1D. N. Moriasi, P.H. Gowda, J. G. Arnold, D.J. Mulla, S. Ale, J.L.

Steiner, and M. D. Tomer

NEW SWAT TILE DRAIN EQUATIONS: MODIFICATIONS, CALIBRATION, VALIDATION, AND APPLICATION

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Outline Introduction

Tile drainage New tile equations

Modifications What and why

Calibration and validation (1983-1996) Results

Application Sensitivity analyses (1983 -1996)

drainage systems and N application rates on tile drain NO3-N losses

Long-term (1915 – 1996) effects of precipitation, drainage design, and N application rates on tile drain NO3-N losses

Conclusions

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Subsurface Tile Drainage

agricultural practice

↑nitrate-nitrogen (NO3-N) to surface waters

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Alternative method – depth, size, and space 3-step approach for computation of drainage flux (q)

Hooghoudt (1940) steady-state equation wtd below surface

Kirkham (1957) equationponded depths

If q > design drainage capacity (DC), q = DC

Hooghoudt and Kirkham Equations

Skaggs, 1978; Moriasi et al., 2007, 2012, 2013a,b

Modifications Parameters g and HDRAIN – 2 new input parameters

Why? Based on preliminary sensitivity analysis of SDRAIN on flow. Initially f(space, size, and height of tile above impervious layer

Modified soil water content method used to estimate retention parameter (S) – over-prediction of surface runoff ∗ ∗

.

Smax is the maximum value the retention parameter can achieve on any given day (mm) SW is the soil water content of the entire profile excluding the amount of water held in

the profile at wilting point (mm H2O) w1 and w2 are shape coefficients SAfctr is the retention parameter adjustment factor (≥1) for a given HRU, which is a

function of the effective soil profile drainable porosity (Sands et al., 2009), slope, drainage system design

CN procedure (SCS, 1972) .

.

Neitsch et al., 2011; Moriasi et al., 2013a,b

Curve Number Method: CN2

Soil’s permeability Land use Antecedent soil water conditions Slope (5%) Not a function of tile drainage

Neitsch et al. (2011)

CoverHydrologic Soil Group

Land Use Treatment or practiceHydrologic condition A B C D

Fallow Bare soil - - - - 77 86 91 94Crop residue cover Poor 76 85 90 93

Good 74 83 88 90Row crops Straight row Poor 72 81 88 91

Good 67 78 85 89Straight row w/ residue Poor 71 80 87 90

Good 64 75 82 85Contoured Poor 70 79 84 88

Good 65 75 82 86Contoured w/ residue Poor 69 78 83 87

Good 64 74 81 85Contoured & terraced Poor 66 74 80 82

Good 62 71 78 81Contoured & terraced w/ residue Poor 65 73 79 81

Good 61 70 77 80Small grains Straight row Poor 65 76 84 88

Good 63 75 83 87Straight row w/ residue Poor 64 75 83 86

Table 2:1-1: Runoff curve numbers for cultivated agricultural lands (from SCS Engineering Division, 1986; Neitsch et al. (2009 )

Crop residue cover applies only if residue is on at least 5% of the surface throughout the year.

Impact of Tile Drainage and Slope on Hydrology

Drainage increases storage capacity in the soil - “sponge effect”

removal of excess water improved soil structure

Lower slopes (0.1% for Waseca)

increase infiltration, reduce surface runoff

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Study Area and Data Three continuous corn plots located

in the University of Minnesota’s Agricultural Experiment Station near Waseca, southern Minnesota

These plots were designed to simulate a tile drain spacing of 27 m. Tile drains were installed at a depth of 1.2 m; with a gradient of 0.1%. Tile drain diameter of 100 mm.

Plots were tilled using moldboard plow.

Field measurements of soil and crop data were made as a part of a tile drainage study

Tile drain flows were measured daily and summed to calculate monthly and yearly values – April – August (1983 -1996).

Weather data recorded at a weather station located 0.5 km from the experimental plots was used in the simulation

Davis et al. (2000)

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Results: Budget and Statistical Measures

Table 1. Observed/reported and simulated average annual water and nitrogen budgets. *Moorman et al. (1999); **Nangia et al. (2010a); ***Meisinger and Randall (1991). ET is evapotranspiration

Water Budget

Obs./Literature Revised SWAT Nitrogen Budget

Annual Average Obs./Literature Revised SWAT

Depth (cm)

Percent of Precipitation

(%)

Depth (cm)

Percent of Precipitation

(%)

Nitrogen (kg ha-1)

% of Applied Nitrogen

Nitrogen (kg ha-1)

% of Applied Nitrogen

Precipitation 52.8 100 52.8 100 Applied Fertilizer 200 100 200.0 100

ET 64 – 70* 37.1 70 Total Crop Uptake 143 73 140.6 70

Tile Drainage 20.7 39 20.5 39 Drainage 33.5 17 34.0 17

Runoff 0.4 5** 0.4 1Denitrification 10 - 25*** 26.5 13

ComponentCalibration Validation

NSE PBIAS (%) RSR RMSE NSE PBIAS (%) RSR RMSE Tile flow 0.84 -1 0.39 2.4 mm 0.76 3 0.49 2.1 mmNO3-N Losses in tile flow 0.74 -4 0.51 5.7 (Kg ha-1) 0.66 1 0.58 5.5 (Kg ha-1)

Corn yield:8.6 metric tons ha-1

Table 2. Monthly calibration and validation statistics. NSE is the Nash-Sutcliffe efficiency, PBIAS is percent bias, RMSE is root mean square error, and RSR is the ratio of RMSE and standard deviation of the observed data.

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Methods – Sensitivity and Long-term

Sensitivity Analyses (1983-1996) on flow and NO3-N losses

Depth (DDRAIN), spacing (SDRAIN), N application rates (Davis et al., 2000) Calibrated and validated Revised SWAT model Calibrated and default values were baseline and kept constant while during

parameter being investigated was varied

Long-term average effects (1915 – 1996) on flow and NO3-N losses

Effect of precipitation linear regression models were developed using the predicted NO3-N losses data (April-August)

for 82 years (1915-1996) for each N application rate

Probability of exceedance of target flow and NO3-N losses in any given year P is defined as the probability that a simulated value of equal or greater magnitude will occur in

any single year predicted tile flow and NO3-N losses were ranked in decreasing order

P is prob. of exceedance; N is rank; n = total number of annual predicted values

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Results: Sensitivity Analyses DDRAIN (3 depths: 0.9, 1.2, 1.5 m)

NO3-N losses decreased by 14% (from 34.0 to 29.4 kg ha-1) when DDRAIN was decreased by 40% (1.5 to 0.90 m).

Tile flow decreased by 8% (from 207 to 191 mm) over the same DDRAIN range.

SDRAIN (6 spacings:15, 27, 40, 80, 100, 200 m) NO3-N losses decreased by 16% (from 33.8 to 28.4 kg ha-1) when

SDRAIN was increased by 122% (27 to 60 m). Tile flow decreased by 11% (from 205 to 182 mm) over the same

SDRAIN range.

N Application Rates (6 rates: 100, 125, 150, 175, 200, 225 kg ha-1) NO3-N losses decreased by 67 % (from 33.8 to 11.1 kg ha-1) when

application rate was decreased by 50% (from 200 to 100 kg ha-1).

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Results: Effect of Precipitation on Tile drain NO3-N Losses

Moriasi et al. (2013b)

Tile flow

Tile flow model† Slope (m) Intercept (b) R2

0.64 -12.67 0.69

Nitrate N loss model‡ N application rate (kg ha-1) Slope (m) Intercept (b) R2

100 0.30 -3.15 0.21125 0.56 -7.58 0.35150 0.83 -12.71 0.43175 1.10 -17.73 0.46200 1.40 -23.15 0.48225 1.64 -27.66 0.49

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Results: Exceedance Probability - Depth

• P is defined as the probability that a predicted value of equal or greater magnitude will occur in any single year

0.45 (45%) at 1.2 or 1.5 m

0.32 (32%) at 0.9 m

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0.48 (48%) at 15 m

0.14(14%) at 200 m

Results: Exceedance Probability -Spacing

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0.44 (44%) at 200 kg ha-1

0.05 (5%) at 125 kg ha-1

Results: Exceedance Probability –N Application Rate

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Modifications made to the new Hooghoudt and Kirkham equations –were successfully made New input parameters – Kirkham g and HDRAIN based on preliminary sensitivity

analysis of SDRAIN on tile flow Incorporated the retention parameter adjustment factor (SAfctr ≥1) – improve

simulation surface runoff and tile flow – useful for both tile drain methods in SWAT

Calibration and validation important to ensure that budgets of simulated components and yields are validated to

ensure right statistical performance values for the right reasons

Sensitivity analysis of physically based parameters and measurable inputs on outputs of processes of

interest is essential Helps validate model algorithms or recommend modifications for pertinent processes

Shallower depth yields relatively larger reductions in tile flow and corresponding NO3-N losses than with wider spacing

However, reduction of N application rate has a much more impact on reduction of NO3-N loses and is more practical From cost-reduction perspective Shallower depth and/or wider spacing lead to reduction in crop yields due to plant stress caused by excess

water in the root zone Trafficability issues

Conclusions

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Long-term impacts Precipitation has a significant impact on NO3-N losses Generated probability of exceedance curves can help extension specialists determine

reasonable drainage systems and N application rates depending on their defined target NO-N losses.

Results show potential of the new tile drainage equations in SWAT to: determine optimum tile drain depth and spacing combinations that reduce NO3-N

losses while optimizing the crop yield Simulate impacts of N-application rates and the climate on NO3-N losses

Conclusions Cont’d

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Thank You!

Questions?E-mail: daniel.moriasi@ars.usda.gov

ReferencesMoriasi, D.N., C.G. Rossi, J.G. Arnold, and M.D. Tomer. 2012. Evaluating hydrology of the Soil and Water Assessment Tool (SWAT) with new tile drain equations. Journal of the Soil and Water Conservation 67(6):513-524.

Moriasi, D.N., P. H. Gowda, J.G. Arnold, D.J. Mulla, S. Ale, J.L. Steiner, and M.D. Tomer. 2013a. Evaluation of the Hooghoudt and Kirkham tile drain equations in the Soil and Water Assessment Tool to simulate tile flow and nitrate-nitrogen. J. Environ. Qual. 42(6):1699–1710.

Moriasi, D.N., P. H. Gowda, J.G. Arnold, D.J. Mulla, S. Ale, and J.L. Steiner. 2013b. Modeling the impact of nitrogen fertilizer application and tile drain configuration on nitrate leaching using SWAT. Agricultural Water Management 130:36– 43.

Neitsch, S.L., Arnold,J.G. Kiniry, J.R. and Williams, J.R., 2011. Soil and Water Assessment Tool theoretical documentation version 2009. Texas Water Resources Institute Technical Report No. 406. College Station, Texas

Skaggs, R.W. 1978. A water management model for shallow water table soils. Report No. 134. Chapel Hill, NC: Water Resources Research Institute of the University of North Carolina.