Abstract

A recent experiment of Bowers et al. (2020) revealed that diffusive mixing of water isotopes (δ2H, δ18O) over a fully saturated soil sample of a few centimetres in length required several days to equilibrate completely. In this study, we present an approach to simulate such time-delayed diffusive mixing processes on the pore scale beyond instantaneously and perfectly mixed conditions. The diffusive pore mixing (DIPMI) approach is based on a Lagrangian perspective on water particles moving by diffusion over the pore space of a soil volume and carrying concentrations of solutes or isotopes. The idea of DIPMI is to account for the self-diffusion of water particles across a characteristic length scale of the pore space using pore-size-dependent diffusion coefficients. The model parameters can be derived from the soil-specific water retention curve and no further calibration is needed. We test our DIPMI approach by simulating diffusive mixing of water isotopes over the pore space of a saturated soil volume using the experimental data of Bowers et al. (2020). Simulation results show the feasibility of the DIPMI approach to reproduce measured mixing times and concentrations of isotopes at different tensions over the pore space. This result corroborates the finding that diffusive mixing in soils depends on the pore size distribution and the specific soil water retention properties. Additionally, we perform a virtual experiment with the DIPMI approach by simulating mixing and leaching processes of a solute in a vertical, saturated soil column and comparing results against simulations with the common perfect-mixing assumption. Results of this virtual experiment reveal that the frequently observed steep rise and long tailing of breakthrough curves, which are typically associated with non-uniform transport in heterogeneous soils, may also occur in homogeneous media as a result of imperfect subscale mixing in a macroscopically homogeneous soil matrix.

Highlights

  • Water isotopes are used widely as tracers to investigate a variety of hydrological processes (Sprenger et al, 2016)

  • Diffusive spreading of water depends on the pore size distribution and specific soil water retention properties

  • It is of interest to examine, in the following virtual experiment (Sects. 4.3 and 4.4), how pore-size-dependent and non5 instantaneous mixing affect simulations of water flow through a saturated soil column on a larger scale, and to delineate their effects on solute breakthrough and redistribution in soil. 4.3 Simulation of the virtual experiment Solute breakthrough curves exhibit a clear difference between simulations with the diffusive pore mixing (DIPMI) approach and the perfect-mixing assumption (Fig. 3)

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Summary

Introduction

Water isotopes are used widely as tracers to investigate a variety of hydrological processes (Sprenger et al, 2016). Common soil hydrological models average over pore-size-dependent differences in the flow field and concentration gradients in control volumes (Berkowitz et al, 2016) to describe diffusive mixing of water and solutes This implies that incoming “new” event water and “old” pockets of pre-event water in soil mix perfectly and instantaneously over 25 the subscale pore size distribution in a single time step. The establishment of stable water pockets in soils is possible, which may comprise significantly different isotopic and chemical compositions depending on the properties of infiltrating water This imperfect mixing of water and solutes in the pore space is frequently discussed rather in the context of rapid preferential flow in macroporous structures (Beven and Germann, 1982; Beven and Germann, 2013), which is commonly assumed to be the main reason for the characteristic steep rise and long tailing of corresponding breakthrough 35 curves (e.g., Berkowitz et al, 2006; Edery et al, 2014). To 20 this end, we simulate diffusive mixing and breakthrough of a representative solute in a vertical 1-D soil column during steady state, saturated flow and compare the results to simulations using the perfect-mixing assumption

Underlying concept of the LAST-Model The
The DIPMI approach: concept to represent subscale diffusion in a Lagrangian model
Simulating the experiment of
DIPMI simulations of the experiment of Bowers et al (2020)
Conclusions and Outlook
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