Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape

Abstract

Abstract. Surface waters are under pressure from diffuse pollution from agricultural activities, and groundwater is known to be a connection between the agricultural fields and streams. This paper is one of the first to calculate long-term in-stream concentrations of tritium, chloride, and nitrate using dynamic groundwater travel time distributions (TTDs) derived from a distributed, transient, 3D groundwater flow model using forward particle tracking. We tested our approach in the Springendalse Beek catchment, a lowland stream in the east of the Netherlands, for which we collected a long time series of chloride and nitrate concentrations (1969–2018). The Netherlands experienced a sharp decrease in concentrations of solutes leaching to groundwater in the 1980s due to legislations on the application of nitrogen to agricultural fields. Stream measurements of chloride and nitrate showed that the corresponding trend reversal in the groundwater-fed stream occurred after a time lag of 5–10 years. By combining calculated TTDs with the known history of nitrogen and chloride inputs, we found that the variable contribution of different groundwater flow paths to stream water quality reasonably explained the majority of long-term and seasonal variation in the measured stream nitrate concentrations. However, combining only TTDs and inputs underestimated the time lag between the peak in nitrogen input and the following trend reversal of nitrate in the stream. This feature was further investigated through an exploration of the model behaviour under different scenarios. A time lag of several years, and up to decades, can occur due to (1) a thick unsaturated zone adding a certain travel time, (2) persistent organic matter with a slow release of N in the unsaturated zone, (3) a long mean travel time (MTT) compared to the rate of the reduction in nitrogen application, (4) areas with a high application of nitrogen (agricultural fields) being located further away from the stream or drainage network, or (5) a higher presence of nitrate attenuating processes close to the stream or drainage network compared to the rest of the catchment. By making the connection between dynamic groundwater travel time distributions and in-stream concentration measurements, we provide a method for validating the travel time approach and make the step towards application in water quality modelling and management.

Transit Time Estimation in Catchments: Recent Developments and Future Directions

“…1-D advection and dispersion was similarly used by to relate the shape of a SAS function to the Pe number. Other examples of age distributions and SAS functions computed from process-based models and tracer data include: stationary two-dimensional groundwater flow problems (van der Velde et al, 2012), non-stationary 1-D and 2-D flow through unsaturated porous media (Asadollahi et al, 2020;Pangle et al, 2017;Sprenger et al, 2018), non-stationary groundwater flow (Kaandorp et al, 2021), and fully coupled catchment-scale flow and transport models Kuppel et al, 2018;Remondi et al, 2018;Wilusz et al, 2020;Yang et al, 2018).…”

“…1‐D advection and dispersion was similarly used by Benettin, Rinaldo, and Botter (2013) to relate the shape of a SAS function to the Pe number. Other examples of age distributions and SAS functions computed from process‐based models and tracer data include: stationary two‐dimensional groundwater flow problems (van der Velde et al., 2012), non‐stationary 1‐D and 2‐D flow through unsaturated porous media (Asadollahi et al., 2020; Pangle et al., 2017; Sprenger et al., 2018), non‐stationary groundwater flow (Kaandorp et al., 2021), and fully coupled catchment‐scale flow and transport models (Kim et al., 2022; Kuppel et al., 2018; Remondi et al., 2018; Smith, Tetzlaff, & Soulsby, 2020; Wilusz et al., 2020; Yang et al., 2018). Recently, Kim and Harman (2022) used hydraulic groundwater theory to derive analytical expressions for the TTD and SAS functions of hillslope subsurface flow under steady recharge.…”

Characterizing Catchment‐Scale Nitrogen Legacies and Constraining Their Uncertainties

“…Regarding the buildup of the subsurface N store, three interacting parameters mostly contribute to the uncertainty, namely the mean travel time in the subsurface, which is also the most influential factor, and the two denitrification rate constants. In this regard, tracer studies, and in particular the combination of tritium concentration and helium isotope measurements, can help to characterize the travel time (Sültenfuß et al, 2009), as well as the modeling of conservative solutes, such as chloride, in combination with nitrate (Kaandorp et al, 2021). In addition, Eschenbach et al (2018) propose a method to characterize denitrification in the groundwater, based on the measurement of the N 2 /Ar ratio.…”

Advancing Watershed Legacy Nitrogen Modeling to Improve Global Water Quality

“…This would improve estimates linking hydrological N transport and N travel times downstream. Promising dynamic approaches have been developed in recent years (e.g., refs , , and ) yet need to evolve and expand. In addition, appropriate groundwater residence times (e.g., more than 5 years) and transport to the nearest surface water and storage of nitrate (and other forms of N) (Figure ) are needed in watershed models used for long-term legacy N storage estimates.…”