  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.

Computational Physics