Impacts of Subsurface Tile Drainage on Age—Concentration Dynamics of Inorganic Nitrogen in Soil

Abstract

We explore the impacts of tile drains in agricultural fields on the coupled age and concentration dynamics of nitrate, immobile ammonium, mobile ammonia and ammonium, and nonreactive tracers such as chloride. We implement two mobile interacting pore domains to capture matrix and preferential flow paths in a coupled ecohydrology and biogeochemistry model, Dhara. We apply this model to an agricultural farm that utilizes a corn-soybean rotation in the Midwestern United States located in the Intensively Managed Landscapes Critical Zone Observatory. In general, we observe both low concentration and age of nitrate in the areas that are classified as topographic depressions even with the presence of tile drains. Also, an increase in the age of mobile ammonia/ammonium is observed after installing tile drains. This is in contrast to the cases for nitrate, immobile ammonium, and nonreactive tracer. These results arise because the depletion of mobile ammonia/ammonium due to tile drainage causes a high mobility flux from immobile ammonium to mobile ammonia/ammonium, which also carries a considerable amount of relatively old age of nitrogen from immobile ammonium to mobile ammonia/ammonium. These results illustrate how storm event scale dynamics impact spatial heterogeneity and temporal variability of the efflux, which helps in disentangling the complexity of nitrogen dynamics in the soil. This understanding can contribute to precision agriculture for nitrogen applications to reduce environmental impacts.Plain Language Summary Age-concentration dynamics for inorganic nitrogen in the soil characterizes for how long and how much nitrogen is present in the soil and how these vary in space and time. This paper shows how the presence of subsurface tile drains, a dominant hydrologic control in the midwestern United States and many other parts of the world, structures this variability. It also argues that using traditional tracer methods, such as chloride and bromide, does not capture the longer residence of nitrogen in the soil due to its complex reactive nature in the soil. By understanding what controls these variabilities, we can develop new methods for reducing the application of fertilizers.

Antecedent conditions control thresholds of tile-runoff generation and nitrogen export in intensively managed landscapes

“…To supplement field observations with a mechanistic simulation, we used the coupled surfacesubsurface flow and soil-vegetation-atmosphere interaction model Dhara (Le and Kumar, 2017;Woo and Kumar, 2019) to simulate the hydrologic and biogeochemical dynamics of a parcel of tile-drained land. The model is used here as a heuristic, providing a basis for interpretation of the processes that are likely to underlie our field observations.…”

“…However, tile flow and nitrate loads captured the patterns of the observed data well overall, providing confidence in the use of the simulation results for process investigation. Refer to Woo and Kumar (2019) for a detailed description of the parameters and equations governing the model. A schematic diagram of Dhara is provided in Supplemental Information (Figure S1).…”

“…A schematic diagram of Dhara is provided in Supplemental Information (Figure S1). A small number of alterations were made to the model application of Woo and Kumar (2019) to address the goals of this study. To simulate the groundwater table, the depth of the model was increased to 3.5 m, with the tile located at 1.2 m below the ground surface.…”

“…Finally, we expect antecedent wetness to control within-event nitrate c-Q relationships because threshold processes associated with runoff generation will be manifested in dynamic hydrologic connectivity in the landscape. To test these expectations, we use a combination of empirical data from a tile-drained field in the IML-Critical Zone Observatory and field-scale simulations of coupled water and nitrogen cycles using the Dhara model (Le and Kumar, 2017;Woo and Kumar, 2019).…”

Development and Evaluation of an ODE Representation of 3D Subsurface Tile Drainage Flow Using the HLM Flood Forecasting System

“…Finally, we fix the hillslope depth and gradient/slope and change the tile depth. Our approach is in contrast to developing more sophisticated point‐scale constitutive relationships to describe flow into the drainage tiles, which would require solving partial differential equations, a bigger computational challenge, as in Woo and Kumar 2019, wherein the authors incorporate macropores and a network of drainage tiles to explore their effects on subsurface chemical concentration dynamics.…”