Water storage, mixing, and fluxes in tile-drained agricultural fields inferred from stable water isotopes

Abstract

Quantifying hydrological processes that control the upper critical zone water balance and contaminant transport in drained landscapes is needed, especially as precipitation patterns driving water balance dynamics continue to shift due to climate change. Here, hydrometric data are integrated with stable isotope signatures to quantify water storage, mixing, and fluxes to subsurface tile drainage at an agricultural field located in Indiana, USA. Over a 2-yr period, precipitation, soil water sampled with suction lysimeters (10–80 cm depth), groundwater (below tile depth; >1 m), and subsurface tile discharge were sampled 97 times. Results showed that isotopic variability in near-surface soil water (10–20 cm) reflected the seasonality of the precipitation input signal, while groundwater values were relatively consistent indicating that water stored below tile drain depth was recharged during winter. Soil water between 20 and 80 cm depth was a mixture of near-surface water and groundwater that varied seasonally depending upon groundwater hydrodynamics. Mean transit time of water ranged from 12 to 20 d for 10-cm soil water to 225–334 d for groundwater, with tile drainage exhibiting a mean transit time of 245 d. Both two- and three-component hydrograph separation indicated that groundwater was the primary source of water to the tile drain followed by soil water. Tile drain hydrograph response (i.e., celerity) was largely controlled by antecedent wetness. Comparison of tile drain celerities and velocities revealed however varying mechanisms controlling hydrograph response across a range of environmental conditions. Data sets of both water and tracer flux were, thus, useful to track the spatiotemporal variability of water fluxes within and from the critical zone. Such data provide valuable information to improve the representation of critical zone processes in these landscapes within spatially distributed hydrological models.