Abstract
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 properties, and TTD predictions. Eight recharge realizations are sampled from a high-resolution dataset of land surface fluxes and states. Calibration-constrained hydraulic conductivity fields (Ks fields) are stochastically generated using the 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 fit against the numerical solutions, serving as a reference for 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 discharging 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 Ks fields moderately magnify or attenuate the predicted MTTs. The analytical model does not properly replicate the numerical solution, and it underestimates the mean travel time. Simulated SAS functions indicate an overall preference for young water for all realizations. The spatial pattern of recharge controls the shape and breadth of simulated TTDs and SAS functions by changing the spatial distribution of particles' pathways. In conclusion, overlooking the spatial nonuniformity and uncertainty of input (forcing) will result in biased travel time predictions. We also highlight the worth of reliable observations in reducing predictive uncertainty and the good interpretability of SAS functions in terms of understanding catchment transport processes.
Highlights
Travel/transit time distributions (TTDs) of groundwater provide a description of how aquifers store and release water and pollutants under external forcing conditions, which has significant implications for interdisciplinary environmental studies
The different realizations of Ks fields manipulate the mean travel time. cv varies from 7.81 % to 15.56 %, indicating a modest degree of uncertainty propagated from Ks estimation to travel time distributions (TTDs) prediction
We explore the relationship between the uncertainty of recharge, calibration-constrained hydraulic conductivity realizations, and predictions of groundwater TTDs
Summary
Travel/transit time distributions (TTDs) of groundwater provide a description of how aquifers store and release water and pollutants under external forcing conditions, which has significant implications for interdisciplinary environmental studies. Remarkable time lags of the reaction of streamflow with outer forcings and considerable amounts of “old water” (i.e., water with an age of decades or longer) in streamflow have been observed in many studies (Howden et al, 2010; Stewart et al, 2012). Groundwater TTDs offer important insights into the vulnerability of aquifers to pollution spreading, and they are critically important for the environmental assessment of non-point-source agricultural contamination (Böhlke and Denver, 1995; Böhlke, 2002; Molnat and Gascuel-Odoux, 2002; Eberts et al, 2012). TTDs shed light on the quantification of the long-term influence of agricultural contamination, which is crucial for water quality and sustainability
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