The Effect of Soil Hydraulic Properties and Deep Seepage Losses on Drainage Flow
using DRAINMOD
By
Debjani Deb, ABE 527 (Term project)
ABSTRACT
The water management simulation model DRAINMOD was used to analyze a tile
drained, agricultural Indiana watershed. The input parameters for the simulation were
climatic data, soil hydraulic properties, crop data and the drainage design parameters. The
error encountered in drainage flow due to uncertainty in soil hydraulic properties and
deep seepage losses at the Animal Science Watershed, Tippecanoe County, Indiana was
studied. It was concluded that the deep seepage losses contributes a portion of base flow
in the stream flow. In order to simulate the observed drainage results, deep seepage needs
to be considered. Along with deep seepage losses, it was also seen that soil hydraulic
properties also affects drainage. Simulating the same input parameters under different soil
type, results in different drainage flows and seepage.
INTRODUCTION
Drainage is the practice of removing excess water from the land and is used as one of the
most important land management tools for improved crop production. It is necessary for
economical and efficient crop production. Since drainage and the related design
parameters like soil, drain spacing, depth of drain, effective radius, distance from surface
to restricting layer, distance from drain to effective drainage barrier etc. affects the
pattern of water flow from the land and also the water quality, modeling of the drainage
flow is very beneficial. Computer simulation models helps in predicting subsurface drain
flow and water table depth in a greater variety of conditions than what is feasible through
monitoring. DRAINMOD is a computer simulation model, designed for soils with high
water table and subsurface drains. It relates water management system with water table
and soil water conditions. DRAINMOD was chosen as it can analyze input data to
evaluate sub-surface hydrology of a tile drained watershed by allowing simulations of
surface and sub-surface hydrology while accounting for the tile drainage. The model is
physical, dynamic, distributed, and deterministic and event based. This means that the
model is based on the laws of physics, parameters have more variability over the modeled
area and simulation of a single storm event enables the analysis of hydrological effect of
the storm on the watershed. The field-testing results of DRAINMOD depend on the
amount of field-specific input data. DRAINMOD hydrology component has been tested
for different soils and climates. Reported average standard errors and average deviations
for subsurface drain flow range from 0.06 to 0.14 and 0.08 to 0.36 cm/day, respectively
(Mosley, 1998). The objective of this study is to asses the error encountered in drainage
flow due to uncertainty in soil hydraulic properties and deep seepage losses at the Animal
Science Watershed, Tippecanoe County, Indiana using DRAINMOD as it simulates the
hydrology of poorly drained, high water table soils and predicts the effects of drainage on
water table depths, the soil water regime and crop yields. It also helps in simulating the
performance of water table management systems along with the lateral and deep seepage
from the field. The portion of rainfall or applied irrigation water that is in excess of the
water holding capacity passes through the rooting zone and is subsequently unavailable
for crop use is known as deep seepage. Deep seepage contributes a portion of base flow
in the stream flow. A hydrological assessment using DRAINMOD has been previously
done on the Animal Science Watershed based on the assumptions that the impermeable
layer of the watershed was at 6 ft deep with no deep seepage and the calculation of soil
types was based on visual assessment from the USDA soil map and some basic
calculation. The analysis of base flow in this study was not precise as deep seepage losses
which contribute to the base flow were not taken into consideration and calculation of
soil types were not accurate and hence introduced errors and uncertainty in the analysis.
The above mentioned limitations of the previous hydrological analysis of the Animal
Science Watershed has been addressed by a systematic procedure to ascertain and
quantify the uncertainty introduced into the results due to the cumulative effects of input
uncertainty and data variability.
MODEL DESCRIPTION
Drainage Theory
By developing one of the fundamental equations used to calculate water table equilibrium
from rainfall or irrigation water, S.B. Hooghoudht became one of the earliest contributors
in the field of watershed drainage processes. Hooghoudt's drainage equation (Hooghoudt
1940) gives a mathematical relation of the parameters involved in the subsurface drainage
of flat land by a system of horizontal and parallel ditches or pipe drains without entrance
resistance, placed at equal depth and subject to a steady recharge evenly distributed over
the area (Figure 1).
h2 = - R (x2 – Sx) + H2
k
(Luthin, 1966)
where h = height of water table above the impermeable layer
R = rainfall rate
k = hydraulic conductivity of the homogeneous soil
S = distance between drains
H = height of water in the drains
q
x
Figure 1: Diagram showing Hooghoudht’s drain flow equation
(DRAINMOD Reference Report, 1980).
DRAINMOD is a computer simulation model that simulates the hydrology of a poorly
drained soil for short/long periods of time and models the field scale effects of drainage
on related water management systems. DRAINMOD has been used mostly as a research
tool to study the performance of broad range of drainage and sub-irrigation systems and
their effects on water use, crop response, treatment of wastewater, and pollutant
movement from agricultural fields. DRAINMOD was developed by Skaggs (1978) for
design and evaluation of multicomponent water management systems that include surface
drainage, subsurface drainage, controlled drainage, and subirrigation as shown in Figure
1 (Skaggs, 1982), which illustrates the basic subsurface drain hydrologic phenomena of
the model. The rate of water drained from the profile is dependent on the drainage design
like drain depth and spacing, the effective profile depth, hydraulic conductivity of the soil
and the depth of water in the drains.
Figure 2: Schematic of water management system with sub surface drains that
may be used for drainage or subirrigation (DRAINMOD Reference Report, 1980).
The model is based on a water balance for a soil profile (Fig 3). The model predicts the
rate of infiltration, drainage, evapotranspiration and water table fluctuations on a daily
basis in response to given inputs consisting of climatic data, soil and crop properties, and
drainage design parameters. Approximate methods are used to evaluate the various
mechanisms of soil water movement and storage. The accuracy of the approximate
methods
was determined by comparing them to exact methods over a range of soils
and boundary conditions.
Figure 3: Schematic of water management system modeled by
Components used in the water balance is shown in the diagram
DRAINMOD (DRAINMOD Reference Report, 1980).
The basic relationship in the model is a water balance for a thin section of soil of unit
surface area, which extends from the impermeable layer to the surface and is located
midway between adjacent drains. The water balance for a time increment Δt can be
written as,
ΔVa = D + ET + DS – F (1)
(1)
where, ΔVa is the change in air volume or the water free pore space (cm),
D is lateral drainage from (or subirrigation into) the section (cm),
ET is evapotranspiration (cm),
DS is deep seepage (cm), and
F is infiltration (cm).
The amount of run-off and storage is computed from the water balance at the soil surface,
a water balance for each time increment Δt is computed as,
P = F + ΔS + RO (2)
where, P is precipitation (cm),
(2)
F is infiltration (cm),
ΔS is the change in volume of water stored on the surface (cm),
RO is runoff (cm).
Basically, one hour is the time increment used in both equations. However, time
increments of 3 minutes or less are used
to compute F when rainfall rates exceed the infiltration capacity. When there is no
rainfall and drainage and ET rates are slow such that the water table position moves
slowly with time, the time increment of 1 day is used in equation (1). When drainage is
rapid but no rainfall occurs, Δt of 2 hours is used in equation(1) (DRAINMOD Reference
Report, 1980).
The following are components used in DRAINMOD:

Precipitation records constitute one of the major inputs for DRAINMOD driving
the water balance. Rainfall data over short time increments will allow better
estimates of the model components like infiltration, runoff and surface storage
than infrequent data. Since hourly rainfall data is easily available the model uses a
basic time increment of 1 hour for rainfall data.

Infiltration is affected by several soil factors, crop factors and climatic factors.
Soil factors affecting infiltration are hydraulic conductivity, initial water content,
depth of profile, surface compaction and water table depth. The plant/crop factors
are extent of cover and depth of root zone. The climatic factors are intensity,
duration, time and distribution of rainfall, temperature and extent to which the soil
is frozen. DRAINMOD uses the Green-Ampt equation to compute infiltration
(Skaggs, 1980).
f = Ks + KsMSav
(3)
F
where f is infiltration rate (cm/hr),
Ks is hydraulic conductivity of the wetted zone (cm/hr),
M is the initial soil water deficit,
Sav is the effective suction at the wetting front (cm),
F is the cumulative infiltration (cm)
The Green-Ampt equation was originally derived for deep homogeneous profiles
with uniform initial water content and assumes that water enters the soil as slug
flow resulting in a sharply defined wetting front which separates a zone which has
been wetted by a totally uninfiltrated zone ((DRAINMOD Reference Report,
1980).
For any specific soil type with known initial water content the above equation
reduces to
f = A/F + B
(4)
where, A and B are constants based on soil properties.

Surface drainage is characterized by the average depth of depression storage that
must be satisfied before runoff can begin (Skaggs, 1982).Depression storage is
further broken down into a micro component and a macro component. Figure 2
shows the components of the depression storage. Macro component, Sm, is the
maximum surface storage which must be filled before runoff occurs. The macro
component is due to larger surface depressions which may be altered by land
forming or grading. The micro-component, S1, represents storage in small
depressions due to surface structure and cover. Surface storage could be
considered as a time dependent function and can be simulated during the year.
However, it is assumed to be constant in the present model version.
Figure 4: Schematic of Drainage from a ponded surface (DRAINMOD Reference Report,
1980).

Subsurface drainage: The rate of subsurface water movement into drain tubes or
ditches depend on hydraulic conductivity of the soil, drain spacing and depth, and
profile depth and water table elevation. DRAINMOD calculates the subsurface
drainage rates based on the assumption that lateral water movement occurs mainly
in the saturated region; thus the lateral saturated hydraulic conductivity is used.
When water table is completely below soil surface and the ponded water depth is
less than S1, Hooghoudt’s equation is used in DRAINMOD (Bouwer and van
Schilfgaarde, 1963).
q = 8Kdem + 4 Km2
CL2
where, q = flux (cm/hr)
(5)
m = midpoint water table height above the drain (cm)
K = lateral saturated hydraulic conductivity (cm/hr)
L = drain spacing (cm)
C = ratio of average flux between drains to the flux midway between
drains (assumed = 1)
de = equivalent depth from drain to restrictive layer (cm), (Calculated
using equation developed by Moody (1966))
When the ponded depth is larger than S1, Kirkham (1957) equation is used:
q = 4πK(t + b - r)
gL
(6)
where, t = ponded depth (cm)
b = distance from surface to drain (cm)
r = radius of drain (cm)
g = constant based on drain size, depth, and spacing and depth of profile.
Both equations depends on the amount of surface storage that is present and
assume drainage is limited by the rate of soil water movement to the lateral drains
but not the drainage coefficient. The drainage coefficient is a drainage design
input parameter that defines the drain’s capacity. When the flux calculated is
greater than the drainage coefficient, the flux is set equal to the drainage
coefficient.

Sub-irrigation is a form of water table management that provides both drainage
and irrigation requirements for crops with one subsurface system. It involves the
application of irrigation water below the ground surface by raising the water table
to within or near the root zone. The quality of the water must be evaluated to
determine suitability for the planned crop and soil before subirrigation is installed.
Figure 5: Schematic of Sub Irrigation (DRAINMOD Reference Report, 1980).
When subirrigation is part of the water table management system the equation
developed by Ernst is used to calculate for the drainage flux from the drain to the soil
profile (Skaggs,1980).
q = [4Km (2ho +ho)] /L2
Do
(7)
where, Do = yo + d
d = distance from the drain to the respective layer (cm)
yo = distance from the drain to the water table above the drain (cm).
DRAINMOD limits the drainage flux to the value of the drainage co-efficient which
is computed by Manning’s formula as:
DC = (864000 R2) / 2At
3S Adn
(8)
where DC is the drainage co-efficient (cm/day)
R = Hydraulic Radius (m)
S = Slope of tile
At = cross sectional area of the tile (sq. m)
Ad = area drained by the tile (sq. m)
n = roughness co-efficient of the tile.

Evapotranspiration is determined in two steps by DRAINMOD.
Evapotranspiration (ET) is computed from potential evapotranspiration (PET) which
is defined as the maximum amount of water that will leave the soil system by
evapotranspiration when there is sufficient supply of soil water. Actual ET is the
amount that can be supplied from the water table plus the amount available from the
unsaturated zone, hence it is limited by soil water conditions. DRAINMOD uses the
Thornthwaite (1948) method is used to calculate PET. This method uses the latitude
and heat index for the location along with daily maximum and minimum air
temperatures (Skaggs, 1982). The monthly PET is expressed as
ej = cTja
(9)
where, ej = monthly potential evaporation
Tj = monthly mean temperature
c and a are constants depending on the location and temperature

Rooting depth helps in defining the zone from which water can be removed when
necessary to supply for ET. Rooting depth in DRAINMOD is a function of Julian
date and since simulation process is continuous for several years, an effective
rooting depth is defined for all periods. The effective rooting depth for a fallow
soil is defined as the depth of the thin layer that will dry out at the surface.
Vertical/Deep Seepage
The portion of rainfall or applied irrigation water that in excess of the water holding
capacity passes through the rooting zone and is subsequently unavailable for crop use is
known as Deep Seepage. Deep seepage contributes a portion of base flow in the stream
flow. Vertical seepage losses become important when the restricting layer confines a
groundwater aquifer with a hydraulic head different from the shallow water table. If the
watershed has deep seepage, an application of Darcy’s law is used to calculate the flow
through the restrictive layer (Skaggs, 1980).
qv = Kv (h1 - h2) / D
(10)
where, qv = flux (cm/hr)
Kv = the effective vertical conductivity of the restrictive layer (cm/hr)
h1 = average distance from the bottom of the restricting layer to the water
table (cm).
h2 = the hydraulic head in the ground water aquifer referenced to the
bottom of the restrictive layer (cm)
D = thickness of the restrictive layer (cm).
METHODOLOGY
A. Designate Test Area (Animal Science Watershed, Tippecanoe County).
The study watershed is the 314 ha tile drained Animal Science Watershed, located in the
northwest portion of Tippecanoe County, Indiana near Lafayette. It is located west of
Purdue University and south of the Purdue University Animal Science Research and
Education Centre. It is entirely agricultural, with the main crop being corn and soil is
predominantly silt loam.
B. Obtain Data needed for analysis (refer to the MS thesis by Rhea Sammons, ABE
2002)
For this project DRAINMOD requires the following input data:
 General
 Climate
 Soil Properties
 Crop
 Drainage Design Parameters
 Seepage
General Input: The general input file consists of two categories – simulation options and
water management options. The sub-surface flow under water management is set to
Conventional.
Weather Input:
Rainfall
Temperature
Description Source
file
Description
Daily
Rainfall
Data
agro.rai
PET
Source file
Daily
Maximum
and
Minimum
Rainfall for March –
Temperature
November
was
collected
by
the
Marshall
Ditch
gauging
station
located directly south
of the watershed
outlet whereas the
rainfall for DecemberFebruary
was
collected
by
the
Lafayette
Gauging
station.
Description
Values
WLafAgro.TEM Thorne Waite Parameters
Compiled from
the
Indiana
Climate website Heat Index
47
PET
Month
January
February
March
April
May
June
July
August
September
October
November
December
PET Factor
5.67
5.00
2.84
1.98
1.51
1.26
1.12
1.08
1.09
1.20
1.45
2.86
Crop Input: Accurate simulation of hydrology requires input of land use data
including crop type. DARINMOD requires the following inputs:
Maximum effective Crop rooting Depth for corn = 2.5 feet
Cropping window and the growing season:
Crop
Corn
Planting Date
May 2, 2001
Harvest Date
Yield (l/acre)
October
20, 187 bu
2001
Acres Planted
132
(Source: MS thesis by Rhea Sammons, ABE 2002). The input file used for this
project is AScorn.cin
Drainage Design Input: The design of the drainage system was based on the Indiana
Drainage Guidelines. DRAINMOD requires the following data as part of the drainage
system design:
 Depth from the soil surface to the tile drain
 Spacing between Drains
 Effective Radius of the drains
 Distance from the surface to the impermeable layer
 Equivalent Depth of the tile drain to the impermeable layer and drainage coefficient (to calculate Kirkham’s co-efficient)
 Depth of the Restricting layer (cm)
 Initial Depth to water table.
Parameters
Drain Depth (cm)
Drain Spacing (m)
Effective Radius (cm)
Drainage Co-efficient (cm/day)
Depth of the Restricting layer (cm)
Initial Depth to Water table (cm)
Values
121
41.63
3.3
1.8
180
60
Seepage Design Consists of:
 Peizometric Head of the aquifer
 Thickness of the restricting layer
 Vertical conductivity of the restricting layer
Soil Input: Inputs for the soil data consists of soil moisture characteristic curve and the
saturated hydraulic conductivity. Soils located in the Animal Science watershed are
Drummer, Throckmorton, Peotone, Toronto-Millbrook (Toronto 1) and Toronto-Octagon
(Toronto 2). Soil permeability is a key factor governing the rate of water movement from
ditches to adjacent fields, and the upward movement of water from the water table to the
plant roots. Good lateral movement of water will occur in moderate to highly permeable
soils. While Evans and Skaggs (1989) recommended a permeability of about 0.45 m/day,
experience in eastern Canada has shown that water table management is also feasible on
soils with permeabilities of 0.3 m/day. A restrictive soil layer, not far from the bottom of
the subsurface drain pipes, will reduce deep seepage losses. The two dominant soil types
in the area of study are Drummer and Toronto1. The soil input files have been prepared
(refer to Rhea Sammon’s Thesis ‘ABE 2002) on the basis of Wiersma’84 datasets and
SSURGO soil data tables. The Wiersma data has been used to create graphs of the water
content curves from sample depth, bulk specific gravity (gms/cm3) for each sample depth,
percent water by weight versus the water tension for each sample depth. The saturated
hydraulic conductivity for each soil was found using the soil textures of the SSURGO
data tables.
C. DRAINMOD simulations were performed to compare and analyze the effect of two
different soil types (Drummer and Toronto 1) on drainage flow. After the generalized
simulations with different sets of design parameters another set of simulation was
performed for each type of soil with varying levels of deep seepage and the impermeable
layer at different depths in order to find out variation drainage flow with deep seepage
and depth of restricting layer.
D. Results and Discussion
The results of the different simulations can be categorized into three sections:
 Variation of drainage and seepage with respect to the input parameters
 Variation of drainage with Soil Types (Drummer and Toronto 1)
i)
Variation of the drainage and seepage with respect to the input
parameters
Drain Depth:
Drainage flow is directly related to drain depth. A very deep tile will drain a wide
area and hence the amount of flow will increase.
Variation of Seepage w ith Depth of Tile
Drain
Seepage (cm)
38. 555
38. 55
38. 545
38. 54
38. 535
Seepage
38. 53
38. 525
38. 52
38. 515
0
20
40
60
80
100
120
140
160
D e p t h ( c m)
Seepage is indirectly related to the drain depth. This is because as the tile drains are
placed deeper the amount of water to be drained increases. This leads to a reduction
in the amount of water that is available for seepage. Hence seepage reduces.
Drain Spacing:
Drain Spacing is inversely related to the amount of drainage. As the spacing between
the tiles increases, the amount of water available for drainage decreases. Tile drains
must be spaced more closely in slowly permeable material if the water table is to be
lowered in a reasonable time.
Seepage increases with increase in the spacing. This is because of the fact that
increase in spacing reduces the amount of water available to be drained. So the excess
water which could not be drained is lost through seepage in due course of time.
Initial Depth of Water Table
Variation of Drainage w ith Initial Depth of
Water Table
Drainage (cm)
0.025
0.02
0.015
Drainage
0.01
0.005
0
0
20
40
60
80
100
120
Depth (cm )
Increase in the initial depth to water table indicates low water availability (low water
table conditions). As such excess water which needs to be drained is also reduced.
Hence drainage reduces with increase in the depth to water table.
Seepage (cm)
Variation of Seepage with Iniatial Depth of
Water Table
40.5
40
39.5
39
38.5
38
37.5
37
36.5
36
35.5
Seepage
0
20
40
60
80
100
120
Depth (cm)
Low water table conditions indicate reduced availability of water, hence loss of water
through seepage is also reduced.
Effective Radius
Variation of Seepage w ith Effective Radius
of Tile Drain
Radius (cm)
45
40
35
30
25
Dr ai nage
20
15
10
5
0
0
1
2
3
4
5
6
Spacing (cm )
Both Drainage and Seepage are invariant with the effective radius of the tile drains.
Depth of the Restricting Layer
Variation of Drainage with Depth of the
Restricting Layer
Drainage (cm)
0.006
0.005
0.004
0.003
Draiange
0.002
0.001
0
0
50
100
150
200
Depth (cm)
Drainage increases with the increase in the depth of the restricting layer. The
restricting layer is assumed to have very low permeability which impedes the
movement of water. The deeper the layer is, more surface area can be drained which
in turn will increase the amount of drainage.
Variation of Seepage w ith Depth of the
Restricting Layer
Seepage (cm)
45
40
35
30
25
Seepage
20
15
10
5
0
0
50
100
Depth (cm )
150
200
Seepage also increases with increase in the depth to the restricting layer. This is also
for the same reason as drainage. Deeper restricting layer means more amount of water
available for drainage/ seepage.
Thickness of the Restricting Layer
Variation of Drainage w ith thickness of the
Restricting Layer
Drainage (cm)
0.012
0.01
0.008
0.006
Drainage
0.004
0.002
0
0
50
100
150
200
Depth (cm )
Drainage increases with increase in the thickness of the restricting layer. This
explained by the fact that a thick impermeable layer will allow less water to pass
though it than a thin impermeable layer. Hence the amount of water in excess is more,
hence drainage increases.
Variation of Seepage w ith thickness of the
Restricting Layer
Seepage (cm)
70
60
50
40
Seepage
30
20
10
0
0
50
100
150
200
Depth (cm )
Seepage increases with decreasing thickness of the restricting layer. This is because
more amount of water can pass through a thin layer of very low permeability than a
thick layer that is nearly impermeable.
Vertical Conductivity of the Restricting Layer
Variation of Drainage w ith Vertical Hydraulic
Conductivity of the Restricting Layer
Drainage (cm)
0.035
0.03
0.025
0.02
Drainage
0.015
0.01
0.005
0
0
0.05
0.1
0.15
0.2
Hydraulic Conductivity (cm /hr)
Drainage increases with decreasing vertical conductivity of the restricting layer.
Decrease in the vertical conductivity of the restricting layer results in low
permeability of the restricting layer. Hence water accumulates (as it is not able to pass
through) and is available for drainage. The lesser the permeability of the restricting
layer, more is the amount of water that accumulates.
Variation of Seepage with Vertical Hydraulic
Conductivity of the Restricting Layer
38.545
Seepage (cm)
38.54
38.535
38.53
38.525
Seepage
38.52
38.515
38.51
38.505
0
0.05
0.1
0.15
0.2
Hydraulic Conductivity (cm)
Seepage increases with increasing vertical conductivity of the restricting layer.
Increase in the vertical conductivity of the restricting layer results in higher
permeability of the restricting layer. Hence water passes through the restricting layer
easily, thus increasing the seepage.
ii)
Variation of the drainage with respect to the input parameters with different
soil types
Drain Depth
Variation of Drainage with Drain Depth for different Soil Types
0.035
0.03
Draiange (cm)
0.025
0.02
Drummer
0.015
Toronto 1
0.01
0.005
0
0
20
40
60
80
100
120
140
160
Depth (cm)
Drain Spacing
Variation of Drainage with Drain Spacing for
different Soil Types
0.07
Draiange (cm)
0.06
0.05
0.04
Drummer
0.03
Toronto 1
0.02
0.01
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Spacing (cm)
Initial Depth to Water Table
Variation of Drainage with Initial Depth to
Water table for different Soil Types
0.035
Draiange (cm)
0.03
0.025
0.02
Drummer
0.015
Toronto 1
0.01
0.005
0
0
20
40
60
80
100
120
Depth (cm)
It is obvious from the graphs above that under similar design parameters the drainage
flow varies with soil types. The drainage in the Drummer soil is less than that that of the
Toronto 1 soil type. This is because the hydraulic conductivity of Drummer (0.17 cm/hr)
is more than that of Toronto 1 (0.153 cm/hr). This makes Drummer more permeable and
leads to less drainage. The graph below explains this:
Drainage Flow in different soil types when all input parameters are similar
E. Calibrate the model.
Once the model is run with the default parameters the model needs to be calibrated.
Model calibration is required to standardize the model. Calibration serves to increase the
accuracy of a general model by temporarily making the model a specific model for a
specified situation. It also determines the deviation from a standard in order to compute
erroneous factors. Usually, calibration involves running the model with normal inputs
and comparing the output generated to the field data.
Comparison between observed and modelled drainage
data for Toronto Soil
1.00E+00
9.00E-01
8.00E-01
Drainage (cm)
7.00E-01
6.00E-01
5.00E-01
Modelled Toronto
4.00E-01
Observed Data
3.00E-01
2.00E-01
1.00E-01
0.00E+00
-1.00E-01 0
100
200
300
400
Julian Date
Drainage Flow in Different Soil Types
1.00E+00
9.00E-01
8.00E-01
Drainage (cm)
7.00E-01
6.00E-01
5.00E-01
Modelled Drummer
4.00E-01
Modelled Toronto
3.00E-01
controlled
2.00E-01
1.00E-01
0.00E+00
-1.00E-01 0
100
200
300
400
Julian Date
Calibrated Parameters:
Drain Depth: 150 cm
Initial Depth to Water Table : 80 cm
Vertical Conductivity of the restricting layer: 0.0000012 cm/hr
The PET values for certain months has been changed like Jan is 10, February is 8 Nov. is
5 and Dec is 15
Saturated Conductivity of Layer 4 for Drummer soil is 2.4 cm/hr and for Toronto Soil is
0.8 cm/hr.
The deviation of the model data from the observed data is more prominent in the winter
months. This is because of DRAINMOD’s limitation for not accounting frozen soil
conditions.
F. Perform a sensitivity analysis based on the results
Sensitivity Analysis
A sensitivity analysis is conducted to determine which parameters are sensitive to the
model. Sensitivity (S) is defined as the derivative of the model results with respect to a
parameter of interest. Values of S close to zero indicate small sensitivity while high
values of S indicate the sensitivity of the parameter. A negative S value means that the
parameter is inversely related to the model. Depending on the results of the generalized
simulations, sensitivity analysis for this project involves the following parameters:
 Drain Depth
 Drain Spacing
 Initial Depth to Water Table
 Depth to Restricting Layer
 Vertical Hydraulic Conductivity of the restricting layer
 Thickness of the Restricting layer
A linear sensitivity analysis has been used based on the linear sensitivity equation.
S = {(b2-b1)/(b2+b1)/2}/{(p2-p1)/(p2+p1)/2}
where, b1 and b2 are the corresponding output values for the parameters p1 and p2.
The range of the parameters tested and the sensitivity values computed are provided in
the tables below.
Table 2: Parameters for Sensitivity Analysis:
Parameter
Drain Spacing
Drain Depth
Effective Radius of Drain
Drainage Co-efficient
Initial Depth to Water Table
Depth to Restricting Layer
Thickness of the Restricting
layer
Vertical
Hydraulic
Conductivity of the restricting
layer
Unit
cm
cm
cm
cm/day
cm
cm
cm
cm/hr
Default
4163
121
3.3
1.8
60
180
71 (Drummer)
16(Toronto 1)
0.17 (Drummer)
0.153 (Toronto
1)
Range+
91.44-152
1500-4163
1.9-3.3
1-3.81
0-100
125-180
25-100
16
0.035-.17
+ Values have been referred from Indiana Drainage Guide and Rhea Sammon’s thesis
ABE’2002.
Table 2: Sensitivity Analysis with Soil Type: Drummer
Variables
Drain Depth
Drain Spacing
Values of
Input
Parameter
Min
Max
p1
p2
91.4 121
4
121
152
1500
2400
Initial Depth to
0
Water Table
60
Depth to Restricting 125
Layer
135
Thickness of the
25
Restricting
71
layer
Vertical Hydraulic
0.03
Conductivity of the 5
restricting layer
0.08
5
Drainage (cm) Sensitivity
(output)
Min
b1
0
Max
b2
0.01
0.01
2400
4163
60
100
135
Seepage (cm)
(output)
Sensitivity
7.19
Min
b1
36.89
Max
b2
36.87
0.02
2.94
36.87
36.86
0.06
0.02
0.03
0.01
0
0.02
0.01
0.01
0
0.01
-2.17
-1.67
-0.50
-3.00
26.0
36.82
36.85
39.6
36.87
31.19
36.85
36.87
36.87
33.6
32.11
0.002
180
71
0.01
0
0.01
0.01
0.00
2.08
32.11
32
36.87
36.87
0.38
0.15
100
0.01
0.01
0.00
36.87
39.94
0.24
0.085
0.04
0.02
-0.80
36.84
36.86
0.80
0.17
0.02
0.01
-1.00
36.86
36.87
1.00
-0.002
-0.001
0.001
-0.036
-0.19
1.00
Table 3: Sensitivity Analysis with Soil Type: Toronto 1
Variables
Drain Depth
Values of Input
Parameter
Min
Max
p1
p2
91.44
121
Drain
1500
Spacing
2400
Initial Depth 0
to
Water 60
Table
Depth
to 125
Restricting
135
Layer
Thickness of 16
the
96
Restricting
layer
Vertical
0.038
Hydraulic
Conductivity
of
the 0.076
restricting
layer
Drainage (cm)
(output)
Min
Max
b1
b2
Sensitivity
Seepage (cm)
(output)
Min
Max
b1
b2
121
152
2400
4163
60
0
0.005
0.03
0.02
0.02
0.005
0.01
0.02
0.005
0.005
7.12
2.94
-0.87
-2.23
-0.6
38.55
38.54
38.5
38.52
40.01
38.54
38.52
38.52
38.54
38.54
100
0.005
0
-4
38.54
36.18
135
0
0.005
1.9
180
0.005
0.005
0
30.26
38.54
96
0.005
0.01
0.45
38.54
51.38
144
0.01
0.01
0
51.38
59.26
Sensitivity
-0.0009
-0.0023
0.0019
0.001
-0.019
-0.126
30.74
30.26
1.02
0.52
0.2
0.36
0.076
0.03
0.01
-1.49
38.51
38.53
0.0008
0.153
0.01
0.005
-1
38.53
38.54
It should be noted that when using different base values, the sensitivities are different. In
different locations, with the change of input variables or parameters, sensitivities would
be changed too. This linear sensitivity assumes there is no interaction between input
variables or parameters.
Discussion of the Sensitivity Analysis
The drainage is highly sensitive to the following parameters:
 Drain Depth
 Drain Spacing
 Initial Depth to Water Table
 Hydraulic Conductivity of the Restricting Layer
 Thickness of the Restricting Layer
 Depth to the Restricting layer
It is seen from the sensitivity analysis that the drainage flow is highly sensitive to drain
depth and spacing. It is also highly sensitive to the vertical hydraulic conductivity of the
0.0004
restricting layer. It is moderately sensitive to the depth and thickness of the restricting
layer.
Other than the design parameters it was observed that the Soil Properties affects the
drainage flow to a great extent. The soil properties which affect drainage flow are
hydraulic conductivity, initial water content in a soil, surface compaction, depth of profile
and water table depth. Amongst these the effect of soil hydraulic properties and initial
water table depth was studied. Initial depth to water table affects both drainage and
seepage inversely. Increase in the depth to water table reduces the amount of water
available for drainage/seepage and hence drainage/seepage decreases. Hydraulic
Conductivity of the soils inversely affects drainage. The physical and chemical properties
of the two soils Drummer and Toronto 1 are given in Table 4. Drummer soils have a
higher hydraulic conductivity and allow water to pass through more easily than the
Toronto soils hence the lower drainage in Drummer. A decrease in the hydraulic
conductivity of the restricting layer makes the layer less permeable to flow and hence
increases drainage.
Table 3. Physical and chemical properties of selected soils.
Soil
Silt
Clay
Organic C
Cation exchange
capacity
_%_
cmolc/kg
Toronto
67.6
20.5
1.34
9.89
Drummer
66.2
21.2
2.91
27.1
Lee et al. (1997).
The amount of drainage also increases if the seepage losses are minimized. The factors to
which seepage is sensitive to are:
 Initial Depth to water table
 Depth of the Restricting layer
 Thickness od restricting layer
 Vertical Hydraulic Conductivity of the Restricting Layer.
Seepage decreases with decrease in the depth to the restrictive layer hence increasing
drainage flow. An increase in the thickness of the restricting layer decreases seepage and
thus increases the drainage flow. Decreasing the initial depth to water table will decrease
seepage as less amount of water will be available for seepage. Decreasing the vertical
hydraulic conductivity of the restricting layer makes the layer less permeable and hence
decreases the seepage. Decreasing seepage will increase in the amount of drainage flow.
CONCLUSION AND RECOMMENDATION
The study was conducted to evaluate the performance of DRAINMOD for simulating
subsurface drain flow taking deep seepage into consideration under different soil
conditions in the Animal Science Watershed. Analysis of the input parameters were
carried out and calibration processes for the hydrology were conducted. The results
strongly agreed with the established effects of different design parameters on subsurface
drain flow. The trend of the modeled data nearly matches with the trend of the observed
data. The outcome of this study is that the deep seepage loss contributes a portion of the
base flow to the stream flow. In order to simulate the observed results one needs to
consider the deep seepage losses. Deep seepage is affected by the depth, thickness and
hydraulic conductivity of the restricting layer. So varying the above three parameters will
vary the deep seepage losses. The best values for the parameters were used to calibrate
the model. Secondly it was also notes that soil hydraulic properties affect drainage.
Simulating the drainage flow with same input parameters for different soil types resulted
in variation of drainage and also seepage.
The model did not predict well for winter and early spring based on current inputs. This
may be because of DRAINMOD’s limitation of not accounting for frozen soil conditions.
This can be improved by considering the effect of snow and frozen conditions on soil
water processes by adjusting the monthly PET. The accuracy of predicting the flow in the
Animal Science Watershed strongly depended on inputs and outputs. The output results
can be refined by extensive sensitivity analysis of the sensitive parameters to determine
the level of sensitivity of each of these parameters and a realistic range of values for each
parameter can be established.
REFERENCES
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of Water Table in Drained Land, Transactions of the ASAE 6(4):288-291, 296.
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Systems. APPLIED ENGINEERING IN AGRICULTURE. 5(4):539-548.
Indiana Farm Drainage Guide, ID-55, Agricultural Eng. Dept., Purdue University
Kirkham, Don, 1950, Potential Flow into Circumferential Openings in Drain
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Lee L.S., Nyman A.K., Hui L., Nyman M.C., Jafvert C., 1997, Initial sorption of
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DRAINMOD, M.S. Thesis, Purdue University.
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