Abstract. The subsurface is a temporally dynamic and spatially heterogeneous
compartment of the Earth's critical zone, and biogeochemical transformations
taking place in this compartment are crucial for the cycling of nutrients.
The impact of spatial heterogeneity on such microbially mediated nutrient
cycling is not well known, which imposes a severe challenge in the prediction
of in situ biogeochemical transformation rates and further of nutrient
loading contributed by the groundwater to the surface water bodies.
Therefore, we used a numerical modelling approach to evaluate the
sensitivity of groundwater microbial biomass distribution and nutrient
cycling to spatial heterogeneity in different scenarios accounting for
various residence times. The model results gave us an insight into domain
characteristics with respect to the presence of oxic niches in predominantly
anoxic zones and vice versa depending on the extent of spatial heterogeneity
and the flow regime. The obtained results show that microbial abundance,
distribution, and activity are sensitive to the applied flow regime and that
the mobile (i.e. observable by groundwater sampling) fraction of microbial
biomass is a varying, yet only a small, fraction of the total biomass in a
domain. Furthermore, spatial heterogeneity resulted in anaerobic niches in
the domain and shifts in microbial biomass between active and inactive
states. The lack of consideration of spatial heterogeneity, thus, can result
in inaccurate estimation of microbial activity. In most cases this leads to
an overestimation of nutrient removal (up to twice the actual amount) along
a flow path. We conclude that the governing factors for evaluating this are
the residence time of solutes and the Damköhler number (Da) of the
biogeochemical reactions in the domain. We propose a relationship to scale
the impact of spatial heterogeneity on nutrient removal governed by the
log10Da. This relationship may be applied in upscaled descriptions of
microbially mediated nutrient cycling dynamics in the subsurface thereby
resulting in more accurate predictions of, for example, carbon and nitrogen cycling
in groundwater over long periods at the catchment scale.