Directory UMM :Data Elmu:jurnal:A:Agricultural Water Management:Vol43.Issue3.Apr2000:
Agricultural Water Management 43 (2000) 285±309
Gleams model application on a heavy
clay soil in Finland
Walter G. Knisela,1, Eila Turtolab,*
a
Biological and Agricultural Engineering Department, University of Georgia,
Coastal Plain Experiment Station, Tifton, GA 31793, USA
b
Institute of Crop and Soil Science, Agricultural Research Centre of Finland,
Jokioinen, FIN-31600, Finland
Accepted 14 July 1999
Abstract
The GLEAMS model version 2.10 was calibrated and validated with data from research plots on
illitic clay soil near the Agricultural Research Centre at Jokioinen, Finland. Observed surface
runoff, drainage ¯ow, erosion, and associated nitrogen and phosphorus loads from 0.46 ha plots
were used in the model application for a 7-year period with different management practices. Two
plots were used for calibration and two plots were used for validation in the present study. The
model was found to represent the soils and management with adjustment, or ®ne tuning, of sensitive
parameters. The simulated runoff, percolation, evapotranspiration, amount of soil erosion,
phosphorus (P) and nitrogen (N) losses in runoff and drainage water compared very well with
observed values on the average, but differed considerably from year-to-year and especially monthto-month. Observed data were required after improved drainage installation in order to adjust
parameters sensitive in water balance calculations. The P component of the model gave better
estimates of the losses in runoff and with eroded soil particles than did the more complex N
component. P with particulate in drainage water was simulated externally since it is a signi®cant
part of the total P lost and it is not considered in the model. Some model modi®cations were made
to better represent the climatic conditions of Finland. The validation study indicated that GLEAMS
can be used satisfactorily in Finland for comparisons of alternate management practices as
recommended by the model developers. # 2000 Elsevier Science B.V. All rights reserved.
Keywords: Surface runoff; Drainage ¯ow; Erosion; Nitrogen leaching; Phosphorus loss
*
Corresponding author. Tel.:358-3-41881; fax: 358-3-4188437.
E-mail address: eila.turtola@mtt.® (E. Turtola).
1
Retired, formerly Senior Visiting Research Scientist.
0378-3774/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 0 6 7 - 0
286
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
1. Introduction
Agriculture is the main source of phosphorus (P) and nitrogen (N) to fresh and coastal
waters of Finland (Rekolainen, 1989; Rekolainen et al., 1995), and the contribution from
agriculture has been increasing. Due to accelerated eutrophication of surface waters,
knowledge of best management practices is urgently needed to reduce the nutrient loads.
However, it is impossible to conduct field research in all aspects of alternate cropping
practices to develop feasible management systems for nonpoint source pollution control.
An alternative is to use available research data to validate comprehensive computer
models, and then use the models in a simulation mode to examine the long-term
consequences of alternate management systems. Rekolainen and Posch (1994) modified
the CREAMS model (Knisel, 1980) for Finnish conditions. The modified model was then
used to estimate the effect of different tillage intensities on soil loss in surface runoff
(Rekolainen et al., 1993). However, CREAMS only considered surface response of P, and
Turtola and Jaakkola (1995) found considerable leaching of dissolved orthophosphate-P
in drainage water.
High shrink/swell-capacity clay soils, such as in Finland, exhibit extensive shrinkage
cracks at the soil surface during prolonged dry periods. The cracks may have pronounced
effects on surface runoff and percolation below the root zone, and crack flow, or
preferential flow, may significantly affect chemical movement as well (e.g. Dekker and
Bouma, 1984).
GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) is a
mathematical model to simulate the complex climate±soil-management interactions for
field-size areas. It was developed to evaluate edge-of-field and bottom-of-root-zone
loadings of water, erosion material, and agricultural chemicals from alternate management systems (Leonard et al., 1987). Later a component was added to the model to
simulate relatively comprehensive nitrogen and phosphorus cycles in the soil (Knisel,
1993). The principal use of the model is to evaluate the differences among management
systems rather than to predict the absolute quantities of water, soil erosion, and chemicals
lost from the field. Examples of GLEAMS applications to assess management
alternatives are numerous (e.g. Leonard and Knisel, 1989; Reck, 1994; Smith et al.,
1994).
When developers formulate comprehensive models, such as GLEAMS, to represent the
complex interactions of the many physical and chemical processes, they use readily
available data to test the validity of the model concepts. These data are very limited when
considering the extreme number of possible combinations of soils, climate, and
management scenarios in all regions of the world. The processes themselves are
obviously the same, but the degree of interactions may be extremely different. For
example, a drainage model developed for high water-table conditions in a soil/climatic
region where little surface runoff occurs would have more emphasis on internal water
movement than on direct runoff, e.g. DRAINMOD (Skaggs et al., 1995). That model
would not have been tested initially in every drainage situation for all light- and heavytexture soils in cold and warm climatic regions with different tillage practices where
water retention/viscosity characteristics differ. The reverse is also true. Surface runoff
from snowmelt on soils frozen at least a portion of the time is vastly different from direct
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
287
runoff from thunderstorms in summer time on soils that are not frozen, or may never
freeze, to change the bulk density and water retention. After initial testing, if a model
appears adequate for its intended purpose and comprehension, evaluation under other
conditions may be made as data become available. Reporting of results Ð good, bad, or
indifferent Ð allows potential users to review the validity for possible model
applications. The alternative is to develop a model for every soil in every climatic
region for every management scenario resulting in millions of models that cannot be
extrapolated beyond their developmental conditions. The purpose of this paper is to
present the results of testing the GLEAMS model, developed in the USA, for high
organic-matter clay soils in the much colder climatic region in Finland. Results of
validation and fine-tuning of sensitive parameters of the model are presented, using
research data for a heavy clay soil at the Agricultural Research Centre of Finland.
2. Model description
The GLEAMS model consists of four components operating simultaneously:
hydrology, erosion/sediment yield, pesticides and plant nutrients. Only a brief description
of the model is given here, but more details can be found elsewhere (Knisel, 1980;
Leonard et al., 1987; Knisel, 1993). Additional detail will be given in a later section on
model modifications for those components changed for preferential flow representation.
2.1. Hydrology
Daily water accounting is simulated in a soil system layered within the genetic
horizons of the root zone. The model distributes soil characteristics into a maximum of 12
computational layers with input from a maximum of 5 soil horizons. Daily potential
evapotranspiration is estimated by the Priestly±Taylor (Priestly and Taylor, 1972) or
alternatively the Penman±Monteith methods (Monteith, 1965). Soil water uptake by a
crop is simulated as a two stage process with ET occurring at potential when water
content is greater than 25% plant available. Runoff is calculated using a modified Soil
Conservation Service curve number procedure (Williams and LaSeur, 1976). The
modification mainly consisted of replacing the 5-day antecedent rainfall with available
soil water storage, and making the procedure a daily simulation rather than a design-type
storm. Percolation through the soil layers uses a storage-routing technique (Williams and
Nicks, 1982).
Rekolainen and Posch (1994) modified the CREAMS model (Knisel, 1980) hydrology
component to better represent the Finnish climate. Mean daily temperature was used to
determine the state of the day for designating snow or rain in the precipitation file. This
adaptation is included as an option in the present version of GLEAMS (Knisel, 1993).
Rekolainen and Posch (1994) also included a simple soil frost model and an adjustable
albedo for simulating evapotranspiration. In GLEAMS version 2.10 (Knisel, 1993), soil
temperature is simulated, and is used to determine when the soil is frozen, at which time
the available soil water storage is reduced. Soil temperature is a function of mean daily
air temperature, 5-day average soil temperature, soil water content, soil depth, and soil
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W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
cover, i.e. snow, crop residue, growing crop canopy, or bare soil. These changes closely
represent some of the modifications made in CREAMS to represent Finnish conditions
(Rekolainen and Posch, 1994).
An irrigation component allows the model user to specify the threshold soil water
content in the active root growth layers for automatic model irrigation and to specify the
water content to which the soil water is to be raised. If irrigation is applied on research
plots, the depth of irrigation water is entered in the precipitation file for the day of
application.
2.2. Erosion
The erosion component of GLEAMS is the Onstad±Foster (Onstad and Foster, 1975)
modification of the universal soil loss equation (USLE) (Wischmeier and Smith, 1978)
for storm-by-storm simulation. Rill and inter-rill erosion are calculated on the nonuniform slope of a representative overland flow element of the field. The erosivity factor
(R) of the USLE is replaced by storm-by-storm rainfall energy calculated from daily
rainfall. The management factors of the USLE, i. e. soil loss ratio (C factor) and practice
factor (P), are maintained. In addition to the overland flow element, concentrated or
channel flow can be represented in the field. Although branching channels are not
considered, two channel sequences such as a terrace channel followed by a terrace outlet
channel can be represented in GLEAMS. A pond element also can be included to
represent temporary ponding, or an impoundment, that drains shortly after runoff ends,
such as an impoundment terrace, gully plug, or temporary ponding caused by restricted
outlet conditions for runoff plots with a weir or flume measuring device. Soil particles
and organic matter detached by raindrop impact are routed through the delivery sequence
of the field (Foster et al., 1985). A characteristic discharge, calculated from the storm
runoff peak rate simulated at the field outlet in the hydrology component, is used to
calculate transport of soil particles. The characteristic discharge is translated back to the
uppermost element and is used to calculate particle transport capacity and deposition of
each computational segment. Erosion/sediment yield and the associated sediment
enrichment ratio (ratio of the specific surface area of the eroded soil particles to the
specific surface area of the original soil) is calculated at the end of each flow element and
at the outlet (edge) of the field (Foster et al., 1980).
Posch and Rekolainen (1993) modified the rainfall energy computation in CREAMS
for Finnish conditions. They fitted monthly coefficients and exponents for different
locations in Finland during the growing season, and these were entered into the model to
give a better comparison between simulated and observed erosion. Their data were used
to approximate a polynomial and calculate a factor to adjust rainfall energy as a function
of latitude. The factor lowered the estimating relationship in CREAMS (Foster et al.,
1980) at 45E latitude (LAT) to a minimum of 20% at 65E latitude without changing the
relationship itself. The polynomial expression for the adjustment factor (FAC) is:
FAC 1.0
FAC 0.146(LAT) ÿ 0.00169(LAT)2 2.14
FAC 0
for 08 LAT < 458
for 458 LAT < 658
for 658 LAT < 908
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
289
The factor is multiplied by the rainfall energy as given by Foster et al. (1980), and the
shape of the energy relationship does not change from south to north.
2.3. Plant nutrients
The plant nutrient component of GLEAMS (Knisel, 1993) considers comprehensive
N and P cycles. Much of the nutrient component is very similar to the EPIC model
(Sharpley and Williams, 1990) except the animal waste component. The N cycle
includes: mineralization, immobilization, denitrification, ammonia volatization, nitrogen
fixation by legumes, fertilizer and animal waste application, crop uptake, and
runoff, erosion, and leaching losses. Mineralization is treated as a two-step process:
first-order ammonification, and zero-order nitrification. The ammonification is consistent
with animal waste loadings and ammonia volatization. The P cycle includes:
mineralization, immobilization, fertilizer and animal waste application, crop uptake,
and runoff, erosion, and leaching losses. Inorganic fertilizer application considers surface
and incorporated as well as fertigation. Animal waste, with specification of nutrient
content, may be represented as surface, incorporated, injected, or liquid effluent
applications. Organic fractions of animal waste N and P are maintained as separate
fractions that mineralize with different rate constants from those for fresh organic N
and P in crop residue or mineralizable soil N and P. Tillage and soil temperature
algorithms are included in the nutrient component. The tillage component incorporates
crop residue, animal waste, and fertilizer, and mixes the respective pools in the ploughed
layers. Ammonification, nitrification, denitrification, volatization, and mineralization
rates are adjusted by soil temperature and water content in the respective computational
soil layers. Rainfall N is an input for the model application site, and N and P in irrigation
water can be considered for locations where concentrations in the water supply may be
significant.
2.4. Pesticides
A pesticide component is included in the GLEAMS model (Leonard et al., 1987), but a
description is not included here since pesticides are not a part of the present study.
3. Model calibration and validation
The GLEAMS model nutrient component had minimal validation with field data in the
USA by Knisel (1993), and no validation of the component has been done outside the
USA. Climate, soil, and management practices vary drastically from region-to-region and
country-to-country. Therefore, it is always recommended that any available local data be
used to calibrate the model for local conditions and adjust, or ``fine-tune'', sensitive
parameters where possible, to assure the model is operating within the proper range. After
calibration, GLEAMS can then be used to compare alternative management practices
over a long-term climatic record.
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W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
Table 1
Soil physical characteristics and chemical properties of the experimental ®eld near the Agricultural Research
Centre of Finland
Depth
(mm)
Particle size fractions (mm)
0.02
0±200
200±400
400±600
61
83
90
16
8
6
23
9
4
a
b
Sat. hydr.
conda (mm/h)
Depth
(mm)
620
6.3
0.064
0±250 5.8
250±600 6.3
600±900 6.7
pH
Org.C
(%)
Total
N (%)
Soluble
Pb (mg/l)
2.6
0.6
0.5
0.19
0.05
0.04
3.2
0.4
0.3
Laboratory measurement with undisturbed soil columns,ù 150 mm (Aura, 1990).
Acid ammonium acetate (pH 4.65) extractable P (Vuorinen and MaÈkitie, 1955).
3.1. Description of research plots
The research plots used to calibrate and validate the GLEAMS model were located at
the Agricultural Research Centre near Jokioinen in southwest Finland on a south-facing
field with a mean slope of 2%. The soil at the site was classified as silty clay or heavy
clay in the plough layer and as heavy clay in the subsoil according to the Finnish textural
classification, and classified taxonomically as Vertic Cambisol (FAO, 1988), and very
fine Typic Cryaquept (Soil Survey Staff, 1992).
Soil characteristics of the plots are given in Table 1. The mean saturated hydraulic
conductivity of the plow layer was 620 mm/h, which is in the normal range for Finnish
clay soils, 500±2000 mm/h (Aura, 1990). Saturated conductivity of the subsoil was
drastically lower, decreasing to less than 10 mm/h. As shown in Table 1, the soil was low
in plant available P with only a slight change from the 250±600 mm depth to that for the
600±900 mm depth. The differences in depths shown in the table between physical and
chemical properties were due to different sampling dates and procedures and not because
of different genetic horizons at the respective sampling locations.
Subsurface drain tiles were installed in the field in 1962 with a 16.5 m spacing at a
depth of approximately 1 m. In 1975 (prior to the research study which began in 1987),
the drains were cut into 33 m lengths, forming 16 drainage plots, each with two drains
and an area of 0.11 ha (Jaakkola, 1984). In June 1991, during the study period, subsurface
drainage was improved by installing plastic drain tubing at the same depth but at a 0.3 m
distance laterally from the old drain tile (Turtola and Paajanen, 1995). Percolate from the
drain tiles and tubing was conducted by pipe to an observation building where the volume
was measured with a recording tipping bucket installation. Flow-weighted samples were
collected for chemical analyses.
Four adjacent surface-runoff plots were established in 1975, each with 0.46 ha
drainage area and containing four subsurface-drainage plots. The plot borders consisted
of soil dikes, or ridges (embankments), that contained the runoff which drained to the
lower plot corner. A small channel with a gravel bottom at the lower end of the plots
collected the runoff which then drained through the gravel into a plastic pipe leading
to a measuring device in an observation building. Runoff from snowmelt generally
flowed directly into the collection channel, but small runoff volumes normally drained to
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
291
the lower plot corner into the channel. Surface runoff was measured with a recording
tipping bucket installation, and flow weighted samples were collected for chemical
analyses.
Percolate from the four drains in each surface plot was summed to determine the total
subsurface flow for the 0.46 ha area. This was necessary to obtain unit runoff and
subsurface flow for the same plot area.
Runoff and drainage samples were analyzed for total solids, representing soil erosion
(TS), total phosphorus (TP), dissolved orthophosphate-phosphorus (PO4-P), total nitrogen
(TN), nitrate-nitrogen (NO3-N), and ammonium-nitrogen (NH4-N). Description of the
analytical laboratory methods is beyond the scope of this paper, but details are given by
Turtola and Paajanen (1995). Analyses of the drainage water were aggregated for the four
drain pipes in each surface plot for unit area comparisons.
The study period for model calibration and validation was 1987 through 1993. Plot
cropping practices with summer and following winter soil cover conditions are given in
Table 2 for the study period. Spring-planted small grains constitute the major crops of the
region, and the barley (Hordeum vulgare) shown in Table 2 is spring barley.
3.2. Model calibration
The GLEAMS model was developed to represent management systems. These include
crop rotations, tillage practices, conservation practices, irrigation, drainage, fertilizer
practices, and pesticide treatments, among others. Knisel et al. (1995) discussed effects of
different management practices on model output. GLEAMS can represent several
different systems in a single simulation. However, some systems, such as drain tile
installation, results in different base parameters, and therefore requires stopping
simulation and beginning a new with different saturated conductivity values for drainage.
Two plots were selected for model calibration (fine tuning of sensitive parameters) and
two plots were then used for model validation. Plots A and D were arbitrarily selected for
model calibration, leaving plots B and C for validation.
Model calibration on plots A and D was made in three parts to correspond with the
cropping practices and improved drainage system. Although the model can represent a
crop rotation in a simulation run, it was considered desirable for calibration to simulate
the 1987±89 period for plot A which was maintained in a continuous bare fallow
condition. The cropping period on plot A, 1990±93 (Table 2), was interrupted by the
installation of the improved drainage system in June 1991. Thus, for comparative
purposes, the model simulations on the two plots were made for three periods: (1) 1987±
89, (2) 1990±May 1991, and (3) July 1991±1993.
Soil samples were obtained from the research plots during the 1987±89 record period
for laboratory analyses of physical characteristics (Aura, 1990) and chemical content.
Samples for physical analyses were taken at two locations in each of plots A, B, and D,
and at four locations in plot C. For chemical analyses, samples were taken at four
locations in all plots with five sub-samples per location. The samples represent the preimproved drainage period. Although the matrix soil, on which the laboratory analyses
were made, did not change, the improved drainage of 1991 imposed significantly
different drainage boundary conditions.
292
Year
Plot
1987
1988
1989
1990
1991a
1992
1993
a
b
A
B
C
D
Bare fallow (ploughed)
Bare fallow (ploughed)
Bare fallow (ploughed)
Spring barley (ploughed)
Spring barley (ploughed)
Timothy (timothy)
Timothy (ploughed)
Bare fallow (ploughed)
Spring barley (ploughed)
Bare fallow (ploughed)
Spring barley (ploughed)
Spring barley (ploughed)
Timothy (timothy)
Timothy (timothy)b
Ryegrass (ploughed)
Spring barley (ploughed)
Ryegrass (ploughed)
Spring barley (ploughed)
Spring barley (ploughed)
Timothy (timothy)
Timothy (ploughed)
Timothy (timothy)
Timothy (timothy)
Timothy (ploughed)
Spring barley (ploughed)
Spring barley (ploughed)
Timothy
Timothy (timothy)b
Subsurface drainage improvement in June, 1991.
Timothy residue; timothy killed with Glyphosate on August 20, 1993.
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
Table 2
Summer crop (the following winter soil cover in parenthesis) on the experimental plots, 1987±93
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
293
Observed soil physical and chemical characteristics (Table 1) were used to develop
initial parameter files for the GLEAMS model. Water retention and drainage
characteristics of soils were determined on samples in the laboratory rather than in situ
field measurements. Since undisturbed soil cores have different boundary conditions from
those in the field, the laboratory-measured values may differ significantly from integrated
field values. Also, model formulations of field processes are not exact and complete. For
example, a single representative value of soil porosity for each soil horizon is used in
GLEAMS without adjustments for changes in water content or frozen-soil conditions as
they are known to occur in the field. Infiltration of rainfall into the soil is not considered
per se, but is a part of the rainfall-runoff relationships (Williams and LaSeur, 1976). Thus,
laboratory derived characteristics must be adjusted in the model to achieve the best
comparison with observed data.
Results of the water balance simulation were compared with observed runoff and
drainage data. The hydrology parameters sensitive in water balance calculations, i.e. soil
porosity, field capacity, and curve number, were adjusted and fine tuned to achieve the
best agreement between total model-simulated and observed runoff and drainage flow
during each period of comparison on each plot. Simulated annual evapotranspiration (ET)
was compared with observed annual ET, with observed values determined by subtracting
combined runoff and drainage flow from total annual precipitation without considering
changes in soil water storage in the root zone from the beginning to the end of the year.
Rooting depth is a sensitive parameter in the GLEAMS water balance calculations, but
the drain tile is approximately 1 m below the land surface, this depth was used as the
rooting depth for comparisons of percolation and leaching.
GLEAMS uses a ``current crop rooting depth'' (CCRD) that may be less than the
effective rooting depth (RD) to reflect uptake of water and chemicals for shallow rooted
crops in rotation. For example, potatoes (Solanum tuberosum) generally root only to the
bottom of the plough layer (perhaps 200±300 mm) compared with deep rooted crops such
as grasses or cereal grains which may have an effective root depth of 600 mm or more.
The model does not simulate water and nutrient uptake below the CCRD, but water and
chemicals move through the lower depths. Therefore, RD for model simulation was set at
1000 mm, and CCRD 600 mm was used for the spring barley, ryegrass, and timothy
grown on the plots.
The best estimates of erosion/sediment yield cannot be obtained until the hydrology
components are fine tuned to achieve the best estimates of water balance components. Likewise, the proper pathways and quantities of nitrogen and phosphorus cannot
be achieved until the water balance and sediment yield were finalized. Nitrate leaching is
a function of the soil nitrate as well as the volume of percolation through the root zone. If
water retention characteristics and initial soil NO3-N are adjusted in the same model run,
the relative effects on nitrate leaching could not be evaluated. Therefore, sequential fine
tuning was performed to obtain the best results in a systematic manner.
After water balance simulations were fine tuned, similar procedures were used for the
erosion component. Erosion parameter sensitivity is site specific since flow sequence and
topography may alter their relative effects. In the present application on the research
plots, an overland flow-pond sequence was simulated to depict the temporary ponding of
water inside the plot boundary resulting from the channel and restrictive pipe conveyance
294
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
of runoff to the observation building. Therefore, the normally sensitive overland flow
parameters (soil loss ratio, P-factor, and Manning's `n') are dominated by the pipe
drainage characteristics. Soil loss ratios and Manning's `n' values were adjusted, but the
practice factor was well-defined because plowing and sowing operations were up-anddown the plot slope.
Initial N and P content of the soil horizons and potential crop yield are the most sensitive
plant nutrient parameters. Such sensitive parameters as depth of fertilizer application (tillage)
were not fine-tuned since one approximate depth was given. However, this can be a source of
discrepancy between model-simulated and observed nitrate-nitrogen leaching. For example,
if the bottom of the depth of tillage is only 5 mm into a computational soil layer, the model
assumes tillage or fertilizer incorporation occurs throughout the soil layer which may be as
much as 100 mm thick. Depending upon how near that layer is o the RD or CCRD, the
fertilizer placement or mixing of mineralizable N and P by tillage may significantly affect
simulation results. This is cited merely to point out that parameter sensitivity may not always
be clearly evident and exact in any model, not just GLEAMS.
Irrigation was applied on plots only during the 1987±89 period. Since irrigation depths
were known, the amounts were included in the precipitation file on the actual dates of
irrigation. This could not be considered as a part of the irrigation option in GLEAMS
since the model calculates the amount of water to be applied is based upon prescribed soil
water content rather than a specified amount.
3.3. Model validation
Model validation was made on plots B and C using (1) average state parameters from
calibration on plots A and D without adjustment, (2) laboratory-determined values for
porosity, water retention characteristics, saturated hydraulic conductivity, and plant nutrient
measurements, and (3) default values (model data base, averaged for all soils) without
adjustment. Simulations were each made for the two periods, January 1987±May 1991 and
July 1991±December 1993. Improved drainage was installed in June 1991, and this is a
management change even though the matrix soils did not change. This method of validation
provides three independent simulations on which to draw conclusions about the GLEAMS
model applicability on clay soils in Finland. It includes the recommended methods of
application, i.e. using measured data where available, averages from locally available data, or
as a last resort, averages for all soils-topography-management scenarios. Changes in
cropping practices on plots B and C during 1987±91 were represented in a single simulation
on each plot as opposed to the calibration simulations for plots A and D.
Results of the calibration and validation simulations are discussed in the following
sections of this paper.
4. Results
4.1. Model calibration
Continuous bare fallow conditions on plot A for 1987±89 (Table 2) offer a unique
opportunity to examine climate±soil interactions without crops. However, representing
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W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
Table 3
Observed and GLEAMS-simulated calibration water balance, erosion, and nutrient losses, plot A, 1987±93
Component
a
Precipitation (mm)
Runoff (mm)
Drainage (mm)
Evapotranspirationb (mm)
Erosion, runoff (kg/ha)
Erosion, drainagec (mm)
N loss, runoff (kg/ha)
Soluble N loss, drainaged (kg/ha)
N loss, runoff drainage (kg/ha)
P loss, runoff (kg/ha)
P loss, drainagee (kg/ha)
P loss, runoff drainage (kg/ha)
1987±May 1991
July 1991±93
Total, 1987±93
Simulated
Observed
Simulated
Observed
Simulated
Observed
2748
866
484
1373
7280
1922
32.7
25.6
58.3
6.06
1.73
7.79
2760
822
497
1441
7206
1162
31.8
50.4
82.2
4.40
1.94
6.34
1545
178
544
809
560
2650
3.9
37.1
41.0
0.55
1.44
1.99
1560
149
595
816
695
2805
3.3
34.1
37.4
0.56
1.91
2.47
4293
1044
1028
2182
7840
4572
36.6
62.7
99.3
6.61
3.17
9.78
4320
971
1092
2277
7901
3967
35.1
84.5
119.6
4.96
3.85
8.81
a
Simulated precipitation includes rainfall and snowmelt.
Observed evapotranspiration calculated as: precipitation±runoff±drainage.
c
Simulated erosion calculated externally from GLEAMS.
d
Simulated for 1 m root zone to compare with ef¯uent from drains at 1 m depth.
e
PO4-P particulate P loss, simulated particulate P loss calculated externally from GLEAMS.
b
these conditions is not without difficulty. What does a rooting depth represent when there
is not a crop growing for extended periods (3 years)? There are no decaying plant roots
that affect infiltration during short-term fallow periods. Models such as GLEAMS are
formulated to consider short-term fallow interspersed between crops but not long-term
fallow.
Results of the GLEAMS model simulations are compared with observed values for plot
A in Table 3. Two periods are shown in the table for comparisons: 1987±May 1991 prior
to improved drainage and July 1991±1993 after improved drainage. Runoff was overestimated some years and under-estimated some years (Fig. 1) with corresponding underand over-estimates of percolation (Fig. 2). Total runoff was over-estimated in both
periods and percolation was under-estimated as well as ET.
Most of the observed and simulated runoff occurred from spring snowmelt, and thus it
would appear that the snow accumulation and melt simulations are not adequately
represented in the model. The simple frozen-soil representation in GLEAMS (Knisel et
al., 1985) does not consider the effects of soil water content at the time of freezing on the
conductivity of water into, within, and through the profile.
Simulated erosion/sediment yield agreed very well for the 1987±91 period but was
over-estimated for 1991±93 with good agreement with observed values for the total 7
year study (Table 3). The high content of illitic-mineral clay in the Finnish soils is
significantly different from those used in the development of the USLE (Wischmeier and
Smith, 1978). Soil erodibility used in the model may be in error since local data are not
available. Also, since most of the erosion occurs from snowmelt runoff, soil surface
condition is an important factor in estimating detachment of soil particles by runoff. The
GLEAMS modification of rainfall energy described earlier under model description is
296
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Fig. 1. Cumulative simulated and observed runoff, plot A.
Fig. 2. Cumulative simulated and observed percolation (drainage), plot A.
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
297
dominated by the runoff-sediment transport conditions, i.e. temporary channel ponding
and drainage through the gravel into the pipe leading to the measuring device. The
temporary ponding allows deposition of soil aggregates and course particles as well as
some suspended sediment. The runoff drainage-outflow conditions are difficult to
represent in GLEAMS. Temporary ponding can be represented in the model, but an
equivalent pipe diameter is required to represent the combined gravel and pipe drainage.
The pipe diameter is a sensitive model parameter for sediment-yield simulation. Also,
continued deposition of sediment over the gravel with each runoff event on the field plots
resulted in continuously changing drainage restrictions. This required adjustment of the
sensitive model parameter to give the best estimate of sediment yield for comparison with
observed values for the entire simulation period.
Considerable soil particles and particulate P were observed in drainage water from
the research plot (Turtola and Jaakkola, 1995; Turtola and Paajanen, 1995) as shown in
Table 3, but such transport is not simulated by the GLEAMS model. Suspended
particlulate matter is generally observed from drainage of heavy clay soils as found by
Bengtsson et al. (1992) in a subdrained basin in southern Finland. Turtola and Paajanen
(1995) reported concentrations of particulate in drainage water varied seasonally with an
average of about 0.4 mg particulate/L of drainage during the study period. The particulate
in drainage water is related to shrinkage and cracking of the clay soils upon drying.
Drying of the soil profile occurs immediately over the drainage pipe where the water table
(free water in the profile) is the lowest. Subsequent rains may move soil particles and
organic matter from the soil surface into the cracks or other macro-pores and to the
drainage pipes early in a rainfall event before the soil matrix is transmitting water.
Tillage, or other management practices, may obliterate the macro-pores at the surface of
the soil, and thereby prevent macro-pore flow from transmitting particulate to the
drainage pipes. These interacting phenomena and macro-pore flow are not included in the
GLEAMS model, and particulate drainage is not possible. An external model was
developed by Shirmohammadi et al. (1998) to estimate daily particulate mass in drainage
flow. Average particulate concentration (averaged over all seasons for all plots, 0.4 mg/l)
and the daily drainage flow, or percolation volume, were used to calculate particulate
mass. Daily percolation was obtained directly from GLEAMS for input into the external
calculation procedure. This is the value shown in Table 3 for erosion in drainage water for
plot A.
Simulated total N in runoff compared very well with observed values during both
periods (Table 3, Fig. 3). NO3-N leaching was under-estimated by a factor of 2 in the
1987±91 period (Table 3), which occurred mainly during the 1987±89 bare-fallow period
(Fig. 4). The under-estimate of NO3-N leaching resulted from a high estimate of
denitrification. Crop uptake of N is usually the largest component of the N cycle, but
without a growing crop during the bare fallow period, denitrification probably was overestimated. Simulated and observed leaching compared fairly well after improved
drainage.
Simulated P losses for plot A compared relatively well with observed values during
both periods. The largest discrepancies for losses in runoff ocurred in 1987 when erosion
was over-estimated by a factor of 3, and for losses in both runoff and drainage in 1990
(Figs. 5 and 6). Particulate P in drainage water, simulated externally by the procedure
298
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
Fig. 3. Cumulative simulated and observed nitrogen loss in runoff, plot A.
Fig. 4. Cumulative simulated and observed nitrate-nitrogen loss in drainage, plot A.
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
299
Fig. 5. Cumulative simulated and observed phosphorus loss in runoff, plot A.
described previously, compared very well with observed values. Total simulated P losses
for the 7-year period agreed well.
The July 1991±December 1993 simulated and observed data for plot A are shown in
Table 3. Model parameters for field capacity (FC), wilting point (WP), and saturated
conductivity (SATK) required significant adjustment from those for the 1987±91 period
Fig. 6. Cumulative simulated and observed phosphorus loss in drainage, plot A.
300
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
to simulate the high volumes of observed drainage flow. During installation of the new
drainage system, the trenches above a gravel envelope around the tubes were back filled
with wood chips in the lower part of the trenches and with loose topsoil in the upper part
of the trenches to the approximate plough depth. The artificially filled trenches, four in
each plot, possibly intercepted some lateral subsurface flow that would not have reached
the old drainage tubes. Some of the lateral subsurface flow intercepted by the back-filled
trenches could have drained into the surface runoff collector ditch under the old drainage
regime. Improved drainage outlet can result in less water held in the soil against the force
of gravity, which is the definition of field capacity. If this is true, the adjustments of
parameter values for field capacity and saturated conductivity are justified to adequately
represent the new drainage regime. The relatively good agreement between simulated and
observed 3-yr totals of water, sediment, and nutrient losses shown in Table 3 were
obtained with these adjustments. In terms of soil characteristics, the soil itself did not
change from the old drainage system to the new, and the same parameter values would be
expected. These changes indicate that even though most of the GLEAMS parameters are
physically based, some parameters must also represent treatment, such as drainage in this
application. These important adjustments were made in the calibration simulations, but
they are extremely difficult when data are not available.
Simulated runoff was significantly under-estimated on plot D for the period before
improved drainage, but agreed very well after drainage (Table 4). Simulated and observed
drainage agreed well for both periods. The under-estimate of runoff resulted in an overestimate of ET prior to improved drainage. Erosion was under-estimated since runoff was
under-estimated prior to improved drainage, but compared favorably after drainage.
Table 4
Observed and GLEAMS-simulated calibration water balance, erosion, and nutrient losses, plot D, 1987±93
Component
a
Precipitation (mm)
Runoff (mm)
Drainage (mm)
Evapotranspirationb (mm)
Erosion, runoff (kg/ha)
Erosion, drainagec (mm)
N loss, runoff (kg/ha)
Soluble N loss, drainaged (kg/ha)
N loss, runoff drainage (kg/ha)
P loss, runoff (kg/ha)
P loss, drainagee (kg/ha)
P loss, runoff drainage (kg/ha)
a
1987±May 1991
July 1991±93
Total, 1987±93
Simulated
Observed
Simulated
Observed
Simulated
Observed
2810
964
209
1634
6220
870
28.2
8.8
37.0
5.45
0.77
6.22
2822
1220
204
1398
7134
698
19.2
3.3
22.5
4.84
0.51
5.35
1545
207
523
784
750
2569
4.4
25.5
29.9
0.48
0.47
0.95
1560
216
588
756
777
2557
4.1
24.1
28.2
0.82
1.69
2.51
4355
1171
732
2418
6970
3439
32.6
34.3
68.9
5.93
1.24
7.17
4382
1436
792
2154
7911
3255
33.3
27.4
60.7
5.66
2.20
7.86
Simulated precipitation includes rainfall, snowmelt and irrigation, observed precipitation includes
irrigation.
b
Observed evapotranspiration calculated as: precipitation±runoff±drainage.
c
Simulated erosion calculated externally from GLEAMS.
d
Simulated for 1 m root zone to compare with ef¯uent from drains at 1 m depth.
e
PO4-P particulate P loss, simulated particulate P loss calculated externally from GLEAMS.
301
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
Simulated particulate matter in drainage compared favorably, also. The N and P
components of GLEAMS performed favorably on plot D for both periods.
4.2. Ranges of calibrated values for selected model parameters
Some parameters in each model component are considered sensitive in the output from
that component. Sensitivity of some parameters are site-specific, especially in the erosion
component. The flow sequence and/or overland flow profile selected in the erosion
component result in damped effects on some parameters and possible increased
sensitivity of others. For example, if only overland flow sequence is selected, the shape of
the overland flow profile will determine the relative sensitivity of soil erodibility,
Mannings `n', soil loss ratio, and practice factor. If the flow profile is continually
steepening to the outlet, all parameters are extremely sensitive because the system is
considered ``soil-detachment limited''. Conversely, if the profile is continually decreasing
in slope or has a compound shape that decreases in slope, the system is considered
``transport limited'' and deposition of eroded particles occurs resulting in relative
insensitivity of the above parameters. If there is a channel (or channels) or pond elements
in the flow sequence, as in the present calibration study, the overland flow profile and its
parameters are very insensitive because the system is transport limited, that is, the system
has a very limited capacity for transport of soil particles detached by raindrop impact and
shear stress.
The most sensitive parameters in each component were selected for reader information.
The parameters and ranges of calibrated values for plots A and D during all calibration
periods are given in Table 5. Even though the soil remains the same on a given plot over
the different calibration periods, it was necessary to change some parameter values to
achieve the best agreement between model simulation results and observed data. These
Table 5
Ranges of ®nal calibration values for selected GLEAMS model parameters for plots A and D
Model component/parameter
Soil horizon (mm)
0±200
200±300
300±600
600±1000
Hydrology/porosity (POR, cm3/cm3)
Field capacity (FC, cm/cm)
Wilting point (WP, cm/cm)
Saturated conductivity (SATK, cm/h)
Saturated conductivity (RC, cm/h)a
Curve number (CN2)
0.550±0.558
0.360±0.520
0.200±0.305
60.0
0.005±0.5
78±92
0.535±0.538
0.380±0.500
0.200±0.320
1.02
0.540
0.425±0.505
0.360±0.395
0.007±0.5
0.520
0.425±0.475
0.395
0.005±0.5
Erosion/soil erodibility (KSOIL, t ha h/ha MJ mm)
0.16
Plant nutrient/total nitrogen (TN, %)
NO3±N concentration (CNIT, mg/g)
Potential min. N (POTMN, kg/ha)
Total phosphorus (TP, %)
Labile P concentration (CLAB, mg/g)
0.17±0.20
5.0±20
700±715
0.06
3.0
0.08
3.0±8.0
148
0.04
2.0
0.039
1.0±4.0
214
0.02
1.0
0.035
1.0±3.5
268
0.016
0.8
a
Below root depth.
302
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
changes were due to the different management representations. The greatest changes
were required for the interrelated parameters porosity (POR), FC, and WP. Drainable
water capacity in the soil is represented by the difference between FC and POR, and
plant-available water is represented by the difference between FC and WP. When the
improved drainage system was installed in 1991, drainage flow increased significantly
(Tables 3 and 4), and these parameters required changes from those of previous years. A
reduction of WP was made in order to simulate evapotranspiration comparable to
observed ET. The continuous bare fallow condition during 1987±89 represents a
prolonged management scenario that the model normally represents for only short
periods between crops in most rotations. The interrelated water retention characteristics
were modified for this practice compared with the other periods. This is the reason for the
changes in parameter values shown in Table 5 for POR, FC, and WP.
Saturated conductivity (SATK and RC) was modified only in soil horizon 3 (300±
600 mm) and horizon 4 (600±1000 mm), and the layer immediately below the effective
root depth (RD in the model). The range of curve number (CN2) given in Table 5
obviously represent the different management conditions during the different periods.
Soil erodibility (KSOIL) was not a sensitive parameter in the calibration simulations
due to the flow profile and flow sequences selected as indicated above. A constant value
was used for all plots as determined for the one surface soil texture and organic carbon for
all plots.
There were few changes in the sensitive parameters in the plant nutrient component as
shown in Table 5. The only changes from plot-to-plot and period-to-period were in the
surface 10-mm of soil for total nitrogen (TN), concentration of nitrate-nitrogen (CNIT),
potentially mineralizable nitrogen (POTMN), and labile phosphorus concentration
(CLAB). These relatively minimal changes indicate that once the best water balance is
achieved in the hydrology component, changes in nutrient parameters do not markedly
change the simulated nutrient losses. Therefore, the data in Table 5 and the relative results
given in Tables 3 and 4 confirm that the water balance is the most critical part of this
calibration. Simulation for other scenarios might also include some erosion parameters.
4.3. Model validation
Average values from Table 5 were used for parameter values, without adjustment, for
plots B and C. This procedure is recommended when some local data are available, i.e.
when some data are available for a specific soil at a location, average values are suitable
unless some extreme conditions are to be represented.
Model simulation results are compared with observed data for plot B in Table 6 for the
1987±91 and 1991±93 periods. The first validation simulation, labeled ``Average
parameters'' in Table 6, used the above averaging of calibrated parameters. Although the
different rotation periods are not shown in Table 6, results for the 1987±89 fallow-spring
barley-fallow rotation compared more favorably with observed data than those for plot A
discussed in the section on calibration. The biggest discrepancy between simulated and
observed values for the 1987±91 period occurred in the NO3-N leaching which was
under-estimated by a factor of 3 for plot B (Table 6). Most of that difference occurred in
the 1987±89 fallow portion of the rotation.
Component
Precipitationd (mm)
Runoff (mm)
Drainage (mm)
Evapotranspiratione (mm)
Erosion, runoff (kg/ha)
Erosion, drainagef (kg/ha)
N loss, runoff (kg/ha)
Soluble N loss, drainageg (kg/ha)
N loss, runoff drainage (kg/ha)
P loss, runoff (kg/ha)
P loss, drainageh (kg/ha)
P loss, runoff drainage (kg/ha)
a
1987±May 1991
June 1991±93
Observed
Average
parametersa
Laboratory
valuesb
Default
parametersc
Observed
Average
parametersa
Laboratory
valuesb
Default
parametersc
2760
1068
323
1431
7322
1066
36.1
20.3
56.4
4.78
0.84
5.62
2755
1026
336
1384
8520
1345
39.8
7.7
47.5
7.45
1.38
8.83
2755
1074
272
1391
10190
1090
53.8
21.2
75.0
10.35
1.31
11.66
2755
900
528
1325
8330
2112
63.3
29.9
93.2
11.79
2.24
14.03
1560
183
582
795
648
2511
3.9
26.0
29.9
0.37
1.62
1.99
1545
206
578
754
760
2312
4.6
44.3
48.9
0.67
1.56
2.03
1545
364
244
902
2410
978
14.0
1.7
15.7
2.55
0.82
3.37
1545
352
286
886
1590
1147
13.0
2.9
15.9
1.84
0.24
2.08
GLEAMS simulation using average parameter values from calibration on plots A and D.
GLEAMS simulation using laboratory measurements for parameter values on plot B.
c
GLEAMS simulation using model default parameters, including nitrogen and phosphorus initializations.
d
Simulated precipitation includes rainfall and snowmelt.
e
Observed evapotranspiration calculated as: precipitation±runoff±drainage.
f
Simulated erosion calculated externally from GLEAMS
g
Simulated for 1000 mm root zone for comparison with ef¯uent from drains at 1000 mm depth.
h
PO4-P particulate P loss, particulate P loss calculated externally from GLEAMS.
b
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
Table 6
Observed and GLEAMS-simulated validation of water balance, erosion, and nutrient losses, pl
Gleams model application on a heavy
clay soil in Finland
Walter G. Knisela,1, Eila Turtolab,*
a
Biological and Agricultural Engineering Department, University of Georgia,
Coastal Plain Experiment Station, Tifton, GA 31793, USA
b
Institute of Crop and Soil Science, Agricultural Research Centre of Finland,
Jokioinen, FIN-31600, Finland
Accepted 14 July 1999
Abstract
The GLEAMS model version 2.10 was calibrated and validated with data from research plots on
illitic clay soil near the Agricultural Research Centre at Jokioinen, Finland. Observed surface
runoff, drainage ¯ow, erosion, and associated nitrogen and phosphorus loads from 0.46 ha plots
were used in the model application for a 7-year period with different management practices. Two
plots were used for calibration and two plots were used for validation in the present study. The
model was found to represent the soils and management with adjustment, or ®ne tuning, of sensitive
parameters. The simulated runoff, percolation, evapotranspiration, amount of soil erosion,
phosphorus (P) and nitrogen (N) losses in runoff and drainage water compared very well with
observed values on the average, but differed considerably from year-to-year and especially monthto-month. Observed data were required after improved drainage installation in order to adjust
parameters sensitive in water balance calculations. The P component of the model gave better
estimates of the losses in runoff and with eroded soil particles than did the more complex N
component. P with particulate in drainage water was simulated externally since it is a signi®cant
part of the total P lost and it is not considered in the model. Some model modi®cations were made
to better represent the climatic conditions of Finland. The validation study indicated that GLEAMS
can be used satisfactorily in Finland for comparisons of alternate management practices as
recommended by the model developers. # 2000 Elsevier Science B.V. All rights reserved.
Keywords: Surface runoff; Drainage ¯ow; Erosion; Nitrogen leaching; Phosphorus loss
*
Corresponding author. Tel.:358-3-41881; fax: 358-3-4188437.
E-mail address: eila.turtola@mtt.® (E. Turtola).
1
Retired, formerly Senior Visiting Research Scientist.
0378-3774/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 0 6 7 - 0
286
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
1. Introduction
Agriculture is the main source of phosphorus (P) and nitrogen (N) to fresh and coastal
waters of Finland (Rekolainen, 1989; Rekolainen et al., 1995), and the contribution from
agriculture has been increasing. Due to accelerated eutrophication of surface waters,
knowledge of best management practices is urgently needed to reduce the nutrient loads.
However, it is impossible to conduct field research in all aspects of alternate cropping
practices to develop feasible management systems for nonpoint source pollution control.
An alternative is to use available research data to validate comprehensive computer
models, and then use the models in a simulation mode to examine the long-term
consequences of alternate management systems. Rekolainen and Posch (1994) modified
the CREAMS model (Knisel, 1980) for Finnish conditions. The modified model was then
used to estimate the effect of different tillage intensities on soil loss in surface runoff
(Rekolainen et al., 1993). However, CREAMS only considered surface response of P, and
Turtola and Jaakkola (1995) found considerable leaching of dissolved orthophosphate-P
in drainage water.
High shrink/swell-capacity clay soils, such as in Finland, exhibit extensive shrinkage
cracks at the soil surface during prolonged dry periods. The cracks may have pronounced
effects on surface runoff and percolation below the root zone, and crack flow, or
preferential flow, may significantly affect chemical movement as well (e.g. Dekker and
Bouma, 1984).
GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) is a
mathematical model to simulate the complex climate±soil-management interactions for
field-size areas. It was developed to evaluate edge-of-field and bottom-of-root-zone
loadings of water, erosion material, and agricultural chemicals from alternate management systems (Leonard et al., 1987). Later a component was added to the model to
simulate relatively comprehensive nitrogen and phosphorus cycles in the soil (Knisel,
1993). The principal use of the model is to evaluate the differences among management
systems rather than to predict the absolute quantities of water, soil erosion, and chemicals
lost from the field. Examples of GLEAMS applications to assess management
alternatives are numerous (e.g. Leonard and Knisel, 1989; Reck, 1994; Smith et al.,
1994).
When developers formulate comprehensive models, such as GLEAMS, to represent the
complex interactions of the many physical and chemical processes, they use readily
available data to test the validity of the model concepts. These data are very limited when
considering the extreme number of possible combinations of soils, climate, and
management scenarios in all regions of the world. The processes themselves are
obviously the same, but the degree of interactions may be extremely different. For
example, a drainage model developed for high water-table conditions in a soil/climatic
region where little surface runoff occurs would have more emphasis on internal water
movement than on direct runoff, e.g. DRAINMOD (Skaggs et al., 1995). That model
would not have been tested initially in every drainage situation for all light- and heavytexture soils in cold and warm climatic regions with different tillage practices where
water retention/viscosity characteristics differ. The reverse is also true. Surface runoff
from snowmelt on soils frozen at least a portion of the time is vastly different from direct
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
287
runoff from thunderstorms in summer time on soils that are not frozen, or may never
freeze, to change the bulk density and water retention. After initial testing, if a model
appears adequate for its intended purpose and comprehension, evaluation under other
conditions may be made as data become available. Reporting of results Ð good, bad, or
indifferent Ð allows potential users to review the validity for possible model
applications. The alternative is to develop a model for every soil in every climatic
region for every management scenario resulting in millions of models that cannot be
extrapolated beyond their developmental conditions. The purpose of this paper is to
present the results of testing the GLEAMS model, developed in the USA, for high
organic-matter clay soils in the much colder climatic region in Finland. Results of
validation and fine-tuning of sensitive parameters of the model are presented, using
research data for a heavy clay soil at the Agricultural Research Centre of Finland.
2. Model description
The GLEAMS model consists of four components operating simultaneously:
hydrology, erosion/sediment yield, pesticides and plant nutrients. Only a brief description
of the model is given here, but more details can be found elsewhere (Knisel, 1980;
Leonard et al., 1987; Knisel, 1993). Additional detail will be given in a later section on
model modifications for those components changed for preferential flow representation.
2.1. Hydrology
Daily water accounting is simulated in a soil system layered within the genetic
horizons of the root zone. The model distributes soil characteristics into a maximum of 12
computational layers with input from a maximum of 5 soil horizons. Daily potential
evapotranspiration is estimated by the Priestly±Taylor (Priestly and Taylor, 1972) or
alternatively the Penman±Monteith methods (Monteith, 1965). Soil water uptake by a
crop is simulated as a two stage process with ET occurring at potential when water
content is greater than 25% plant available. Runoff is calculated using a modified Soil
Conservation Service curve number procedure (Williams and LaSeur, 1976). The
modification mainly consisted of replacing the 5-day antecedent rainfall with available
soil water storage, and making the procedure a daily simulation rather than a design-type
storm. Percolation through the soil layers uses a storage-routing technique (Williams and
Nicks, 1982).
Rekolainen and Posch (1994) modified the CREAMS model (Knisel, 1980) hydrology
component to better represent the Finnish climate. Mean daily temperature was used to
determine the state of the day for designating snow or rain in the precipitation file. This
adaptation is included as an option in the present version of GLEAMS (Knisel, 1993).
Rekolainen and Posch (1994) also included a simple soil frost model and an adjustable
albedo for simulating evapotranspiration. In GLEAMS version 2.10 (Knisel, 1993), soil
temperature is simulated, and is used to determine when the soil is frozen, at which time
the available soil water storage is reduced. Soil temperature is a function of mean daily
air temperature, 5-day average soil temperature, soil water content, soil depth, and soil
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W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
cover, i.e. snow, crop residue, growing crop canopy, or bare soil. These changes closely
represent some of the modifications made in CREAMS to represent Finnish conditions
(Rekolainen and Posch, 1994).
An irrigation component allows the model user to specify the threshold soil water
content in the active root growth layers for automatic model irrigation and to specify the
water content to which the soil water is to be raised. If irrigation is applied on research
plots, the depth of irrigation water is entered in the precipitation file for the day of
application.
2.2. Erosion
The erosion component of GLEAMS is the Onstad±Foster (Onstad and Foster, 1975)
modification of the universal soil loss equation (USLE) (Wischmeier and Smith, 1978)
for storm-by-storm simulation. Rill and inter-rill erosion are calculated on the nonuniform slope of a representative overland flow element of the field. The erosivity factor
(R) of the USLE is replaced by storm-by-storm rainfall energy calculated from daily
rainfall. The management factors of the USLE, i. e. soil loss ratio (C factor) and practice
factor (P), are maintained. In addition to the overland flow element, concentrated or
channel flow can be represented in the field. Although branching channels are not
considered, two channel sequences such as a terrace channel followed by a terrace outlet
channel can be represented in GLEAMS. A pond element also can be included to
represent temporary ponding, or an impoundment, that drains shortly after runoff ends,
such as an impoundment terrace, gully plug, or temporary ponding caused by restricted
outlet conditions for runoff plots with a weir or flume measuring device. Soil particles
and organic matter detached by raindrop impact are routed through the delivery sequence
of the field (Foster et al., 1985). A characteristic discharge, calculated from the storm
runoff peak rate simulated at the field outlet in the hydrology component, is used to
calculate transport of soil particles. The characteristic discharge is translated back to the
uppermost element and is used to calculate particle transport capacity and deposition of
each computational segment. Erosion/sediment yield and the associated sediment
enrichment ratio (ratio of the specific surface area of the eroded soil particles to the
specific surface area of the original soil) is calculated at the end of each flow element and
at the outlet (edge) of the field (Foster et al., 1980).
Posch and Rekolainen (1993) modified the rainfall energy computation in CREAMS
for Finnish conditions. They fitted monthly coefficients and exponents for different
locations in Finland during the growing season, and these were entered into the model to
give a better comparison between simulated and observed erosion. Their data were used
to approximate a polynomial and calculate a factor to adjust rainfall energy as a function
of latitude. The factor lowered the estimating relationship in CREAMS (Foster et al.,
1980) at 45E latitude (LAT) to a minimum of 20% at 65E latitude without changing the
relationship itself. The polynomial expression for the adjustment factor (FAC) is:
FAC 1.0
FAC 0.146(LAT) ÿ 0.00169(LAT)2 2.14
FAC 0
for 08 LAT < 458
for 458 LAT < 658
for 658 LAT < 908
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
289
The factor is multiplied by the rainfall energy as given by Foster et al. (1980), and the
shape of the energy relationship does not change from south to north.
2.3. Plant nutrients
The plant nutrient component of GLEAMS (Knisel, 1993) considers comprehensive
N and P cycles. Much of the nutrient component is very similar to the EPIC model
(Sharpley and Williams, 1990) except the animal waste component. The N cycle
includes: mineralization, immobilization, denitrification, ammonia volatization, nitrogen
fixation by legumes, fertilizer and animal waste application, crop uptake, and
runoff, erosion, and leaching losses. Mineralization is treated as a two-step process:
first-order ammonification, and zero-order nitrification. The ammonification is consistent
with animal waste loadings and ammonia volatization. The P cycle includes:
mineralization, immobilization, fertilizer and animal waste application, crop uptake,
and runoff, erosion, and leaching losses. Inorganic fertilizer application considers surface
and incorporated as well as fertigation. Animal waste, with specification of nutrient
content, may be represented as surface, incorporated, injected, or liquid effluent
applications. Organic fractions of animal waste N and P are maintained as separate
fractions that mineralize with different rate constants from those for fresh organic N
and P in crop residue or mineralizable soil N and P. Tillage and soil temperature
algorithms are included in the nutrient component. The tillage component incorporates
crop residue, animal waste, and fertilizer, and mixes the respective pools in the ploughed
layers. Ammonification, nitrification, denitrification, volatization, and mineralization
rates are adjusted by soil temperature and water content in the respective computational
soil layers. Rainfall N is an input for the model application site, and N and P in irrigation
water can be considered for locations where concentrations in the water supply may be
significant.
2.4. Pesticides
A pesticide component is included in the GLEAMS model (Leonard et al., 1987), but a
description is not included here since pesticides are not a part of the present study.
3. Model calibration and validation
The GLEAMS model nutrient component had minimal validation with field data in the
USA by Knisel (1993), and no validation of the component has been done outside the
USA. Climate, soil, and management practices vary drastically from region-to-region and
country-to-country. Therefore, it is always recommended that any available local data be
used to calibrate the model for local conditions and adjust, or ``fine-tune'', sensitive
parameters where possible, to assure the model is operating within the proper range. After
calibration, GLEAMS can then be used to compare alternative management practices
over a long-term climatic record.
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Table 1
Soil physical characteristics and chemical properties of the experimental ®eld near the Agricultural Research
Centre of Finland
Depth
(mm)
Particle size fractions (mm)
0.02
0±200
200±400
400±600
61
83
90
16
8
6
23
9
4
a
b
Sat. hydr.
conda (mm/h)
Depth
(mm)
620
6.3
0.064
0±250 5.8
250±600 6.3
600±900 6.7
pH
Org.C
(%)
Total
N (%)
Soluble
Pb (mg/l)
2.6
0.6
0.5
0.19
0.05
0.04
3.2
0.4
0.3
Laboratory measurement with undisturbed soil columns,ù 150 mm (Aura, 1990).
Acid ammonium acetate (pH 4.65) extractable P (Vuorinen and MaÈkitie, 1955).
3.1. Description of research plots
The research plots used to calibrate and validate the GLEAMS model were located at
the Agricultural Research Centre near Jokioinen in southwest Finland on a south-facing
field with a mean slope of 2%. The soil at the site was classified as silty clay or heavy
clay in the plough layer and as heavy clay in the subsoil according to the Finnish textural
classification, and classified taxonomically as Vertic Cambisol (FAO, 1988), and very
fine Typic Cryaquept (Soil Survey Staff, 1992).
Soil characteristics of the plots are given in Table 1. The mean saturated hydraulic
conductivity of the plow layer was 620 mm/h, which is in the normal range for Finnish
clay soils, 500±2000 mm/h (Aura, 1990). Saturated conductivity of the subsoil was
drastically lower, decreasing to less than 10 mm/h. As shown in Table 1, the soil was low
in plant available P with only a slight change from the 250±600 mm depth to that for the
600±900 mm depth. The differences in depths shown in the table between physical and
chemical properties were due to different sampling dates and procedures and not because
of different genetic horizons at the respective sampling locations.
Subsurface drain tiles were installed in the field in 1962 with a 16.5 m spacing at a
depth of approximately 1 m. In 1975 (prior to the research study which began in 1987),
the drains were cut into 33 m lengths, forming 16 drainage plots, each with two drains
and an area of 0.11 ha (Jaakkola, 1984). In June 1991, during the study period, subsurface
drainage was improved by installing plastic drain tubing at the same depth but at a 0.3 m
distance laterally from the old drain tile (Turtola and Paajanen, 1995). Percolate from the
drain tiles and tubing was conducted by pipe to an observation building where the volume
was measured with a recording tipping bucket installation. Flow-weighted samples were
collected for chemical analyses.
Four adjacent surface-runoff plots were established in 1975, each with 0.46 ha
drainage area and containing four subsurface-drainage plots. The plot borders consisted
of soil dikes, or ridges (embankments), that contained the runoff which drained to the
lower plot corner. A small channel with a gravel bottom at the lower end of the plots
collected the runoff which then drained through the gravel into a plastic pipe leading
to a measuring device in an observation building. Runoff from snowmelt generally
flowed directly into the collection channel, but small runoff volumes normally drained to
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
291
the lower plot corner into the channel. Surface runoff was measured with a recording
tipping bucket installation, and flow weighted samples were collected for chemical
analyses.
Percolate from the four drains in each surface plot was summed to determine the total
subsurface flow for the 0.46 ha area. This was necessary to obtain unit runoff and
subsurface flow for the same plot area.
Runoff and drainage samples were analyzed for total solids, representing soil erosion
(TS), total phosphorus (TP), dissolved orthophosphate-phosphorus (PO4-P), total nitrogen
(TN), nitrate-nitrogen (NO3-N), and ammonium-nitrogen (NH4-N). Description of the
analytical laboratory methods is beyond the scope of this paper, but details are given by
Turtola and Paajanen (1995). Analyses of the drainage water were aggregated for the four
drain pipes in each surface plot for unit area comparisons.
The study period for model calibration and validation was 1987 through 1993. Plot
cropping practices with summer and following winter soil cover conditions are given in
Table 2 for the study period. Spring-planted small grains constitute the major crops of the
region, and the barley (Hordeum vulgare) shown in Table 2 is spring barley.
3.2. Model calibration
The GLEAMS model was developed to represent management systems. These include
crop rotations, tillage practices, conservation practices, irrigation, drainage, fertilizer
practices, and pesticide treatments, among others. Knisel et al. (1995) discussed effects of
different management practices on model output. GLEAMS can represent several
different systems in a single simulation. However, some systems, such as drain tile
installation, results in different base parameters, and therefore requires stopping
simulation and beginning a new with different saturated conductivity values for drainage.
Two plots were selected for model calibration (fine tuning of sensitive parameters) and
two plots were then used for model validation. Plots A and D were arbitrarily selected for
model calibration, leaving plots B and C for validation.
Model calibration on plots A and D was made in three parts to correspond with the
cropping practices and improved drainage system. Although the model can represent a
crop rotation in a simulation run, it was considered desirable for calibration to simulate
the 1987±89 period for plot A which was maintained in a continuous bare fallow
condition. The cropping period on plot A, 1990±93 (Table 2), was interrupted by the
installation of the improved drainage system in June 1991. Thus, for comparative
purposes, the model simulations on the two plots were made for three periods: (1) 1987±
89, (2) 1990±May 1991, and (3) July 1991±1993.
Soil samples were obtained from the research plots during the 1987±89 record period
for laboratory analyses of physical characteristics (Aura, 1990) and chemical content.
Samples for physical analyses were taken at two locations in each of plots A, B, and D,
and at four locations in plot C. For chemical analyses, samples were taken at four
locations in all plots with five sub-samples per location. The samples represent the preimproved drainage period. Although the matrix soil, on which the laboratory analyses
were made, did not change, the improved drainage of 1991 imposed significantly
different drainage boundary conditions.
292
Year
Plot
1987
1988
1989
1990
1991a
1992
1993
a
b
A
B
C
D
Bare fallow (ploughed)
Bare fallow (ploughed)
Bare fallow (ploughed)
Spring barley (ploughed)
Spring barley (ploughed)
Timothy (timothy)
Timothy (ploughed)
Bare fallow (ploughed)
Spring barley (ploughed)
Bare fallow (ploughed)
Spring barley (ploughed)
Spring barley (ploughed)
Timothy (timothy)
Timothy (timothy)b
Ryegrass (ploughed)
Spring barley (ploughed)
Ryegrass (ploughed)
Spring barley (ploughed)
Spring barley (ploughed)
Timothy (timothy)
Timothy (ploughed)
Timothy (timothy)
Timothy (timothy)
Timothy (ploughed)
Spring barley (ploughed)
Spring barley (ploughed)
Timothy
Timothy (timothy)b
Subsurface drainage improvement in June, 1991.
Timothy residue; timothy killed with Glyphosate on August 20, 1993.
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
Table 2
Summer crop (the following winter soil cover in parenthesis) on the experimental plots, 1987±93
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
293
Observed soil physical and chemical characteristics (Table 1) were used to develop
initial parameter files for the GLEAMS model. Water retention and drainage
characteristics of soils were determined on samples in the laboratory rather than in situ
field measurements. Since undisturbed soil cores have different boundary conditions from
those in the field, the laboratory-measured values may differ significantly from integrated
field values. Also, model formulations of field processes are not exact and complete. For
example, a single representative value of soil porosity for each soil horizon is used in
GLEAMS without adjustments for changes in water content or frozen-soil conditions as
they are known to occur in the field. Infiltration of rainfall into the soil is not considered
per se, but is a part of the rainfall-runoff relationships (Williams and LaSeur, 1976). Thus,
laboratory derived characteristics must be adjusted in the model to achieve the best
comparison with observed data.
Results of the water balance simulation were compared with observed runoff and
drainage data. The hydrology parameters sensitive in water balance calculations, i.e. soil
porosity, field capacity, and curve number, were adjusted and fine tuned to achieve the
best agreement between total model-simulated and observed runoff and drainage flow
during each period of comparison on each plot. Simulated annual evapotranspiration (ET)
was compared with observed annual ET, with observed values determined by subtracting
combined runoff and drainage flow from total annual precipitation without considering
changes in soil water storage in the root zone from the beginning to the end of the year.
Rooting depth is a sensitive parameter in the GLEAMS water balance calculations, but
the drain tile is approximately 1 m below the land surface, this depth was used as the
rooting depth for comparisons of percolation and leaching.
GLEAMS uses a ``current crop rooting depth'' (CCRD) that may be less than the
effective rooting depth (RD) to reflect uptake of water and chemicals for shallow rooted
crops in rotation. For example, potatoes (Solanum tuberosum) generally root only to the
bottom of the plough layer (perhaps 200±300 mm) compared with deep rooted crops such
as grasses or cereal grains which may have an effective root depth of 600 mm or more.
The model does not simulate water and nutrient uptake below the CCRD, but water and
chemicals move through the lower depths. Therefore, RD for model simulation was set at
1000 mm, and CCRD 600 mm was used for the spring barley, ryegrass, and timothy
grown on the plots.
The best estimates of erosion/sediment yield cannot be obtained until the hydrology
components are fine tuned to achieve the best estimates of water balance components. Likewise, the proper pathways and quantities of nitrogen and phosphorus cannot
be achieved until the water balance and sediment yield were finalized. Nitrate leaching is
a function of the soil nitrate as well as the volume of percolation through the root zone. If
water retention characteristics and initial soil NO3-N are adjusted in the same model run,
the relative effects on nitrate leaching could not be evaluated. Therefore, sequential fine
tuning was performed to obtain the best results in a systematic manner.
After water balance simulations were fine tuned, similar procedures were used for the
erosion component. Erosion parameter sensitivity is site specific since flow sequence and
topography may alter their relative effects. In the present application on the research
plots, an overland flow-pond sequence was simulated to depict the temporary ponding of
water inside the plot boundary resulting from the channel and restrictive pipe conveyance
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W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
of runoff to the observation building. Therefore, the normally sensitive overland flow
parameters (soil loss ratio, P-factor, and Manning's `n') are dominated by the pipe
drainage characteristics. Soil loss ratios and Manning's `n' values were adjusted, but the
practice factor was well-defined because plowing and sowing operations were up-anddown the plot slope.
Initial N and P content of the soil horizons and potential crop yield are the most sensitive
plant nutrient parameters. Such sensitive parameters as depth of fertilizer application (tillage)
were not fine-tuned since one approximate depth was given. However, this can be a source of
discrepancy between model-simulated and observed nitrate-nitrogen leaching. For example,
if the bottom of the depth of tillage is only 5 mm into a computational soil layer, the model
assumes tillage or fertilizer incorporation occurs throughout the soil layer which may be as
much as 100 mm thick. Depending upon how near that layer is o the RD or CCRD, the
fertilizer placement or mixing of mineralizable N and P by tillage may significantly affect
simulation results. This is cited merely to point out that parameter sensitivity may not always
be clearly evident and exact in any model, not just GLEAMS.
Irrigation was applied on plots only during the 1987±89 period. Since irrigation depths
were known, the amounts were included in the precipitation file on the actual dates of
irrigation. This could not be considered as a part of the irrigation option in GLEAMS
since the model calculates the amount of water to be applied is based upon prescribed soil
water content rather than a specified amount.
3.3. Model validation
Model validation was made on plots B and C using (1) average state parameters from
calibration on plots A and D without adjustment, (2) laboratory-determined values for
porosity, water retention characteristics, saturated hydraulic conductivity, and plant nutrient
measurements, and (3) default values (model data base, averaged for all soils) without
adjustment. Simulations were each made for the two periods, January 1987±May 1991 and
July 1991±December 1993. Improved drainage was installed in June 1991, and this is a
management change even though the matrix soils did not change. This method of validation
provides three independent simulations on which to draw conclusions about the GLEAMS
model applicability on clay soils in Finland. It includes the recommended methods of
application, i.e. using measured data where available, averages from locally available data, or
as a last resort, averages for all soils-topography-management scenarios. Changes in
cropping practices on plots B and C during 1987±91 were represented in a single simulation
on each plot as opposed to the calibration simulations for plots A and D.
Results of the calibration and validation simulations are discussed in the following
sections of this paper.
4. Results
4.1. Model calibration
Continuous bare fallow conditions on plot A for 1987±89 (Table 2) offer a unique
opportunity to examine climate±soil interactions without crops. However, representing
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Table 3
Observed and GLEAMS-simulated calibration water balance, erosion, and nutrient losses, plot A, 1987±93
Component
a
Precipitation (mm)
Runoff (mm)
Drainage (mm)
Evapotranspirationb (mm)
Erosion, runoff (kg/ha)
Erosion, drainagec (mm)
N loss, runoff (kg/ha)
Soluble N loss, drainaged (kg/ha)
N loss, runoff drainage (kg/ha)
P loss, runoff (kg/ha)
P loss, drainagee (kg/ha)
P loss, runoff drainage (kg/ha)
1987±May 1991
July 1991±93
Total, 1987±93
Simulated
Observed
Simulated
Observed
Simulated
Observed
2748
866
484
1373
7280
1922
32.7
25.6
58.3
6.06
1.73
7.79
2760
822
497
1441
7206
1162
31.8
50.4
82.2
4.40
1.94
6.34
1545
178
544
809
560
2650
3.9
37.1
41.0
0.55
1.44
1.99
1560
149
595
816
695
2805
3.3
34.1
37.4
0.56
1.91
2.47
4293
1044
1028
2182
7840
4572
36.6
62.7
99.3
6.61
3.17
9.78
4320
971
1092
2277
7901
3967
35.1
84.5
119.6
4.96
3.85
8.81
a
Simulated precipitation includes rainfall and snowmelt.
Observed evapotranspiration calculated as: precipitation±runoff±drainage.
c
Simulated erosion calculated externally from GLEAMS.
d
Simulated for 1 m root zone to compare with ef¯uent from drains at 1 m depth.
e
PO4-P particulate P loss, simulated particulate P loss calculated externally from GLEAMS.
b
these conditions is not without difficulty. What does a rooting depth represent when there
is not a crop growing for extended periods (3 years)? There are no decaying plant roots
that affect infiltration during short-term fallow periods. Models such as GLEAMS are
formulated to consider short-term fallow interspersed between crops but not long-term
fallow.
Results of the GLEAMS model simulations are compared with observed values for plot
A in Table 3. Two periods are shown in the table for comparisons: 1987±May 1991 prior
to improved drainage and July 1991±1993 after improved drainage. Runoff was overestimated some years and under-estimated some years (Fig. 1) with corresponding underand over-estimates of percolation (Fig. 2). Total runoff was over-estimated in both
periods and percolation was under-estimated as well as ET.
Most of the observed and simulated runoff occurred from spring snowmelt, and thus it
would appear that the snow accumulation and melt simulations are not adequately
represented in the model. The simple frozen-soil representation in GLEAMS (Knisel et
al., 1985) does not consider the effects of soil water content at the time of freezing on the
conductivity of water into, within, and through the profile.
Simulated erosion/sediment yield agreed very well for the 1987±91 period but was
over-estimated for 1991±93 with good agreement with observed values for the total 7
year study (Table 3). The high content of illitic-mineral clay in the Finnish soils is
significantly different from those used in the development of the USLE (Wischmeier and
Smith, 1978). Soil erodibility used in the model may be in error since local data are not
available. Also, since most of the erosion occurs from snowmelt runoff, soil surface
condition is an important factor in estimating detachment of soil particles by runoff. The
GLEAMS modification of rainfall energy described earlier under model description is
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Fig. 1. Cumulative simulated and observed runoff, plot A.
Fig. 2. Cumulative simulated and observed percolation (drainage), plot A.
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
297
dominated by the runoff-sediment transport conditions, i.e. temporary channel ponding
and drainage through the gravel into the pipe leading to the measuring device. The
temporary ponding allows deposition of soil aggregates and course particles as well as
some suspended sediment. The runoff drainage-outflow conditions are difficult to
represent in GLEAMS. Temporary ponding can be represented in the model, but an
equivalent pipe diameter is required to represent the combined gravel and pipe drainage.
The pipe diameter is a sensitive model parameter for sediment-yield simulation. Also,
continued deposition of sediment over the gravel with each runoff event on the field plots
resulted in continuously changing drainage restrictions. This required adjustment of the
sensitive model parameter to give the best estimate of sediment yield for comparison with
observed values for the entire simulation period.
Considerable soil particles and particulate P were observed in drainage water from
the research plot (Turtola and Jaakkola, 1995; Turtola and Paajanen, 1995) as shown in
Table 3, but such transport is not simulated by the GLEAMS model. Suspended
particlulate matter is generally observed from drainage of heavy clay soils as found by
Bengtsson et al. (1992) in a subdrained basin in southern Finland. Turtola and Paajanen
(1995) reported concentrations of particulate in drainage water varied seasonally with an
average of about 0.4 mg particulate/L of drainage during the study period. The particulate
in drainage water is related to shrinkage and cracking of the clay soils upon drying.
Drying of the soil profile occurs immediately over the drainage pipe where the water table
(free water in the profile) is the lowest. Subsequent rains may move soil particles and
organic matter from the soil surface into the cracks or other macro-pores and to the
drainage pipes early in a rainfall event before the soil matrix is transmitting water.
Tillage, or other management practices, may obliterate the macro-pores at the surface of
the soil, and thereby prevent macro-pore flow from transmitting particulate to the
drainage pipes. These interacting phenomena and macro-pore flow are not included in the
GLEAMS model, and particulate drainage is not possible. An external model was
developed by Shirmohammadi et al. (1998) to estimate daily particulate mass in drainage
flow. Average particulate concentration (averaged over all seasons for all plots, 0.4 mg/l)
and the daily drainage flow, or percolation volume, were used to calculate particulate
mass. Daily percolation was obtained directly from GLEAMS for input into the external
calculation procedure. This is the value shown in Table 3 for erosion in drainage water for
plot A.
Simulated total N in runoff compared very well with observed values during both
periods (Table 3, Fig. 3). NO3-N leaching was under-estimated by a factor of 2 in the
1987±91 period (Table 3), which occurred mainly during the 1987±89 bare-fallow period
(Fig. 4). The under-estimate of NO3-N leaching resulted from a high estimate of
denitrification. Crop uptake of N is usually the largest component of the N cycle, but
without a growing crop during the bare fallow period, denitrification probably was overestimated. Simulated and observed leaching compared fairly well after improved
drainage.
Simulated P losses for plot A compared relatively well with observed values during
both periods. The largest discrepancies for losses in runoff ocurred in 1987 when erosion
was over-estimated by a factor of 3, and for losses in both runoff and drainage in 1990
(Figs. 5 and 6). Particulate P in drainage water, simulated externally by the procedure
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Fig. 3. Cumulative simulated and observed nitrogen loss in runoff, plot A.
Fig. 4. Cumulative simulated and observed nitrate-nitrogen loss in drainage, plot A.
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
299
Fig. 5. Cumulative simulated and observed phosphorus loss in runoff, plot A.
described previously, compared very well with observed values. Total simulated P losses
for the 7-year period agreed well.
The July 1991±December 1993 simulated and observed data for plot A are shown in
Table 3. Model parameters for field capacity (FC), wilting point (WP), and saturated
conductivity (SATK) required significant adjustment from those for the 1987±91 period
Fig. 6. Cumulative simulated and observed phosphorus loss in drainage, plot A.
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W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
to simulate the high volumes of observed drainage flow. During installation of the new
drainage system, the trenches above a gravel envelope around the tubes were back filled
with wood chips in the lower part of the trenches and with loose topsoil in the upper part
of the trenches to the approximate plough depth. The artificially filled trenches, four in
each plot, possibly intercepted some lateral subsurface flow that would not have reached
the old drainage tubes. Some of the lateral subsurface flow intercepted by the back-filled
trenches could have drained into the surface runoff collector ditch under the old drainage
regime. Improved drainage outlet can result in less water held in the soil against the force
of gravity, which is the definition of field capacity. If this is true, the adjustments of
parameter values for field capacity and saturated conductivity are justified to adequately
represent the new drainage regime. The relatively good agreement between simulated and
observed 3-yr totals of water, sediment, and nutrient losses shown in Table 3 were
obtained with these adjustments. In terms of soil characteristics, the soil itself did not
change from the old drainage system to the new, and the same parameter values would be
expected. These changes indicate that even though most of the GLEAMS parameters are
physically based, some parameters must also represent treatment, such as drainage in this
application. These important adjustments were made in the calibration simulations, but
they are extremely difficult when data are not available.
Simulated runoff was significantly under-estimated on plot D for the period before
improved drainage, but agreed very well after drainage (Table 4). Simulated and observed
drainage agreed well for both periods. The under-estimate of runoff resulted in an overestimate of ET prior to improved drainage. Erosion was under-estimated since runoff was
under-estimated prior to improved drainage, but compared favorably after drainage.
Table 4
Observed and GLEAMS-simulated calibration water balance, erosion, and nutrient losses, plot D, 1987±93
Component
a
Precipitation (mm)
Runoff (mm)
Drainage (mm)
Evapotranspirationb (mm)
Erosion, runoff (kg/ha)
Erosion, drainagec (mm)
N loss, runoff (kg/ha)
Soluble N loss, drainaged (kg/ha)
N loss, runoff drainage (kg/ha)
P loss, runoff (kg/ha)
P loss, drainagee (kg/ha)
P loss, runoff drainage (kg/ha)
a
1987±May 1991
July 1991±93
Total, 1987±93
Simulated
Observed
Simulated
Observed
Simulated
Observed
2810
964
209
1634
6220
870
28.2
8.8
37.0
5.45
0.77
6.22
2822
1220
204
1398
7134
698
19.2
3.3
22.5
4.84
0.51
5.35
1545
207
523
784
750
2569
4.4
25.5
29.9
0.48
0.47
0.95
1560
216
588
756
777
2557
4.1
24.1
28.2
0.82
1.69
2.51
4355
1171
732
2418
6970
3439
32.6
34.3
68.9
5.93
1.24
7.17
4382
1436
792
2154
7911
3255
33.3
27.4
60.7
5.66
2.20
7.86
Simulated precipitation includes rainfall, snowmelt and irrigation, observed precipitation includes
irrigation.
b
Observed evapotranspiration calculated as: precipitation±runoff±drainage.
c
Simulated erosion calculated externally from GLEAMS.
d
Simulated for 1 m root zone to compare with ef¯uent from drains at 1 m depth.
e
PO4-P particulate P loss, simulated particulate P loss calculated externally from GLEAMS.
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Simulated particulate matter in drainage compared favorably, also. The N and P
components of GLEAMS performed favorably on plot D for both periods.
4.2. Ranges of calibrated values for selected model parameters
Some parameters in each model component are considered sensitive in the output from
that component. Sensitivity of some parameters are site-specific, especially in the erosion
component. The flow sequence and/or overland flow profile selected in the erosion
component result in damped effects on some parameters and possible increased
sensitivity of others. For example, if only overland flow sequence is selected, the shape of
the overland flow profile will determine the relative sensitivity of soil erodibility,
Mannings `n', soil loss ratio, and practice factor. If the flow profile is continually
steepening to the outlet, all parameters are extremely sensitive because the system is
considered ``soil-detachment limited''. Conversely, if the profile is continually decreasing
in slope or has a compound shape that decreases in slope, the system is considered
``transport limited'' and deposition of eroded particles occurs resulting in relative
insensitivity of the above parameters. If there is a channel (or channels) or pond elements
in the flow sequence, as in the present calibration study, the overland flow profile and its
parameters are very insensitive because the system is transport limited, that is, the system
has a very limited capacity for transport of soil particles detached by raindrop impact and
shear stress.
The most sensitive parameters in each component were selected for reader information.
The parameters and ranges of calibrated values for plots A and D during all calibration
periods are given in Table 5. Even though the soil remains the same on a given plot over
the different calibration periods, it was necessary to change some parameter values to
achieve the best agreement between model simulation results and observed data. These
Table 5
Ranges of ®nal calibration values for selected GLEAMS model parameters for plots A and D
Model component/parameter
Soil horizon (mm)
0±200
200±300
300±600
600±1000
Hydrology/porosity (POR, cm3/cm3)
Field capacity (FC, cm/cm)
Wilting point (WP, cm/cm)
Saturated conductivity (SATK, cm/h)
Saturated conductivity (RC, cm/h)a
Curve number (CN2)
0.550±0.558
0.360±0.520
0.200±0.305
60.0
0.005±0.5
78±92
0.535±0.538
0.380±0.500
0.200±0.320
1.02
0.540
0.425±0.505
0.360±0.395
0.007±0.5
0.520
0.425±0.475
0.395
0.005±0.5
Erosion/soil erodibility (KSOIL, t ha h/ha MJ mm)
0.16
Plant nutrient/total nitrogen (TN, %)
NO3±N concentration (CNIT, mg/g)
Potential min. N (POTMN, kg/ha)
Total phosphorus (TP, %)
Labile P concentration (CLAB, mg/g)
0.17±0.20
5.0±20
700±715
0.06
3.0
0.08
3.0±8.0
148
0.04
2.0
0.039
1.0±4.0
214
0.02
1.0
0.035
1.0±3.5
268
0.016
0.8
a
Below root depth.
302
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
changes were due to the different management representations. The greatest changes
were required for the interrelated parameters porosity (POR), FC, and WP. Drainable
water capacity in the soil is represented by the difference between FC and POR, and
plant-available water is represented by the difference between FC and WP. When the
improved drainage system was installed in 1991, drainage flow increased significantly
(Tables 3 and 4), and these parameters required changes from those of previous years. A
reduction of WP was made in order to simulate evapotranspiration comparable to
observed ET. The continuous bare fallow condition during 1987±89 represents a
prolonged management scenario that the model normally represents for only short
periods between crops in most rotations. The interrelated water retention characteristics
were modified for this practice compared with the other periods. This is the reason for the
changes in parameter values shown in Table 5 for POR, FC, and WP.
Saturated conductivity (SATK and RC) was modified only in soil horizon 3 (300±
600 mm) and horizon 4 (600±1000 mm), and the layer immediately below the effective
root depth (RD in the model). The range of curve number (CN2) given in Table 5
obviously represent the different management conditions during the different periods.
Soil erodibility (KSOIL) was not a sensitive parameter in the calibration simulations
due to the flow profile and flow sequences selected as indicated above. A constant value
was used for all plots as determined for the one surface soil texture and organic carbon for
all plots.
There were few changes in the sensitive parameters in the plant nutrient component as
shown in Table 5. The only changes from plot-to-plot and period-to-period were in the
surface 10-mm of soil for total nitrogen (TN), concentration of nitrate-nitrogen (CNIT),
potentially mineralizable nitrogen (POTMN), and labile phosphorus concentration
(CLAB). These relatively minimal changes indicate that once the best water balance is
achieved in the hydrology component, changes in nutrient parameters do not markedly
change the simulated nutrient losses. Therefore, the data in Table 5 and the relative results
given in Tables 3 and 4 confirm that the water balance is the most critical part of this
calibration. Simulation for other scenarios might also include some erosion parameters.
4.3. Model validation
Average values from Table 5 were used for parameter values, without adjustment, for
plots B and C. This procedure is recommended when some local data are available, i.e.
when some data are available for a specific soil at a location, average values are suitable
unless some extreme conditions are to be represented.
Model simulation results are compared with observed data for plot B in Table 6 for the
1987±91 and 1991±93 periods. The first validation simulation, labeled ``Average
parameters'' in Table 6, used the above averaging of calibrated parameters. Although the
different rotation periods are not shown in Table 6, results for the 1987±89 fallow-spring
barley-fallow rotation compared more favorably with observed data than those for plot A
discussed in the section on calibration. The biggest discrepancy between simulated and
observed values for the 1987±91 period occurred in the NO3-N leaching which was
under-estimated by a factor of 3 for plot B (Table 6). Most of that difference occurred in
the 1987±89 fallow portion of the rotation.
Component
Precipitationd (mm)
Runoff (mm)
Drainage (mm)
Evapotranspiratione (mm)
Erosion, runoff (kg/ha)
Erosion, drainagef (kg/ha)
N loss, runoff (kg/ha)
Soluble N loss, drainageg (kg/ha)
N loss, runoff drainage (kg/ha)
P loss, runoff (kg/ha)
P loss, drainageh (kg/ha)
P loss, runoff drainage (kg/ha)
a
1987±May 1991
June 1991±93
Observed
Average
parametersa
Laboratory
valuesb
Default
parametersc
Observed
Average
parametersa
Laboratory
valuesb
Default
parametersc
2760
1068
323
1431
7322
1066
36.1
20.3
56.4
4.78
0.84
5.62
2755
1026
336
1384
8520
1345
39.8
7.7
47.5
7.45
1.38
8.83
2755
1074
272
1391
10190
1090
53.8
21.2
75.0
10.35
1.31
11.66
2755
900
528
1325
8330
2112
63.3
29.9
93.2
11.79
2.24
14.03
1560
183
582
795
648
2511
3.9
26.0
29.9
0.37
1.62
1.99
1545
206
578
754
760
2312
4.6
44.3
48.9
0.67
1.56
2.03
1545
364
244
902
2410
978
14.0
1.7
15.7
2.55
0.82
3.37
1545
352
286
886
1590
1147
13.0
2.9
15.9
1.84
0.24
2.08
GLEAMS simulation using average parameter values from calibration on plots A and D.
GLEAMS simulation using laboratory measurements for parameter values on plot B.
c
GLEAMS simulation using model default parameters, including nitrogen and phosphorus initializations.
d
Simulated precipitation includes rainfall and snowmelt.
e
Observed evapotranspiration calculated as: precipitation±runoff±drainage.
f
Simulated erosion calculated externally from GLEAMS
g
Simulated for 1000 mm root zone for comparison with ef¯uent from drains at 1000 mm depth.
h
PO4-P particulate P loss, particulate P loss calculated externally from GLEAMS.
b
W.G. Knisel, E. Turtola / Agricultural Water Management 43 (2000) 285±309
Table 6
Observed and GLEAMS-simulated validation of water balance, erosion, and nutrient losses, pl