What do we need to predict groundwater nitrate recovery trajectories?

Highlights

• Nitrate recovery trajectories were predicted based on a few key-parameters.

• Two age tracers are necessary to predict groundwater nitrate concentration.

• The stratification of denitrification controls the nitrate dynamic in the aquifer.

• Uncertainty about past nitrogen inputs may not alter the predictions.

Abstract

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.

Introduction

Human activity has more than doubled reactive nitrogen delivery to Earth's ecosystems, creating eutrophic (over-fertilized) conditions in aquatic and coastal environments around the world (Galloway et al., 2008; Kronvang et al., 2005). In the past several decades, widespread efforts have been made to reduce human nutrient loading to protect freshwater resources and ecosystems (Abbott et al., 2018; Boers, 1996; Kronvang et al., 2008; Kronvang et al., 2005; Steffen et al., 2015). However, natural systems often respond to changes in nutrient inputs with a time lag, making recovery trajectories difficult to predict. This complicates the evaluation of mitigation strategies and can even imperil public and political support for investment in mitigation (Hamilton, 2012; Kronvang et al., 2005; Meals et al., 2010; Van Meter and Basu, 2015; Van Meter and Basu, 2017). In many catchments, long time lags mean that the agricultural policies we choose today will affect nitrogen concentration in surface and groundwater for decades to come (Ehrhardt et al., 2019; Thomas and Abbott, 2018; Van Meter and Basu, 2015). Thus, improving predictions of nutrient recovery timelines following different agricultural scenarios is an important ecological and socioeconomic goal (Kolbe et al., 2019; Le Moal et al., 2019; Marcais et al., 2018; Minaudo et al., 2019).

Nitrogen can be stored for several years to several decades in different compartments of surface and subsurface ecosystems, leading to what is called a nitrogen legacy (Ehrhardt et al., 2019; Hrachowitz et al., 2015; Van Meter et al., 2017; Van Meter et al., 2016). Apart from the soil (Sebilo et al., 2013), one of the main drivers of nitrogen legacy is groundwater. As aquifers contain two orders of magnitude more water than all rivers and lakes (Abbott et al., 2019), groundwater nitrogen can be stored for decades before reaching the surface (Fenton et al., 2011; Wendland et al., 2002). However, because the major form of groundwater nitrogen is nitrate (NO₃⁻), microbial activity during groundwater storage and transport can reduce nitrogen stocks via anaerobic metabolism (denitrification), which eventually transforms NO₃⁻ into N₂ gas (Green et al., 2016; Kolbe et al., 2019; Korom, 1992). As a result, groundwater circulation exerts a dual control on NO₃⁻ pollution: it creates a delay or time lag between inputs and outputs, and it can reduce the NO₃⁻ stock in the aquifer system.

Predicting the recovery trajectory of NO₃⁻ for a given aquifer requires information about the past and future nitrogen inputs, the water residence times, and the rates of nitrate removal in different compartments of the aquifer (Kolbe et al., 2019; Małoszewski and Zuber, 1982; Van Meter and Basu, 2015). Each of these three functions is defined by a set of parameters that need to be quantified. However, constraining these functions is challenging because of a lack of data at appropriate spatiotemporal scales (Abbott et al., 2016; Frei et al., 2020; McDonnell et al., 2007). The information is often only available in few study sites where measurements and modeling efforts have been coupled (Böhlke and Denver, 1995; Green et al., 2016; Kolbe et al., 2019; Paradis et al., 2017; Singleton et al., 2007; Tesoriero and Puckett, 2011). Because mitigation strategies are mainly decided and implemented at large scales such as regions or nations (Kronvang et al., 2005; US EPA, 2008), large-scale predictions of removal and storage capacities are needed to set realistic expectations of mitigation actions and recovery time frames and to predict ecosystem vulnerability to nutrient loading (Abbott et al., 2018; Pinay et al., 2015).

In this context, we tested the sensitivity of NO₃⁻ recovery predictions based on simple but robust hydrological parameters in a well-studied unconfined fractured bedrock aquifer. We performed a sensitivity analysis to identify the key parameters, including both anthropic and natural drivers that need to be constrained to predict the future nitrate trajectories in the aquifer. Our immediate goals were to 1. forecast groundwater nitrate trajectories for different loading scenarios, 2. determine the dominant controls on groundwater nitrate concentration, and 3. assess how much hydrological detail is needed to make accurate predictions of large-scale biogeochemical patterns in space and time.