Deep denitrification: Stream and groundwater biogeochemistry reveal contrasted but connected worlds above and below.

Excess nutrients from agricultural and urban development have created a cascade of ecological crises around the globe. Nutrient pollution has triggered eutrophication in most freshwater and coastal ecosystems, contributing to a loss in biodiversity, harm to human health, and trillions in economic damage every year. Much of the research conducted on nutrient transport and retention has focused on surface environments, which are both easy to access and biologically active. However, surface characteristics of watersheds, such as land use and network configuration, often do not explain the variation in nutrient retention observed in rivers, lakes, and estuaries. Recent research suggests subsurface processes and characteristics may be more important than previously thought in determining watershed-level nutrient fluxes and removal. In a small watershed in western France, we used a multi-tracer approach to compare surface and subsurface nitrate dynamics at commensurate spatiotemporal scales. We combined 3-D hydrological modeling with a rich biogeochemical dataset from 20 wells and 15 stream locations. Water chemistry in the surface and subsurface showed high temporal variability, but groundwater was substantially more spatially variable, attributable to long transport times (10-60 years) and patchy distribution of the iron and sulfur electron donors fueling autotrophic denitrification. Isotopes of nitrate and sulfate revealed fundamentally different processes dominating the surface (heterotrophic denitrification and sulfate reduction) and subsurface (autotrophic denitrification and sulfate production). Agricultural land use was associated with elevated nitrate in surface water, but subsurface nitrate concentration was decoupled from land use. Dissolved silica and sulfate are affordable tracers of residence time and nitrogen removal that are relatively stable in surface and subsurface environments. Together, these findings reveal distinct but adjacent and connected biogeochemical worlds in the surface and subsurface. Characterizing how these worlds are linked and decoupled is critical to meeting water quality targets and addressing water issues in the Anthropocene.

A hillslope-scale aquifer-model to determine past agricultural legacy and future nitrate concentrations in rivers.

The long-term fate of agricultural nitrate depends on rapid subsurface transfer, denitrification and storage in aquifers. Quantifying these processes remains an issue due to time varying subsurface contribution, unknown aquifer storage and heterogeneous denitrification potential. Here, we develop a parsimonious modelling approach that uses long-term discharge and river nitrate concentration time-series combined with groundwater age data determined from chlorofluorocarbons in springs and boreholes. To leverage their informational content, we use a Boussinesq-type equivalent hillslope model to capture the dynamics of aquifer flows and evolving surface and subsurface contribution to rivers. Nitrate transport was modelled with a depth-resolved high-order finite-difference method and denitrification by a first-order law. We applied the method to three heavily nitrate loaded catchments of a crystalline temperate region of France (Brittany). We found that mean water transit time ranged 10-32 years and Damköhler ratio (transit time/denitrification time) ranged 0.12-0.55, leading to limited denitrification in the aquifer (10-20%). The long-term trajectory of nitrate concentration in rivers appears determined by flows stratification in the aquifer. The results suggest that autotrophic denitrification is controlled by the accessibility of reduced minerals which occurs at the base of the aquifer where flows decrease. One interpretation is that denitrification might be an interfacial process in zones that are weathered enough to transmit flows and not too weathered to have remaining accessible reduced minerals. Consequently, denitrification would not be controlled by the total aquifer volume and related mean transit time but by the proximity of the active weathered interface with the water table. This should be confirmed by complementary studies to which the developed methodology might be further deployed.

What do we need to predict groundwater nitrate recovery trajectories?

Nitrate contamination affects many of the Earth's aquifers and surface waters. Large-scale predictions of groundwater nitrate trends normally require the characterization of multiple anthropic and natural factors. To assess different approaches for upscaling estimates of nitrate recovery, we tested the influence of hydrological, historical, and biological factors on predictions of future nitrate concentration in aquifers. We tested the factors with a rich hydrogeological dataset from a fractured bedrock catchment in western France (Brittany). A sensitivity analysis performed on a calibrated model of groundwater flow, denitrification, and nitrogen inputs revealed that trends in nitrate concentration can effectively be approximated with a limited number of key parameters. The total mass of nitrate that entered the aquifer since the beginning of the industrial period needs to be characterized, but the shape of the historical nitrogen input time series can be largely simplified without substantially altering the predictions. Aquifer flow and transport processes can be represented by the mean and standard deviation of the residence time distribution, offering a tractable tool to make reasonable predictions at watershed to regional scales. Apparent sensitivity to denitrification rate was primarily attributable to time lags in oxygen depletion, meaning that denitrification can be simplified to an on/off process, defined only by the time needed for nitrate to reach the hypoxic reactive layer. Obtaining these key parameters at large scales is still challenging with currently available information, but the results are promising regarding our future ability to predict nitrate concentration with integrated monitoring and modeling approaches.