Evaluation of the Hooghoudt and Kirkham Tile Drain Equations in the Soil and Water Assessment Tool to Simulate Tile Flow and Nitrate-Nitrogen

Abstract

Subsurface tile drains in agricultural systems of the midwestern United States are a major contributor of nitrate-N (NO-N) loadings to hypoxic conditions in the Gulf of Mexico. Hydrologic and water quality models, such as the Soil and Water Assessment Tool, are widely used to simulate tile drainage systems. The Hooghoudt and Kirkham tile drain equations in the Soil and Water Assessment Tool have not been rigorously tested for predicting tile flow and the corresponding NO-N losses. In this study, long-term (1983-1996) monitoring plot data from southern Minnesota were used to evaluate the SWAT version 2009 revision 531 (hereafter referred to as SWAT) model for accurately estimating subsurface tile drain flows and associated NO-N losses. A retention parameter adjustment factor was incorporated to account for the effects of tile drainage and slope changes on the computation of surface runoff using the curve number method (hereafter referred to as Revised SWAT). The SWAT and Revised SWAT models were calibrated and validated for tile flow and associated NO-N losses. Results indicated that, on average, Revised SWAT predicted monthly tile flow and associated NO-N losses better than SWAT by 48 and 28%, respectively. For the calibration period, the Revised SWAT model simulated tile flow and NO-N losses within 4 and 1% of the observed data, respectively. For the validation period, it simulated tile flow and NO-N losses within 8 and 2%, respectively, of the observed values. Therefore, the Revised SWAT model is expected to provide more accurate simulation of the effectiveness of tile drainage and NO-N management practices.

Comparison of performance of tile drainage routines in SWAT 2009 and 2012 in an extensively tile-drained watershed in the Midwest

“…When the water table completely fills the surface and ponded water remains at the surface for long periods of time, drainage flux is computed using the Kirkham equation (Moriasi et al, 2012(Moriasi et al, , 2013:…”

“…where q is the drainage flux (mm h −1 ) and DRAIN_CO is the drainage coefficient (mm day −1 ) (Moriasi et al, 2012(Moriasi et al, , 2013. These new tile drainage routines have been used to model tile flow at watershed scale since SWAT2009 (Boles et al, 2015).…”

“…Parameters related to surface runoff, tile drainage, evapotranspiration (ET), sediment losses, and nitrate loss processes were selected during model calibration ( Table 2). Ranges of parameters (Table 3) were determined based on the previous DRAINMOD studies in the LVR watershed (Singh et al, 2001a) and several tile drain studies in Iowa (Moriasi et al, 2012(Moriasi et al, , 2013Schilling and Helmers, 2008;Singh et al, 2007Singh et al, , 2006Singh and Helmers, 2008) and Indiana (Boles et al, 2015). The uniform random sampling method was used to generate 2000 uniformly distributed samples for the above parameters.…”

“…Water table was lower when respiratory activity was highest in summer (Muhr et al, 2011), which may be lower than the depth of subsurface tiles during long dry summer periods. Water table depth calculation based on change in the soil water for the whole soil profile tended to overestimate the distance between water table and the soil surface when long-term simulations were performed, most commonly in cases where days without rainfall dominated (Moriasi et al, 2013). Thus, Rev.…”

“…3b) (DDRAIN, 1075 mm). For long-term water table depth simulation (19 years in this study), the computed water table depth may gradually drop as profile soil water decreases due to periods of higher ET, which makes it harder for the water table to rise to the surface after rain events (Moriasi et al, 2013). When water storage is higher than the height of the surface storage threshold (20 % of the static maximum depressional storage (SSTMAXD), 0.24 mm in this study) and water table is near the bottom of the soil surface, the Kirkham equation is used to calculate drainage flux (Boles et al, 2015).…”