Modeling Nitrogen Transport and Transformations in High Water Table Soils

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ABSTRACT

YOUSSEF, MOHAMED ABDELMONEIM. Modeling Nitrogen Transport and Transformations in High Water Table Soils. (Under the direction of R. Wayne Skaggs).

Development of management practices that reduce nitrogen (N) losses from agricultural lands has been the focus of research over many years. Development and testing of such practices is a complex task since it requires understanding of N dynamics in the soil-water-plant system, which is regulated by a large number of interacting physical, chemical, and biological processes. Nitrogen models are useful tools for developing and evaluating management practices for sustainable agriculture. The model, DRAINMOD-N was originally developed to simulate N dynamics in artificially drained soils. However, the model was based on a simplified N cycle, which restricted its applicability. A new version of DRAINMOD-N, referred to as DRAINMOD-N II, was developed and field-tested in this study.

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DRAINMOD-N II was tested with a six-year data set from the North Carolina Lower Coastal Plain. The experimental site consists of eight 1.7-hectare instrumented, subsurface drained plots. The site was planted to a corn-wheat-soybean rotation. Water table depth (WTD) midway between the drains, subsurface drainage flow rates, and meteorological data were automatically measured and recorded. Flow proportional drainage water quality samples were collected and analyzed to determine N concentrations and loads.

Results of the simulations showed good agreement between predicted and observed WTD. On average, predicted WTD was within 11.8 to 13.9 cm of observed values. The average coefficient of determination (R2) for WTD was in the range of 0.71 to 0.77.

There was also good agreement between predicted and observed subsurface drainage rates. On average, predicting annual subsurface drainage was within 5.7-12.1% of observed values. The average R2 values for predicted versus observed subsurface drainage ranged from 0.65 to 0.73.

The DRAINMOD-N II simulations showed good agreement between predicted annual nitrate-nitrogen (NO3-N) drainage losses and observed values. On average,

predicted annual NO3-N leaching losses were in the range of 19.9-46.0%. The high errors

in predicting annual NO3-N leaching losses were mostly induced by errors in predicting

WTD and drainage rates.

The model did an excellent job in predicting cumulative drainage and NO3-N

leaching losses over the whole simulated period. Cumulative drainage and NO3-N

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values, respectively. In spite of relatively large discrepancies on an annual basis, in some years, errors in predicting cumulative NO3-N losses over the six-year period were

remarkably small.

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MODELING NITROGEN TRANSPORT AND

TRANSFORMATIONS IN HIGH WATER TABLE SOILS

by

MOHAMED A. YOUSSEF

A dissertation submitted to the Graduate Faculty North Carolina State University

in partial fulfillment of the requirements of the Degree of

Doctor of Philosophy

BIOLOGICAL AND AGRICULTURAL ENGINEERING DEPARTMENT Raleigh

2003

APPROVED BY:

_____________________________ ______________________________ Dr. R. Wayne Skaggs Dr. J. Wendell Gilliam

Chair of Advisory Committee Minor Representative

_____________________________ ______________________________

Dr. John E. Parsons Dr. George M. Chescheir

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DEDICATION

To my LORD.

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BIOGRAPHY

Mohamed Abdel Moneim Youssef was born October 25, 1966 in Demyat, Egypt.

He lived and attended school in Demyat unit 1985 when he moved to Mansoura, Egypt.

He received a Bachelor of Science in Civil Engineering from Mansoura

University in May 1990. He worked as a field engineer for the Egyptian Public Authority

for Drainage Projects until 1995 when he moved to work as a research engineer for the

Drainage Research Institute. He received a two-year Diploma in Computer Science and

Information form Cairo University in May 1994. He received a Master of Science in

Civil Engineering from Mansoura University in August 1997.

He came to the United States of America in August 1997 to begin studying for the

Doctor of Philosophy in Biological and Agricultural Engineering at North Carolina State

University.

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TABLE OF CONTENTS (continued)

CHAPTER 3: FIELD TESTING OF DRAINMOD 5.1/DRAINMOD-N II

MODELS FOR NORTH CAROLINA SOILS 58

ABSTRACT 58 INTRODUCTION 60

MATERIALS AND METHODS 62

The Field Study 62

Soil Description 63

The Drainage System 63

Meteorological Data 65

Cropping and Fertilization 65

Water Table Management 67

Model Description 68

The Nitrogen Cycle 68

The Carbon Cycle 68

Modes of Operations 69

Nitrogen Transport 69

The Effect of Environmental Factors on C and N Transformations 70

Fertilizer Application 70

Application of Animal Waste and Crop Residues 70

Organic C Decomposition and N Mineralization/Immobilization 71

Nitrification 71

Denitrification 71

Plant Uptake 71

Modeling Temporal Change in Soil pH and NH3 Volatilization 72

Model Parameterization 72

Hydrologic Input Parameters 73

Nitrogen Input Parameters 76

RESULTS AND DISCUSSIONS 94

HYDROLOGY 95 NITROGEN 100

SUMMARY AND CONCLUSION 109

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TABLE OF CONTENTS (continued)

APPENDIX A: DRAINMOD INPUT PARAMETERS 223

WEATHER PARAMETERS 224

DRAINAGE SYSTEM PARAMETERS 225

SOIL PARAMETERS 225

Lateral Saturated Hydraulic Conductivity 225

Soil Water Characteristic 226

Water Table-Volume Drained-Upward Flux Relationships 227

Infiltration 229

CROP PARAMETERS 230

Yield-Planting 230

Yield-Excess Water Stress 231

Yield-Deficit Water Stress 232

Effective Rooting Depths 233

SEW 235 Trafficability 236

SOIL TEMPERATURE PARAMETERS 237

APPENDIX B: INPUT FILES OF DRAINMOD-N II SIMULATIONS OF

THE TIDEWATER RESEARCH STATION AGRICULTURAL SITE 238

DRAINMOD-N II INPUT FILE FOR FIELD 2 239

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LIST OF TABLES

CHAPTER 2:

Table 2.1 Retardation factors and effective dispersion coefficients for three nitrogen species.

47

CHAPTER 3:

Table 3.1 Cropping sequence, tillage practices, and liming and N fertilization rates and timing for 1992 to 1997.

112

Table 3.2 DRAINMOD-N II settings used in simulating N dynamics in the

TRS agricultural site. 113

Table 3.3 Soil properties of experimental fields 2-5 of the TRS site. 113 Table 3.4 Measured crop yield (on dry weight basis) and grain N content for

wheat, soybean, and corn in 1992-1998. 114

Table 3.5 Values for Harvest Indices of wheat, soybean, and corn reported in the literature.

115

Table 3.6 Values for root-to-shoot ratio of wheat, soybean, and corn

reported in the literature. 116

Table 3.7 Chemical composition of wheat, soybean, and corn residues reported in the literature.

117

Table 3.8 Tabulated function of N plant uptake used for wheat, soybean, and corn.

118

Table 3.9 Urease activity values measured in six published studies. 119 Table 3.10 Values of Michaelis constant of urea hydrolysis reaction reported

in the literature.

119

Table 3.11 Temperature response function parameters for urea hydrolysis. 120 Table 3.12 Summary of published studies on pH effect on urea hydrolysis. 120 Table 3.13 Summary of published studies on soil water effect on urea

hydrolysis.

120

Table 3.14 Published values of maximum nitrification rates measured at 1/3

bar moisture tension. 121

Table 3.15 Temperature response function parameters for nitrification. 121 Table 3.16 Summary of published studies on soil water effect on nitrification. 122 Table 3.17 Summary of published studies on pH effect on nitrification. 122 Table 3.18 Parameters of DRAINMOD-N II response function for nitrification

inhibitors.

122

Table 3.19 Michaelis-Menten parameters for denitirification reported in five published studies.

123

Table 3.20 Published values of the denitrification threshold WFPS as related to soil texture.

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LIST OF TABLES (continued)

Table 3.21 C:N ratios and initial percentages of total OC of the active, slow,

and passive SOM pools. 123

Table 3.22 Potential rate of decomposition for all OM pools considered in

DRAINMOD-N II.

123

Table 3.23 Statistical comparison between observed and predicted water table

depth for experimental fields 2-5 of the TRS site in 1992-1997. 124 Table 3.24 Statistical comparison between observed and predicted subsurface

drainage rates for experimental fields 2-5 of the TRS site in 1992-1997.

125

Table 3.25 Observed and predicted annual subsurface drainage for experimental fields 2-5 of the TRS site in 1992-1997.

126

Table 3.26 Observed and predicted annual loss of NO3-N via subsurface

drainage for experimental fields 2-5 of the TRS site in 1992-1997. 127 Table 3.27 Observed and predicted cumulative drainage and NO3-N drainage

loss for experimental fields 2-5 of the TRS site in 1992-1997. 128 Table 3.28 Predicted annual rates (kg N/ha) of fertilizer application, net

mineralization, plant uptake, denitrification, and NO3-N drainage

loss for field 2 of the TRS site in 1992-1997.

128

Table 3.29 Predicted annual rates (kg N/ha) of fertilizer application, net mineralization, plant uptake, denitrification, and NO3-N drainage

128

129

APPENDIX A:

Table A.1 Monthly estimated PET at the Tidewater Research Station experimental site near Plymouth, N.C.

224

Table A.2 Monthly measured rainfall at the Tidewater Research Station experimental site near Plymouth, N.C.

224

Table A.3 Drainage system parameters for experimental fields 2-5. 225 Table A.4 Lateral saturated hydraulic conductivity for experimental

fields 2-5. 225

Table A.5 Soil water characteristic data for field 2. 226

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LIST OF TABLES (continued)

Table A.9 Water table depth-volume drained relationship for fields 2-5. 228 Table A.10 Water table depth-upward flux relationship for fields 2-5. 229 Table A.11 Coefficients of Green-Ampt equation for fields 2-5. 229 Table A.12 Cropping window, growing season, and planting date reduction

parameters for wheat. 230

Table A.13 Cropping window, growing season, and planting date reduction

parameters for soybean. 230

Table A.14 Cropping window, growing season, and planting date reduction

parameters for corn. 231

Table A.15 Excess water stress susceptibility factors for wheat,soybean, and

corn. 231

Table A.16 Excess water stress parameters for wheat,soybean, and corn. 231 Table A.17 Deficit Water Stress Susceptibility Factors for wheat,soybean,

and corn. 232

Table A.18 Deficit water stress parameters for wheat,soybean, and corn. 233 Table A.19 Effective rooting depth functions for wheat, soybean, and corn. 233 Table A.20 SEW parameters for wheat, soybean, and corn. 235 Table A.21 Trafficability parameters for wheat, soybean, and corn. 236 Table A.22 Soil temperature input parameters for the Tidewater Research

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LIST OF FIGURES

CHAPTER 1:

Figure 1.1 Nitrogen cycle considered in the original version of RAINMOD-N. 10

CHAPTER 2:

Figure 2.1 Nitrogen cycle considered in DRAINMOD-N II. 48

Figure 2.2 Interaction between NH4-N and NH3-N in the solid (s), aqueous

(a), and gaseous (g) phases. 48

Figure 2.3 Carbon Cycle as modeled in DRAINMOD-N II. 49

Figure 2.4 Descretization of DRAINMOD-N II solution domain using

cell-centered, non-uniform grid. 50

Figure 2.5 Temperature response functions of the old and new versions of

DRAINMOD-N. 51

CHAPTER 3:

Figure 3.1 General layout of the Tidewater Research Station experimental

site at Plymouth, N.C. 130

Figure 3.2 Detailed layout of an experimental field of the Tidewater Research

Station site. 130

Figure 3.3 Water table management treatments of fields 2, 3, 4, and 5

in 1992. 131

in 1993. 132

in 1994. 133

in 1995. 134

in 1996 and 1997. 135

Figure 3.8 Observed and predicted water table depths for field 2 in 1992. 136 Figure 3.9 Water table depth scatter diagram for field 2 in 1992. 136 Figure 3.10 Cumulative rainfall, evapotranspiration, subsurface drainage, and

surface runoff for field 2 in 1992. 137

Figure 3.11 Subsurface drainage scatter diagram for field 2 in 1992. 137 Figure 3.12 Observed and predicted water table depths for field 2 in 1993. 138 Figure 3.13 Water table depth scatter diagram for field 2 in 1993. 138 Figure 3.14 Cumulative rainfall, evapotranspiration, subsurface drainage and

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LIST OF FIGURES (continued)

Figure 3.16 Observed and predicted water table depths for field 2 in 1994. 140 Figure 3.17 Water table depth scatter diagram for field 2 in 1994. 140 Figure 3.18 Cumulative rainfall, evapotranspiration, subsurface drainage and

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LIST OF FIGURES (continued)

Figure 3.46 Cumulative rainfall, evapotranspiration, subsurface drainage and

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LIST OF FIGURES (continued)

Figure 3.103 Subsurface drainage scatter diagram for field 5 in 1997. 183 Figure 3.104 Observed and predicted cumulative rates of subsurface drainage

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LIST OF FIGURES (continued)

Figure 3.105 Cumulative rates of fertilizer application, plant uptake, denitrification, and net mineralization as predicted by

DRAINMOD-N II (Field 2, 1992). 184

Figure 3.106 Observed and predicted cumulative rates of subsurface drainage

and NO3-N leaching loss for field 2 in 1993. 185

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LIST OF FIGURES (continued)

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LIST OF FIGURES (continued)

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LIST OF FIGURES (continued)

and NO3-N leaching loss for field 3 in 1992-1997. 209

and NO3-N leaching loss for field 4 in 1992-1997. 210

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CHAPTER 1

INTRODUCTION

THE ORIGINAL VERSION OF DRAINMOD-N

An Overview

DRAINMOD-N (Brevé, 1994) was originally developed as a nitrogen (N) component

of the water management model, DRAINMOD (Skaggs, 1978; Skaggs et al., 1991) to

simulate N dynamics in shallow water table soils with artificial drainage. It considers

a simple N cycle, which includes atmospheric deposition, fertilizer dissolution, net

mineralization of organic nitrogen (ON), denitrification, plant uptake, and N losses via

surface runoff and subsurface drainage (Figure 1.1). Nitrate-Nitrogen (NO3-N) is the only N

pool considered in the model. It simulates reactive transport of NO3-N using a finite

difference solution to the one-dimensional advection-dispersion-reaction (ADR) equation. N

transformations affecting NO3-N dynamics were modeled using simple (zero- or first-order)

functional relationships and lumped together as a source/sink term in the ADR equation.

DRAINMOD-N daily predictions include NO3-N concentration in soil solution and drainage

outflow, and cumulative rates of rainfall deposition, fertilizer dissolution, net mineralization,

plant uptake, denitrification, and N losses due to subsurface drainage and surface runoff

(Brevé et al., 1997).

The model uses the one-dimensional ADR equation to describe reactive transport of

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( )

_S z vC z C D z t C + ∂ ∂ −       ∂ ∂ ∂ ∂ = ∂ ∂θ _θ (1.1)

where C is the NO3-N concentration [ML-3], θ is the volumetric soil water content [L3L-3], v

is the soil water flux [LT-1], D is the coefficient of hydrodynamic dispersion [L2 T-1], S is

a source/sink term [ML-3 T-1], t is time [T], and z is a spatial coordinate [L].

The coefficient of hydrodynamic dispersion is given by (Brevé et al., 1997),

d v D τ θ µ + = _(1.2)

where µ is the longitudinal dispersivity [L], τ is a dimensionless tortuosity factor, and d is

the molecular diffusion coefficient [L2 T-1].

DRAINMOD-N numerical solver discretizes the solution domain, extending from soil

surface to a user-located impermeable layer, using a vertex-centered, uniform grid. It uses a

first-order accurate, explicit finite difference scheme to solve Equation (1.1) numerically.

Thus, NO3-N concentration at node i and new time level, l+1 is given by (Brevé et al., 1997),

(

)

(

)

[

]

[

]

l

i l i l i l i l i l i l i l i l i l i l i l i l i l i l i l i l i l i l i l i l i l i S t C q C v C v z t C C D C C D z t C C 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 + + + + − + + − + + + + + + + + ∆ + − − ∆ ∆ + − − − ∆ ∆ + = θ θ θ θ θ θ θ (1.3)

where ∆z is the spatial increment, ∆t is the temporal increment, and qC is a term introduced

by Brevé (1994) to represent lateral flux going to parallel drains.

Soil water flux and soil water content at old and new time levels are estimated by

interpolating DRAINMOD-predicted average daily soil water flux and soil water content

(Brevé et al., 1997). No lateral transport is considered in the unsaturated zone and q is set to

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Hooghoudt’s drainage flux at the surface of the water table to zero at the impermeable layer

(Brevé et al., 1997).

Processes other than N transport are included in the source/sink term of the ADR

equation as follows,

den upt rnf mnl fer

dep S S S S S

S

S = + + − − − _(1.4)

where Sdep, Sfer, and Smnl are amounts of N [ML-3T-1] added to the system due to rainfall

deposition, fertilizer dissolution, and net mineralization, respectively, and Srnf, Supt, and Sden

are amounts of N [ML-3T-1] taken from the system due to surface runoff, plant uptake, and

denitrification, respectively.

Rainfall deposition to soil surface is estimated by (Brevé et al., 1997),

z fC

S rain

dep = _∆ (1.5)

where f is the infiltration rate [LT-1_{] and}_C_{rain is NO}

3-N concentration in rainfall.

Fertilizer dissolution is modeled as a zero-order rate process (Brevé et al., 1997),

fer fer fer fer D A K S =     < ≥ = ₋ − fer fer fer for day for day K θ θ θ θ 1 1 0 1 (1.6)

where Afer is the fertilizer application rate [ML-2], Dfer is the incorporation depth [L], Kfer is

a zero-order rate dissolution constant [T-1] and θfer is a threshold soil water content below

which fertilizer dissolution does not occur.

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org b t mnl

mnl K f f N

S = _θ_mnl ρ _(1.7)

where Kmnl is a zero-order rate constant [T-1], fθ_mnl and ft are dimensionless soil water and

temperature response functions, respectively, ρb is the soil bulk density [ML-3_{], and}_N_{org is}

the ON mass fraction [M M-1] as approximated by (Brevé et al., 1997),

(

z

)

f f N N z z org org α − = = exp max (1.8)

where Norg_max is the mass fraction of ON at the surface soil layer [MM-1], fz is a depth function

approximating the distribution of ON throughout the soil profile, and α is an empirical

constant.

Runoff loss of NO3-N is estimated by (Brevé et al., 1997),

z C q S_rnf rnf rnf

∆

= _(1.9)

where qrnf is runoff rate [LT-1] as predicted by DRAINMOD and Crnf is an empirically

estimated concentration [ML-3] of NO3-N in runoff.

Plant uptake is approximated by an empirical relationship (Brevé et al., 1997),

root upt crp upt D f N

S = _(1.10)

where Ncrp is the total amount of N taken up by plants during the whole growing season

[ML-2_],_f_{upt is a fractional N-uptake demand [T}-1_{] defined as a function of growing season,}

and Droot is the effective rooting depth [L].

Denitrification is modeled as a first-order rate process (Brevé et al., 1997),

3 NO z t den

den K f f f C

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where Kden is a first-order rate constant [T-1], fθ_den is a soil water response function for

denitrification, θ is the volumetric soil water content [L3L-3], and CNO₃ is the NO3-N

concentration [ML-3].

Soil water and temperature response functions are introduced to account for the effect

of soil water and temperature on rates of mineralization and denitrification processes.

The soil water response function for mineralization, fθ_mn is given by (Brevé et al., 1997),

           < ≤           − − < ≤ < ≤         − − + = l wp wp l wp h l s h h s s mn f θ θ θ θ θ θ θ θ θ θ θ θ θ θ θ θ θ θ 2 2 0 . 1 4 . 0 6 . 0 (1.12)

where θh and θl are threshold soil water contents [L3L-3] defining the optimum range for

mineralization rate and θs and θwpare soil water contents [L3L-3] at saturation and permanent

wilting point, respectively.

The soil water response function for denitrification, fθ_dn is given by (Brevé et al.,

1997),       ≥         − − < = dn dn s dn dn dn

f _θ _θ

θ θ θ θ θ θ θ 2 0 (1.13)

where θdn is a minimum soil water content [L3L-3] below which denitrification does not

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The same temperature response function is used for both mineralization and

denitrification processes (Brevé et al., 1997),

      − = 10 10 b T T t Q f _(1.14)

where Q10 is the process response to a 10oC soil temperature change, T is the soil temperature

[oC] and Tb is the base temperature [oC] at which the response function equals unity.

Brevé et al. (1997) used an empirical equation to estimate soil temperature,

(

)

_      ₋ ₋      ₋ − = m j m o air D z t D z A T

T π ϕ

365 2 cos

exp _(1.15)

where Tair is the annual average air temperature [oC], Ao is the amplitude of the temperature

wave [oC], Dm is the wave damping depth [L], ϕ is the phase shift [T], and tj is the day of

the year.

Luo et al. (2000) modified DRAINMOD to model field hydrology under cold

conditions. The modified version of DRAINMOD predicts soil temperature by solving

the one-dimensional heat equation using the implicit Crank-Nicolson finite difference

scheme. DRAINMOD-N was modified to either use Equation (1.15) to estimate soil

temperature (Brevé et al., 1997) or directly use DRAINMOD-predicted soil temperature

(Luo et al., 2000).

Drawbacks and Limitations

Brevé (1994) assumed that ammonium-nitrogen (NH4-N) is very short-lived in

the soil-water system. Thus, he ignored the nitrification process and did not consider

the NH4-N pool. This approach is applicable only when the source of NH4-N is either net

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the system is large and sudden, as would occur after the application of ammoniacal N

fertilizers (Molina and Smith, 1998). It also fails if nitrification process is impaired either due

to unfavorable environmental conditions or by using nitrification inhibitors (Tisdale et al.,

1993). Modeling the nitrification process with reaction rates reflective of environmental

conditions and fertilizer management practices is essential for modeling N dynamics and

turnover in the soil-water-plant system.

Ammonium, NH4+, is usually considered as an immobile cation. It may be nitrified to

NO3-, absorbed by higher plants or microorganisms, fixed to some clay minerals, or adsorbed

in the exchange sites of the soil solid phase (Tisdale et al., 1993). However under certain

conditions, NH4-N can be found in the aqueous phase with appreciable concentrations and be

susceptible to leaching losses. Factors affecting NH4-N concentration in the soil solution are,

soil exchange capacity, soil pH, soil organic matter, rate of nitrification process, and fertilizer

management practices. The transport of NH4-N should be modeled when conditions favor

relatively high concentrations of NH4-N in the soil solution.

The original version of DRAINMOD-N has a limited capability of modeling fertilizer

application and accompanying processes. It does not handle the application of NH4 and

NH4-forming fertilizers such as urea, anhydrous ammonia, and urea-ammonium-nitrate

(UAN). This limitation comes as a direct consequence of neglecting the NH4-N pool.

One of the main drawbacks of the original version of DRAINMOD-N is the way of

treating soil organic matter. Brevé (1994) used a simple approach to model the interaction

between organic and mineral N pools. The ON pool was considered as a continuous supply

of mineral N through a one-way mineralization process. Once it is initialized at the beginning

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this approach is not capable of modeling the cumulative effects of agricultural practices such

as application of plant residues and animal manure on ON dynamics and mineral N

availability. It fails also in modeling long-term effects of agricultural practices on ON

dynamics and turnover. A more realistic representation of soil ON is required for long-term

simulations and if organic forms of N (crop residues and animal waste) are to be used.

The denitrification process is another area of weakness of the model. In recent

field-testing, the model poorly predicted cumulative denitrification rates (Madramootoo et al.,

1999). Brevé (1994) modeled the denitrification process using a first-order rate kinetics with

respect to NO3-N and simulated organic carbon (OC) effect on process rate using a depth

function. This approach performs well under NO3-N limiting conditions; however, it tends to

over-estimate process rates under high NO3-N concentrations.

OBJECTIVES

The main goal of this research is to develop and test a new version of DRAINMOD-N

(referred to as DRAINMOD-N II) capable of modeling N dynamics and turnover in the

soil-water-plant system under different management practices and soil and environmental

conditions. Specific objectives are:

1. Modifying DRAINMOD-N to:

1.1. consider both NO3-N and NH4-N pools in modeling mineral N and simulate

the nitrification process;

1.2. provide a comprehensive fertilizer submodel capable of simulating the application of

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associated short-term processes such as fertilizer dissolution, urea hydrolysis, and

NH3 volatilization;

1.3. simulate OC dynamics using a simplified carbon cycle in order to better describe N

mineralization/immobilization processes and allow modeling the application of ON

sources (crop residues and animal waste);

1.4. model the denitrification process in a way that better describes the effect of NO3-N

and soil OC availability on process rate.

These modifications should improve model performance and increase the range of

conditions and management practices for which the model can be appropriately used.

2. Field-testing of DRAINMOD 5.1/DRAINMOD-N II models to:

2.1. evaluate the performance of DRAINMOD 5.1 in simulating hydrologic processes at

the field scale;

2.2. calibrate and validate the newly developed nitrogen model, DRAINMOD-N II.

Chapter 2 of this dissertation describes DRAINMOD-N II with focus on its new

features. Chapter 3 describes the field-testing of DRAINMOD 5.1/DRAINMOD-N II models

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Figure 1.1 Nitrogen cycle considered in the original version of DRAINMOD-N

NO3-N

Atmosphere

N Plant-N

ON _Fertilizer-NMineral

Plant Uptake

Fert. Dissolution Mineralization

Denitrification

Atm. Deposition

Lea

chin

g and

Runoff los

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REFERENCES

Brevé, M. A. 1994. Modeling the movement and fate of nitrogen in artificially drained soils. Unpublished Ph.D. dissertation, North Carolina State University, Raleigh, N. C.

Brevé, M. A., R. W. Skaggs, J. E. Parsons, and J. W. Gilliam. 1997. DRAINMOD-N, A nitrogen model for artificially drained soils. T. ASAE 40(4): 1067-1075.

Luo, W., R. W. Skaggs, and G. M. Chescheir. 2000. DRAINMOD modifications for cold conditions. T. ASAE 43(6): 1569-1582.

Madramootoo, C. A., J. W. Kaluli, G. T. Dodds. 1999. Simulating nitrogen dynamics under water table management systems with DRAINMOD-N. T. ASAE 42(4): 965-973.

Molina, J. E. and P. Smith. 1998. Modeling carbon and nitrogen processes in soils. Adv. Agron. 62: 253-298.

Skaggs, R. W. 1978. A water management model for shallow water table soils. Rept. 134. Raleigh, N.C.: North Carolina Water Resources Res. Inst., North Carolina State Univ.

Skaggs, R. W., T. Karvonen, and H. Kandil. 1991. Predicting soil water fluxes in drained lands. ASAE paper No. 91-2090. St. Joseph, Mich., ASAE.

Tisdale, S. L., W. L. Nelson, J. D. Beaton, and J. L. Havlin. 1993. Soil fertility and fertilizers.

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CHAPTER 2

MODELING NITROGEN DYNAMICS IN ARTIFICIALLY DRAINED SOILS: A NEW VERSION OFDRAINMOD-N

ABSTRACT

A new version of DRAINMOD-N (referred to as DRAINMOD-N II) was developed to

simulate nitrogen (N) dynamics and turnover in the soil-water-plant system under different

management practices and soil conditions. The original version of the model simulated

the dynamics of only one N pool, nitrate-nitrogen (NO3-N), using a simplified N cycle,

which restricted its applicability. DRAINMOD-N II considers a more complete N cycle, adds

a carbon (C) cycle, and operates at different levels of complexity according to the conditions

of the system being simulated. Processes considered in the new model are atmospheric

deposition, application of mineral N fertilizers including urea and anhydrous ammonia

(NH3), soil amendment with organic N sources such as plant residues and animal manure,

plant uptake, N mineralization and immobilization, nitrification, denitrification, NH3

volatilization and N losses due to leaching and surface runoff. Nitrogen pools considered in

DRAINMOD-N II are NO3-N, ammoniacal-nitrogen (NHx-N) and organic nitrogen (ON).

DRAINMOD-N II simulates the dynamics of organic carbon (OC) using a simplified C cycle

to better describe the interaction between mineral and organic N pools. It divides OC into

different pools characterized by their carbon-to-nitrogen ratios (C:N) and turnover rates.

Each OC pool has a corresponding ON pool. DRAINMOD-N II uses a simplified approach to

simulate temporal changes in soil pH induced by N fertilizer application, nitrification,

and N plant uptake; consequently it determines the composition of the NHx-N pool.

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of the one-dimensional advection-dispersion-reaction (ADR) equation. Model output

includes daily concentrations of NO3-N and NH4-N in soil solution and drainage outflow, and

cumulative rates of simulated N processes.

INTRODUCTION

The detrimental impacts of nitrogen (N) losses from agricultural lands on

environmental quality have long been recognized. Nitrate-Nitrogen (NO3-N) leached from

agricultural fields elevates N concentrations in groundwater and surface water bodies which

contaminates drinking water supplies and enhances eutrophication of surface waters causing

hypoxia problems (Gilliam et al., 1999). Gaseous N emitted from agricultural fields may also

cause air pollution. Nitrous oxide is a greenhouse gas that contributes to the global warming

and ammonia (NH3) contributes to the acid rain phenomena.

Developing agricultural management practices that reduce off-site environmental

impacts of crop production has been the focus of research over many years (Gilliam et al.,

1999). However, the development and testing of such practices is a complex task since it

requires understanding of N dynamics in the soil-water-plant system, which is regulated by

a large number of interacting and sometimes highly dynamic physical, chemical, and

biological processes. Nitrogen models are useful tools for developing and evaluating

management practices for sustainable agricultural systems.

DRAINMOD-N (Brevé, 1994) was developed to model N fate and transport in

artificially drained soils. However, it used a simplified N cycle, which limited its

applicability. Brevé (1994) assumed that ammonium-nitrogen (NH4-N) is very short-lived in

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applicable when the only source of NH4-N is either mineralization of organic nitrogen (ON)

or application of slow-release N fertilizers. However, it fails when NH4-N input to the system

is large and sudden, as would occur after the application of ammoniacal N fertilizers (Molina

and Smith, 1998). It also fails if the nitrification process is impaired either due to unfavorable

environmental conditions or by using nitrification inhibitors. Consequently, the model was

unable to simulate the application of any NH4 or NH4-forming fertilizers including urea and

anhydrous NH3.

Brevé (1994) modeled the interaction between organic and mineral N pools as a

one-way net mineralization process with constant potential rate. The model did not simulate

temporal changes in the ON pool, but it rather modeled the ON pool as a static source of

mineral N. This approach did not adequately describe the interaction between organic and

mineral N. It also did not allow the model to simulate amending soils with ON sources such

as crop residues and animal waste.

Brevé (1994) modeled the denitrification process using a first order rate kinetics with

respect to NO3-N and simulated organic carbon (OC) effect on process rate using

an exponential depth function. In recent field-testing, the model poorly predicted cumulative

denitrification rates (Madramootoo et al., 1999). Taking into consideration the complexity of

the denitrification process and the relatively limited success in modeling its rates at the field

scale (Barton et al., 1999), Brevé’s modeling approach of denitrification may be acceptable.

However, better models that account for the effect of NO3-N and OC variability on process

rate should be possible.

The main goal of this research was to develop a new version of DRAINMOD-N

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soil-water-plant system under different management practices, and soil and environmental

conditions. DRAINMOD-N II considers both NO3-N and ammoniacal-nitrogen (NHx-N)

pools in modeling inorganic N and simulates the nitrification process. It includes

a comprehensive fertilizer submodel capable of simulating the application of NH4 and

NH4-forming fertilizers, including urea and anhydrous NH3, and associated short-term

processes such as fertilizer dissolution, urea hydrolysis, temporal pH change, and NH3

volatilization. It simulates OC dynamics using a simplified C cycle in order to better describe

N mineralization/immobilization processes and model the application of ON sources.

It includes a new denitrification routine to better describe the effect of NO3-N and OC

availability on process rate.

MODEL DESCRIPTION

The Nitrogen Cycle

DRAINMOD-N II considers a detailed N cycle that includes three N pools: NO3-N, NHx-N,

and ON. The NHx-N pool is set to be optional so, it may be ignored if soil and environmental

conditions do not favor its accumulation in the system. Processes considered in

DRAINMOD-N II are atmospheric deposition, application of mineral N fertilizers including

urea and anhydrous NH3, application of ON sources, N plant uptake, N mineralization and

immobilization, nitrification, denitrification, NH3 volatilization and NO3-N and NHx-N losses

due to leaching and surface runoff (Figure 2.1).

The form of NHx-N in soil is pH dependent. NH4-Ndominates in acid to neutral soils.

Appreciable amounts of NH3-N appear when soil pH exceeds 7.5 (Tisdale et al., 1993).

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is strongly held in the negative exchange sites of clay minerals and thus its aqueous phase

concentration is usually small. In sandy soils, however, considerable amounts of NH4-N can

be found in the aqueous phase, which makes it susceptible to leaching. On the other hand,

NH3-N can occur in solid, aqueous, and gaseous phases and thus it can be leached and

volatilized (Figure 2.2).

The Carbon Cycle

Organic carbon plays a central rule in regulating N dynamics in the soil-plant system.

Readily decomposable OC is necessary for the denitrification process. N mineralization/

immobilization processes are closely related to C dynamics during organic matter (OM)

decomposition. DRAINMOD-N II includes a submodel that simulates C dynamics and

turnover in the soil-plant system according to a simplified C cycle (Figure 2.3). It is based on

the soil organic matter (SOM) component of the CENTURY model (Parton et al., 1987).

It includes three SOM pools: active, slow, and passive and two added organic matter (AOM)

pools: metabolic and structural (Figure 2.3). Each pool is characterized by its OC content,

potential rate of decomposition, and its carbon-to-nitrogen (C:N) ratio. Only the C:N ratio of

the metabolic pool varies with time. Each OM pool has a corresponding ON pool.

Thus, the main ON pool shown in Figure 2.1 includes three ON pools associated with

the active, slow, and passive SOM pools and two ON pools associated with the metabolic

and structural AOM pools.

The active pool includes microbial biomass and metabolites and has the fastest

turnover rate among the SOM pools. The slow pool includes stabilized decomposition

products with intermediate turnover rate. The passive pool represents highly stabilized OM

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structural pools according to its lignin-to-nitrogen (L:N) ratio. All lignin goes to

the structural pool, which decomposes in much slower rates than the metabolic pool.

The lignin content of the structural pool influences its turnover rate. It also affects OC flow

from the structural pool to the active and slow pools. Soil texture affects OC decomposition

of the active pool. OC flow among individual pools during the course of decomposition is

associated with C loss as CO2 due to microbial respiration (Parton et al., 1987; Paustian et al.,

1992; Parton, 1996).

Modes of Operation

DRAINMOD-N II operates in three modes with different levels of complexity. The simplest

mode is based on the original version of the model (Brevé, 1994), which ignores the NHx-N

pool and considers all mineral N to be in the NO3-N form. In this mode, the model ignores

processes of nitrification, temporal pH change and NH3 volatilization. It considers NO3-N as

the end product of the mineralization process, and restricts processes of atmospheric

deposition, fertilizer application, immobilization, plant uptake, and leaching and runoff losses

to the NO3-N pool only. The model in this mode is unable to simulate the application of urea

and anhydrous NH3 and associated short-term processes promoting volatilization losses.

The model, however, can still handle soil amendment with ON sources.

In the other two modes, the model considers both NO3-N and NHx-N pools in

modeling mineral N. The second mode is referred to as the normal mode. In this mode

the model considers NHx-N as one species, NH4-N, which partitions between the aqueous

and solid phases. The last mode, referred to as the volatilization mode, is the most complex.

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NH4-N, like in the normal mode, is partitioned between the aqueous and solid phases,

whereas NH3-N is partitioned among the gaseous, aqueous, and solid phases.

The user should set the model to ignore or consider the NHx-N pool according to

the conditions of the system being simulated. When the model is set to consider the NHx-N

pool, it switches automatically between the normal and volatilization modes according to

soil pH.

Transport Component of DRAINMOD-N II

Governing Equations

The transport component of DRAINMOD-N II simulates N transport using a

multi-phase form of the one-dimensional advection-dispersion-reaction (ADR) equation. Both

advection and dispersion are considered in modeling transport in the aqueous phase.

Molecular diffusion is assumed to be the main transport mechanism in the gaseous phase

(Hillel, 1982).

The mass balance of a generic species that is partitioned between aqueous, gaseous,

and solid phases can be expressed as (Baehr, 1987),

(

)

(

)

S

z C v z C d z C D z C C C t a a g g g a a a s b g g a a + ∂ ∂ −       ∂ ∂ + ∂ ∂ ∂ ∂ = + + ∂ ∂ _θ _θ _ρ _θ _θ (2.1)

where Ca and Cg are the species concentrations [ML-3] in the aqueous and gaseous phases,

respectively, Cs is the species concentration in the solid phase [MM-1], θa and θg are

the volumetric fractions [L3L-3] of the aqueous and gaseous phases, respectively, ρb is the dry

bulk density of the solid phase [ML-3], va is the volumetric flux of the aqueous phase [LT-1],

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transport in the aqueous phase, dg is the molecular diffusion coefficient [L2 T-1] that

characterizes diffusive transport in the gaseous phase, S is a source/sink term [ML-3 T-1], t is

time [T], and z is a spatial coordinate [L].

It is assumed that the sorption process is fast compared to the model time step. In

other words, it is assumed that there is local equilibrium between species concentrations in

both aqueous and solid phases. Thus, when a state of equilibrium is disturbed, a new state is

attained instantaneously. Preul and Schroepfer (1965) showed experimentally that the time

required to attain equilibrium between dissolved and adsorbed NH4-N ranges from 2 to 6

hours depending on soil type. So the local equilibrium assumption seems to be reasonable for

modeling the interaction between dissolved and adsorbed N species. It is further assumed that

the relationship between aqueous and solid phase concentrations of N species can be

described using a linear Freundlich isotherm (Zheng and Bennett, 1995),

a d

s K C

C = _(2.2)

where Kd is the distribution coefficient [L3M-1].

Preul and Schroepfer (1965) found experimentally that NH4-N sorption follows

the Freundlich isotherm with an exponent close to unity. Other researchers also used

the linear Freundlich isotherm to describe NH4-N sorption (Selim and Iskandar, 1981; Knisel

et al., 1993; Groenendijk and Kroes, 1997).

Henry's law is used to related species concentrations in both aqueous and gaseous

phases (Baehr, 1987),

H C

Cg = a _(2.3)

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Thus, equation (2.1) can be written in terms of species concentration in the aqueous

phase,

(

)

_S

z C v z C H d D z C K H t a a a g g a a a d b g a + ∂ ∂ −       ∂ ∂       ₊ ∂ ∂ =             ₊ ₊ ∂ ∂ _θ θ _ρ _θ _θ (2.4)

Equation (2.4) is a multi-phase form of the one-dimensional ADR equation, which

can be rewritten in a more compact form (Šimůnek and Suarez, 1993) as,

(

)

(

)

S

z C v z C D z C R t a a a e a f a + ∂ ∂ −       ∂ ∂ ∂ ∂ = ∂ ∂ _θ (2.5)

where Rf is a dimensionless retardation factor and De is an effective dispersion coefficient

[L2T-1].

Equation (2.5) can be used to describe the reactive transport of NO3-N, NH4-N, and

NH3-N by adjusting Rf and De (Table2.1). The negatively charged NO3 ion primarily occurs

in the aqueous phase. NH4 is positively charged and thus is held strongly in the negative

exchange sites of clay minerals and thus can occur in both aqueous and solid phases. NH3 is

a neutral ion that can be held weakly to the solid phase but it also partitions to the aqueous

and gaseous phases.

The coefficient of hydrodynamic dispersion, Da can be expressed as (Brevé et al.,

1997),

a a

a v d

D τ

θ

µ +

= _(2.6)

where µ is the longitudinal dispersivity [L], τ is the aqueous phase tortuosity [dimensionless],

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NH3-N diffusion coefficient in the gaseous phase, dg is expressed as (Domenico and

Schwartz, 1998),

air g

g d

d =τ _(2.7)

whereτgis the gaseous phase tortuosity and d air is the diffusion coefficient of NH3 gas in air

[L2 T-1].

The gaseous phase tortuosity is quantified using the Millington and Quirk empirical

formula (Domenico and Schwartz, 1998),

2 3 7

n

g

g θ

τ = (2.8)

where n is the soil porosity [L3_L-3_].

The diffusion coefficient of NH3 gas in air, d air (cm2d-1) is quantified by (Sadeghi et

al., 1988),

6 15897 96

120. T .

dair = − (2.9)

where T is temperature [Ko].

NH4-N adsorption into the soil matrix depends on clay content, the cation exchange

capacity of the prevailing clay mineral, SOM content, and composition of soil solution.

The distribution coefficient, which characterizes NH4-N sorption, Kd,NH4 can be obtained

either experimentally or from literature. Knisel et al. (1993) provided an empirical expression

for estimating Kd,NH4 as a function of clay content. Groenendijk and Kroes (1997) used

a more detailed expression to estimate Kd,NH4 taking into account the effects of both soil

texture and solution chemistry. To give the model more flexibility, Kd,NH4 is set to be an input

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The distribution coefficient of NH3-N, Kd,NH3 is quantified by an expression that is

frequently used in modeling sorption of neutral hydrocarbon contaminants (Domenico and

Schwartz, 1998),

oc oc NH

,

d K f

K 3 = (2.10)

where Koc is the partition coefficient of NH3 between organic carbon and water, set to 3.0903

cm3gm-1 and foc is a dimensionless mass fraction of OC.

The partitioning of NH3-N between aqueous and gaseous phases varies markedly with

temperature. Freney et al. (1983), reported an expression for the variation of Henry’s

coefficient, H of NH3-N with temperature,

6937 1 7 1477 10 . T . H

log = − _(2.11)

where T is temperature [Ko].

Numerical Solution

The transport component of DRAINMOD-N II solves the ADR equation (2.5) for

the boundary conditions,

( )

_{( )}

( )

₀ ₀

0 0 0 0 0 0 0 > = ∂ ∂ >    > ≤ = t Z t, L C t v C v t, C a a rain a a (2.12)

and the initial condition,

( )

Z, C

( )

Z

Ca 0 = o (2.13)

where Crain is N species concentration in rainfall [ML-3], Co(Z) is the initial concentration of

individual N species as a function of soil depth, and L is the length of the solution

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The boundary conditions given by (2.12) represent a first-kind (Dirichlet) boundary

condition at the top boundary and a second-kind (Neumann) boundary condition at

the bottom boundary. N species concentration at the top boundary is set to zero except when

there is infiltration induced by a rainfall event. In such case, N species concentration at

the top boundary is set to its concentration in rainfall (Brevé et al., 1997).

As shown in Figure 2.4, DRAINMOD-N II descretizes the solution domain, which

extends from soil surface to the top of a user-defined impermeable layer, using a

cell-centered, non-uniform grid (Thomas, 1995). Cell length is a user-input that should be chosen

such that, Z j j j Z max j min n ,..., j , Z Z Z . n ,..., j , Z Z Z ∆ + + ∆ = ∆ ≤ ∆ ≤ ∆ = ∆ ≤ ∆ ≤ ∆ 1 2 5 0 1 1 1 (2.14)

where ∆Zmin and ∆Zmax are the minimum and maximum allowable spatial increments,

respectively, and n∆Z is the total number of spatial increments.

DRAINMOD-N II uses the standard split-operator approach to decouple the transport

portion and reaction/transformation portion of the ADR equation. The model first solves

the transport portion over a single time step to get an intermediate set of N species

concentrations which are then used to solve the reaction/transformation portion to get N

species concentrations at the new time level (Miller and Rabideau, 1993).

The transport portion is solved numerically using a first-order accurate, explicit finite

difference scheme. Thus, average N species concentration of cell i and new time level, l+1 is