an overall preference for young water for all realizations. The spatial pattern of recharge also has a strong impact on the shape and breadth of simulated TTDs. In conclusion, overlooking the input (forcing) uncertainty will result in biased travel time predictions, and may underestimate the overall uncertainty of TTD predictions. We also highlight the worth of reliable observations in reducing predictive uncertainty, and the good interpretability of SAS function in terms of understanding catchment transport processes.

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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 StorAge Selection (SAS) function. Through detailed numerical and analytical investiagtions, we emphasize the key role of recharge estimation in the reliable predictions of TTDs and the good interpretability of SAS function.
We evaluated the uncertainty propagation from the inputs (forcings) and parameters to the...
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