Gleams model application on a heavy
clay soil in Finland
Walter G. Knisel
a,1, Eila Turtola
b,* aBiological and Agricultural Engineering Department, University of Georgia,Coastal Plain Experiment Station, Tifton, GA 31793, USA bInstitute 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 month-to-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).
1Retired, formerly Senior Visiting Research Scientist.
0378-3774/00/$ ± see front matter#2000 Elsevier Science B.V. All rights reserved.
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 manage-ment 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).
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
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 non-uniform 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 (Cfactor) 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:
FAC1.0 for 08LAT < 458
FAC0.146(LAT)ÿ0.00169(LAT)22.14 for 458LAT < 658
FAC0 for 658LAT < 908
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.
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 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) Sat. hydr.
conda(mm/h)
aLaboratory measurement with undisturbed soil columns,ù 150 mm (Aura, 1990).
bAcid ammonium acetate (pH 4.65) extractable P (Vuorinen and MaÈkitie, 1955).
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 pre-improved 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.
Table 2
Summer crop (the following winter soil cover in parenthesis) on the experimental plots, 1987±93
Year Plot
A B C D
1987 Bare fallow (ploughed) Bare fallow (ploughed) Ryegrass (ploughed) Timothy (timothy)
1988 Bare fallow (ploughed) Spring barley (ploughed) Spring barley (ploughed) Timothy (timothy)
1989 Bare fallow (ploughed) Bare fallow (ploughed) Ryegrass (ploughed) Timothy (ploughed)
1990 Spring barley (ploughed) Spring barley (ploughed) Spring barley (ploughed) Spring barley (ploughed)
1991a Spring barley (ploughed) Spring barley (ploughed) Spring barley (ploughed) Spring barley (ploughed)
1992 Timothy (timothy) Timothy (timothy) Timothy (timothy) Timothy
1993 Timothy (ploughed) Timothy (timothy)b Timothy (ploughed) Timothy (timothy)b
aSubsurface drainage improvement in June, 1991.
bTimothy residue; timothy killed with Glyphosate on August 20, 1993.
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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 CCRD600 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 com-ponents. 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
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-and-down 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
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 over-estimated some years and over-estimated some years (Fig. 1) with corresponding under-and 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 Table 3
Observed and GLEAMS-simulated calibration water balance, erosion, and nutrient losses, plot A, 1987±93
Component 1987±May 1991 July 1991±93 Total, 1987±93
Simulated Observed Simulated Observed Simulated Observed
Precipitationa(mm) 2748 2760 1545 1560 4293 4320
Runoff (mm) 866 822 178 149 1044 971
Drainage (mm) 484 497 544 595 1028 1092
Evapotranspirationb(mm) 1373 1441 809 816 2182 2277
Erosion, runoff (kg/ha) 7280 7206 560 695 7840 7901
Erosion, drainagec(mm) 1922 1162 2650 2805 4572 3967
N loss, runoff (kg/ha) 32.7 31.8 3.9 3.3 36.6 35.1
Soluble N loss, drainaged(kg/ha) 25.6 50.4 37.1 34.1 62.7 84.5
N loss, runoffdrainage (kg/ha) 58.3 82.2 41.0 37.4 99.3 119.6
P loss, runoff (kg/ha) 6.06 4.40 0.55 0.56 6.61 4.96
P loss, drainagee(kg/ha) 1.73 1.94 1.44 1.91 3.17 3.85
P loss, runoffdrainage (kg/ha) 7.79 6.34 1.99 2.47 9.78 8.81
aSimulated precipitation includes rainfall and snowmelt.
bObserved evapotranspiration calculated as: precipitation±runoff±drainage.
cSimulated erosion calculated externally from GLEAMS.
dSimulated for 1 m root zone to compare with ef¯uent from drains at 1 m depth.
ePO
4-Pparticulate P loss, simulated particulate P loss calculated externally from GLEAMS.
Fig. 1. Cumulative simulated and observed runoff, plot A.
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 over-estimated. 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
Fig. 3. Cumulative simulated and observed nitrogen 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. 5. Cumulative simulated and observed phosphorus loss in runoff, plot A.
Fig. 6. Cumulative simulated and observed phosphorus loss in drainage, plot A.
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 over-estimate of ET prior to improved drainage. Erosion was under-over-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 1987±May 1991 July 1991±93 Total, 1987±93
Simulated Observed Simulated Observed Simulated Observed
Precipitationa(mm) 2810 2822 1545 1560 4355 4382
Runoff (mm) 964 1220 207 216 1171 1436
Drainage (mm) 209 204 523 588 732 792
Evapotranspirationb(mm) 1634 1398 784 756 2418 2154
Erosion, runoff (kg/ha) 6220 7134 750 777 6970 7911
Erosion, drainagec(mm) 870 698 2569 2557 3439 3255
N loss, runoff (kg/ha) 28.2 19.2 4.4 4.1 32.6 33.3
Soluble N loss, drainaged(kg/ha) 8.8 3.3 25.5 24.1 34.3 27.4
N loss, runoffdrainage (kg/ha) 37.0 22.5 29.9 28.2 68.9 60.7
P loss, runoff (kg/ha) 5.45 4.84 0.48 0.82 5.93 5.66
P loss, drainagee(kg/ha) 0.77 0.51 0.47 1.69 1.24 2.20
P loss, runoffdrainage (kg/ha) 6.22 5.35 0.95 2.51 7.17 7.86
aSimulated precipitation includes rainfall, snowmelt and irrigation, observed precipitation includes
irrigation.
bObserved evapotranspiration calculated as: precipitation±runoff±drainage.
cSimulated erosion calculated externally from GLEAMS.
dSimulated for 1 m root zone to compare with ef¯uent from drains at 1 m depth.
ePO
4-Pparticulate P loss, simulated particulate P loss calculated externally from GLEAMS.
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) 0.550±0.558 0.535±0.538 0.540 0.520
Field capacity (FC, cm/cm) 0.360±0.520 0.380±0.500 0.425±0.505 0.425±0.475
Wilting point (WP, cm/cm) 0.200±0.305 0.200±0.320 0.360±0.395 0.395
Saturated conductivity (SATK, cm/h) 60.0 1.02 0.007±0.5 0.005±0.5
Saturated conductivity (RC, cm/h)a 0.005±0.5
Curve number (CN2) 78±92
Erosion/soil erodibility (KSOIL, t ha h/ha MJ mm) 0.16
Plant nutrient/total nitrogen (TN, %) 0.17±0.20 0.08 0.039 0.035
NO3±N concentration (CNIT, mg/g) 5.0±20 3.0±8.0 1.0±4.0 1.0±3.5
Potential min. N (POTMN, kg/ha) 700±715 148 214 268
Total phosphorus (TP, %) 0.06 0.04 0.02 0.016
Labile P concentration (CLAB,mg/g) 3.0 2.0 1.0 0.8
aBelow root depth.
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.
Observed and GLEAMS-simulated validation of water balance, erosion, and nutrient losses, plot B, 1987±93
Precipitationd(mm) 2760 2755 2755 2755 1560 1545 1545 1545
Runoff (mm) 1068 1026 1074 900 183 206 364 352
Drainage (mm) 323 336 272 528 582 578 244 286
Evapotranspiratione(mm) 1431 1384 1391 1325 795 754 902 886
Erosion, runoff (kg/ha) 7322 8520 10190 8330 648 760 2410 1590
Erosion, drainagef(kg/ha) 1066 1345 1090 2112 2511 2312 978 1147
N loss, runoff (kg/ha) 36.1 39.8 53.8 63.3 3.9 4.6 14.0 13.0
Soluble N loss, drainageg(kg/ha) 20.3 7.7 21.2 29.9 26.0 44.3 1.7 2.9
N loss, runoffdrainage (kg/ha) 56.4 47.5 75.0 93.2 29.9 48.9 15.7 15.9
P loss, runoff (kg/ha) 4.78 7.45 10.35 11.79 0.37 0.67 2.55 1.84
P loss, drainageh(kg/ha) 0.84 1.38 1.31 2.24 1.62 1.56 0.82 0.24
P loss, runoffdrainage (kg/ha) 5.62 8.83 11.66 14.03 1.99 2.03 3.37 2.08
aGLEAMS simulation using average parameter values from calibration on plots A and D.
bGLEAMS simulation using laboratory measurements for parameter values on plot B.
cGLEAMS simulation using model default parameters, including nitrogen and phosphorus initializations.
dSimulated precipitation includes rainfall and snowmelt.
eObserved evapotranspiration calculated as: precipitation±runoff±drainage.
fSimulated erosion calculated externally from GLEAMS
gSimulated for 1000 mm root zone for comparison with ef¯uent from drains at 1000 mm depth.
hPO
4-Pparticulate P loss, particulate P loss calculated externally from GLEAMS.
Information on parameter values from calibration on plots A and D, as explained in the previous section, was used to adjust some soil parameters for plot B in the post-drainage period, July 1991±93. Adjusted values of FC and SATK to represent improved drainage resulted in good agreement between simulated and observed runoff and percolation as shown in Table 6. Simulated N leaching was over-estimated by a factor of 2 in the post-drainage period, but P losses agreed relatively well. Simulation results for the 7-year study period compared very favorably with observed values for plot B.
The second validation simulation consisted of laboratory-determined parameters without any adjustments (labeled ``Laboratory values'' in Table 6). Results of the simulations were similar to those for ``Average parameters'' for the 1987±91 period, but were considerably different from the observed data for the after-drainage period, July 1991±1993. The reason for the big differences in the latter period is that properties of soils measured in the laboratory do not change with drainage as opposed to those in situ where boundary/drainage conditions affect the ``apparent'' water-retention and saturated-conductivity characteristics. An imposed drainage system represents an imposition on the normal matrix soil drainage, but the degree cannot be specified objectively, only subjectively. The adjustments obviously were not sufficient for good water-balance calculations as shown in Table 6. The water balance components are the carriers of detached eroded soil and plant nutrients, and good estimates of these components are essential to achieving good estimates of water quality constituents. Laboratory-determined parameters, measured for the pre-improved drainage period gave reasonable results, but runoff was over-estimated by a factor of 2 for the after-drainage period, and percolation was under-estimated by a factor of 2.4 for the period. Similar over- and under-estimates were simulated for erosion and plant nutrients.
The third validation simulation used model default parameter values, and the results are labeled ``Default parameters'' in Table 6 for plot B. Default values for plant nutrient initializations are based upon averages for all treatments for all soil orders, and as such cannot be expected to adequately reflect local conditions. Over-all results were not as good for the 1987±May 1991 period as those for ``Average parameters'' or ``Laboratory values'', but were not greatly different from the ``Laboratory values''. As a matter of fact, both ``Laboratory values'' and ``Default parameters'' gave better agreement with observed nitrogen leached than did ``Average parameters''. More discussion of the observed data for all plots during like management is given in a later section.
Simulations using ``Default parameters'' for the after-drainage period again showed poor agreement with observed data for the same reasons. Lack of objectivity in adjusting parameter values to reflect improved drainage is obvious for the critical water-balance components. These comparisons show that different cropping system management can be simulated with the GLEAMS model, but an extreme management perturbation such as drainage cannot be easily represented without calibration data.
Results of the validation simulations are compared with observed data for plot C in Table 7. The same general trends as for plot B are evident, with some differences in degree. The comparisons are better for the before-drainage period than for the after-drainage period. These results for plot C strengthen the conclusions for plot B.
Observed and GLEAMS-simulated validation of water balance, erosion, and nutrient losses, plot C, 1987±93
Precipitationd(mm) 2760 2755 2755 2755 1560 1545 1545 1545
Runoff (mm) 1032 933 758 671 111 206 354 317
Drainage (mm) 304 322 385 585 626 582 256 415
Evapotranspiratione(mm) 1424 1565 1581 1512 823 735 852 770
Erosion, runoff (kg/ha) 5793 6320 4890 7250 449 550 100 500
Erosion, drainagef(kg/ha) 947 1231 1514 2420 2295 2328 1024 1661
N loss, runoff (kg/ha) 22.9 38.2 32.4 44.6 3.0 3.9 8.2 8.1
Soluble N loss, drainageg(kg/ha) 7.4 7.9 22.7 37.0 31.8 40.0 0.9 1.5
N loss, runoffdrainage (kg/ha) 30.3 46.1 55.1 81.6 34.8 43.9 9.1 9.6
P loss, runoff (kg/ha) 4.48 4.44 4.81 7.52 0.37 0.43 0.84 1.00
P loss, drainageh(kg/ha) 0.75 1.17 1.55 2.54 1.53 0.50 0.41 1.49
P loss, runoffdrainage (kg/ha) 5.23 5.61 6.36 10.06 1.90 0.93 1.25 2.49
aGLEAMS simulation using average parameter values from calibration on plots A and D.
bGLEAMS simulation using laboratory measurements for parameter values on plot B.
cGLEAMS simulation using model default parameters, including nitrogen and phosphorus initializations.
dSimulated precipitation includes rainfall and snowmelt.
eObserved evapotranspiration calculated as: precipitation±runoff±drainage.
fSimulated erosion calculated externally from GLEAMS.
gSimulated for 1000 mm root zone for comparison with ef¯uent from drains at 1000 mm depth.
hPO
4-P particulate P loss, particulate P loss calculated externally from GLEAMS.
observed site-specific data, parameter adjustments are difficult to make for extreme management changes such as improved drainage. It cannot be determined from these observed and simulated data whether or not differences between un-drained soils and some degree of drainage can be simulated effectively.
4.4. Model simulations and observations for uniform plot treatment
Results of the calibration and validation studies are evidence that a model (any model) may perform very well or very poor if data are available for only one plot (field) at a
Table 8
Observed data, plots A, B, C, and D, 1990±93
Year Component Plot
A B C D
1990 Runoff (mm) 206 225 272 296
Drainage (mm) 106 68 48 33
Erosion, runoff (kg/ha) 1682 1636 1940 2158
Erosion, drainage (kg/ha) 506 293 148 120
N loss, runoff (kg/ha) 6.7 6.4 4.5 4.3
Soluble N loss, drainage (kg/ha) 7.9 4.5 1.1 0.9
P loss, runoff (kg/ha) 1.17 1.11 1.56 1.71
P loss, drainage (kg/ha) 0.52 0.27 0.11 0.09
1991a Runoff (mm) 98±11 136±7 133±6 151±13
Drainage (mm) 69±160 34±129 36±133 26±144
Erosion, runoff (kg/ha) 493±82 655±62 672±32 730±123
Erosion, drainage (kg/ha) 306±1000 154±901 125±826 92±970
N loss, runoff (kg/ha) 4.1±0.3 3.7±0.2 4.0±0.1 3.3±0.2
Soluble N loss, drainage (kg/ha) 3.9±11.3 1.5±6.9 1.2±10.0 0.6±6.4
P loss, runoff (kg/ha) 0.33±0.08 0.41±0.05 0.37±0.03 0.46±0.10
P loss, drainage (kg/ha) 0.22±0.72 0.08±0.60 0.06±0.44 0.06±0.70
1992 Runoff (mm) 55 57 37 71
Drainage (mm) 295 321 333 322
Erosion, runoff (kg/ha) 148 122 106 146
Erosion, drainage (kg/ha) 928 888 826 918
N loss, runoff (kg/ha) 0.9 0.9 0.9 1.2
Soluble N loss, drainage (kg/ha) 13.8 10.9 13.5 10.6
P loss, runoff (kg/ha) 0.12 0.13 0.08 0.18
P loss, drainage (kg/ha) 0.57 0.56 0.52 0.57
1993 Runoff (mm) 81 119 68 133
Drainage (mm) 140 132 161 122
Erosion, runoff (kg/ha) 465 454 311 509
Erosion, drainage (kg/ha) 877 722 833 669
N loss, runoff (kg/ha) 2.1 2.7 2.0 2.7
Soluble N loss, drainage (kg/ha) 9.0 8.2 8.3 7.1
P loss, runoff (kg/ha) 0.36 0.19 0.27 0.55
P loss, drainage (kg/ha) 0.62 0.45 0.57 0.42
aImproved drainage June, 1991: ®rst value, January±May; second value, July±December.
location. The calibration study showed differences between plot responses when the plots were treated alike (1990±93). Observed values for all plots are shown by year for the 1990±93 period in Table 8. Effects of differential treatment may carry over for some time before similar response occurs. For example, the bare-fallow treatment did not produce mineralizable residue, and fertilizer was not applied during the 1987±89 differential treatment period. A new level or status, including soil physical condition, resulted by 1990 when the same treatment was imposed on all plots. Deficiencies, in the case of plot A, or surpluses, among plots had to be overcome (evened out) before responses can be evened. Therefore, it is not known if the plot differences, especially in 1991±93, are carry-over effects of treatment differential or inherent plot differences which may not be discernable by physical measurements or sampling.
The year-by-year comparisons of observed data shown in Table 8 reveal considerable differences for all components during the uniform treatment. Runoff, where the total volume is measured with a tipping bucket device, was highest each year on plot D, but measurements on the other plots varied from plot-to-plot each year. Observed drainage, also measured with a tipping bucket device, did not show corresponding results of lowest drainage on plot D, as would be expected from high runoff, during the like-treatment period 1990±93. Knisel and Leonard (1990) discussed model simulations for ``representative'', short-term, and long-term climatic records. Leonard and Knisel (1990) showed how even long-term data may not be adequate for model validation. With these ideas, and with the observed data in Table 8 for the period of like treatment on the research plots, results of GLEAMS model calibration and validation are relatively good.
5. Summary and conclusions
The GLEAMS model was modified to better represent the climate and soils of Finland. Potential evaporation from frozen soil in winter was reduced to give a better estimate of simulated actual evapotranspiration. Also, rainfall energy calculations in the model were reduced for high northern latitudes to better represent the low intensity rainfall normally experienced in Finland.
Plot data from 1987±93 were used to calibrate and validate GLEAMS for different cropping systems on heavy clay soil. Available data were used to determine if the model can be used with confidence to evaluate management alternatives in estimating nonpoint source pollutant loadings from agriculture. The following conclusions can be drawn from the study:
GLEAMS can be used to represent clay soils, crops, climate, and management in southern Finland.
Continuous bare fallow (for more than a year) can be represented in the model, but some finite ``effective rooting depth'' must be used for evaporation purposes. Laboratory-measured soil data must be adjusted for model applications to represent in situ field conditions. Soil characteristics (parameters) must be adjusted to represent some drainage conditions. Field data should be used for fine tuning of parameters if at all possible.
In the absence of in situ water retention and saturated hydraulic conductivity for improved drainage conditions, observed runoff and drainage data are required in order to adjust parameter values to represent the extreme management change. Adjustments cannot be made objectively to adequately simulate water balance components.
GLEAMS should not be used in a predictive mode for absolute predictions of water volumes or erosion and chemical masses. It should be used only for relative comparisons of alternate management systems.
Response of the P component of GLEAMS compares very favorably with observed data, but initial conditions (model phosphorus pools) may be sensitive for comparisons with observed output. Particulate P in drainage water is not considered in GLEAMS, but calculation of particulate P loss can be done with an external computational procedure.
The N component of GLEAMS gives reasonable results compared with observed data.
Initialization of the model N pools are sensitive for comparison with observed data, especially in the first simulation year. NO3-N leaching is generally over estimated with
GLEAMS.
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