Next Generation Computers Warrant Next Generation Groundwater Models.

Modern hydrologic models have extraordinary capabilities for representing complex process in surface-subsurface systems. These capabilities have revolutionized the way we conceptualize flow systems, but how to represent uncertainty in simulated flow systems is not as well developed. Currently, characterizing model uncertainty can be computationally expensive, in part, because the techniques are appended to the numerical methods rather than seamlessly integrated. The next generation of computers, however, presents opportunities to reformulate the modeling problem so that the uncertainty components are handled more directly within the flow system simulation. Misconceptions about quantum computing abound and they will not be a "silver bullet" for solving all complex problems, but they might be leveraged for certain kinds of highly uncertain problems, such as groundwater (GW). The point of this issue paper is that the GW community could try to revise the foundations of our models so that the governing equations being solved are tailored specifically for quantum computers. The goal moving forward should not just be to accelerate the models we have, but also to address their deficiencies. Embedding uncertainty into the models by evolving distribution functions will make predictive GW modeling more complicated, but doing so places the problem into a complexity class that is highly efficient on quantum computing hardware. Next generation GW models could put uncertainty into the problem at the very beginning of a simulation and leave it there throughout, providing a completely new way of simulating subsurface flows.

A review of spatial Markov models for predicting pre-asymptotic and anomalous transport in porous and fractured media.

Heterogeneity across a broad range of scales in geologic porous media often manifests in observations of non-Fickian or anomalous transport. While traditional anomalous transport models can successfully make predictions in certain geological systems, increasing evidence suggests that assumptions relating to independent and identically distributed increments constrain where and when they can be reliably applied. A relatively novel model, the Spatial Markov model (SMM), relaxes the assumption of independence. The SMM belongs to the family of correlated continuous time random walks and has shown promise across a wide range of transport problems relevant to natural porous media. It has been successfully used to model conservative as well as more recently reactive transport in highly complex flows ranging from pore scales to much larger scales of interest in geology and subsurface hydrology. In this review paper we summarize its original development and provide a comprehensive review of its advances and applications as well as lay out a vision for its future development.

Reactive particle-tracking solutions to a benchmark problem on heavy metal cycling in lake sediments.

Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations.

Predicting the enhancement of mixing-driven reactions in nonuniform flows using measures of flow topology.

The ability for reactive constituents to mix is often the key limiting factor for the completion of reactions across a huge range of scales in a variety of media. In flowing systems, deformation and shear enhance mixing by bringing constituents into closer proximity, thus increasing reaction potential. Accurately quantifying this enhanced mixing is key to predicting reactions and typically is done by observing or simulating scalar transport. To eliminate this computationally expensive step, we use a Lagrangian stochastic framework to derive the enhancement to reaction potential by calculating the collocation probability of particle pairs in a heterogeneous flow field accounting for deformations. We relate the enhanced reaction potential to three well known flow topology metrics and demonstrate that it is best correlated to (and asymptotically linear with) one: the largest eigenvalue of the (right) Cauchy-Green tensor.

Scalar dissipation rates in non-conservative transport systems.

This work considers how the inferred mixing state of diffusive and advective-diffusive systems will vary over time when the solute masses are not constant over time. We develop a number of tools that allow the scalar dissipation rate to be used as a mixing measure in these systems without calculating local concentration gradients. The behavior of dissipation rates is investigated for single and multi-component kinetic reactions and a commonly studied equilibrium reaction. The scalar dissipation rate of a tracer experiencing first-order decay can be determined exactly from the decay constant and the dissipation rate of a passive tracer, and the mixing rate of a conservative component is not the superposition of the solute specific mixing rates. We then show how the behavior of the scalar dissipation rate can be determined from a limited subset of an infinite domain. Corrections are derived for constant and time dependent limits of integration the latter is used to approximate dissipation rates in advective-diffusive systems. Several of the corrections exhibit similarities to the previous work on mixing, including non-Fickian mixing. This illustrates the importance of accounting for the effects that reaction systems or limited monitoring areas may have on the inferred mixing state.

Using groundwater age distributions to estimate the effective parameters of Fickian and non-Fickian models of solute transport.

Groundwater age distributions are used to estimate the parameters of Fickian, and non-Fickian, effective models of solute transport. Based on the similarities between the transport and age equations, we develop a deconvolution based approach that describes transport between two monitoring wells. We show that the proposed method gives exact estimates of the travel time distribution between two wells when the domain is stationary and that the method still provides useful information on transport when the domain is non-stationary. The method is demonstrated using idealized uniform and layered 2-D aquifers. Homogeneous transport is determined exactly and non-Fickian transport in a layered aquifer was also approximated very well, even though this example problem is shown to be scale-dependent. This work introduces a method that addresses a significant limitation of tracer tests and non-Fickian transport modeling which is the difficulty in determining the effective parameters of the transport model.

Non-Fickian dispersion of groundwater age.

We expand the governing equation of groundwater age to account for non-Fickian dispersive fluxes using continuous random walks. Groundwater age is included as an additional (fifth) dimension on which the volumetric mass density of water is distributed and we follow the classical random walk derivation now in five dimensions. The general solution of the random walk recovers the previous conventional model of age when the low order moments of the transition density functions remain finite at their limits and describes non-Fickian age distributions when the transition densities diverge. Previously published transition densities are then used to show how the added dimension in age affects the governing differential equations. Depending on which transition densities diverge, the resulting models may be nonlocal in time, space, or age and can describe asymptotic or pre-asymptotic dispersion. A joint distribution function of time and age transitions is developed as a conditional probability and a natural result of this is that time and age must always have identical transition rate functions. This implies that a transition density defined for age can substitute for a density in time and this has implications for transport model parameter estimation. We present examples of simulated age distributions from a geologically based, heterogeneous domain that exhibit non-Fickian behavior and show that the non-Fickian model provides better descriptions of the distributions than the Fickian model.