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Exponent that governs LAI decline rate
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Perennial leaf turnover rate with maximum stress
Above-ground biomass that dies off at dormancy
Root to shoot ratio at the beginning of the growing season
Root to shoot ratio at the end of the growing season
Plant population corresponding to the 1st point on the population LAI curve
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Fraction of field capacity to initiate growth of tropical plants during monsoon season
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Wind erosion factor for dead residue
Wind erosion factor for flat residue
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Concentration of NO3-N in suspended solid load from impervious areas
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Depth of mixing caused by the tillage operation
Random roughness
Ridge height
Ridge spacing
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Fraction of fertilizer that is organic N
Fraction of fertilizer that is organic P
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Aquatic pesticide reaction coefficient
Aquatic volatilization coefficient
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Aquatic resuspension velocity for pesticide sorbed to sediment
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There are several databases included in SWAT+:
Plants/land cover: plants.plt
Urban land use: urban.urb
Tillage: tillage.til
Fertilizer and manure: fertilizer.frt
Pesticides: pesticide.pes
Septic systems: septic.sep
The plant growth database file stores information required to simulate plant growth by plant species.
The plant growth database distributed with SWAT+ includes parameters for most of the common plant species. If a user needs to model a land use or plant not included in the database, please feel free to contact the SWAT+ development team for assistance in determining plant parameters.
Field | Description | Type | Unit | Default | Range |
---|---|---|---|---|---|
Name of the plant/landcover
string
n/a
n/a
n/a
Plant/landcover type
string
n/a
n/a
n/a
Phenology trigger
string
n/a
n/a
n/a
Nitrogen fixation coefficient
real
none
0.0
n/a
Days to maturity
real
days
110.0
0.0-300.0
Biomass-energy ratio
real
(kg/ha/(MJ/m^2)
15.0
10.0-90.0
Harvest index for optimal growth conditions
real
(kg/ha)/(kg/ha)
0.76
0.01-1.25
Maximum potential leaf area index
real
none
5.0
0.50-10.0
Fraction of the growing season heat units corresponding to the 1st point on optimal leaf area development curve
real
fraction
0.05
0.0-1.0
Fraction of the maximum leaf area index corresponding to the 1st point on optimal leaf area development curve
real
fraction
0.05
0.0-1.0
Fraction of the growing season heat units corresponding to the 2nd point on optimal leaf area development curve
real
fraction
0.40
0.0-1.0
Fraction of the maximum leaf area index corresponding to the 2nd point on optimal leaf area development curve
real
fraction
0.95
0.0-1.0
Fraction of growing season when leaf area begins to decline
real
fraction
0.99
0.2-1.0
Exponent that governs the LAI decline rate
real
n/a
1.0
Maximum canopy height
real
m
6.0
0.1-20.0
Maximum rooting depth
real
m
3.50
0.0-3.0
Optimal temperature for plant growth
real
deg c
30.0
11.0-38.0
Minimum temperature for plant growth
real
deg c
10.0
0.0-18.0
Normal fraction of N in yield
real
kg N/kg yield
0.0015
0.0015-0.075
Normal fraction of P in yield
real
kg P/kg yield
0.0003
0.0015-0.0075
Normal fraction of N in plant biomass at emergence
real
kg N/kg biomass
0.006
0.004-0.07
Normal fraction of N in plant biomass at 50% maturity
real
kg N/kg biomass
0.002
0.002-0.05
Normal fraction of N in plant biomass at maturity
real
kg N/kg biomass
0.0015
0.001-0.27
Normal fraction pf P in plant biomass at emergence
real
kg P/kg biomass
0.0007
0.0005-0.01
Normal fraction of P in plant at 50% maturity
real
kg P/kg biomass
0.0004
0.0002-0.007
Normal fraction of P in plant at maturity
real
kg P/kg biomass
0.0003
0.0003-0.0004
Harvest index that represents the lowest harvest index expected due to water stress
real
(kg/ha)/(kg/ha)
0.01
-0.2-1.1
Minimum value of the USLE C factor for water erosion
real
none
0.001
0.001-0.50
Maximum stomatal conductance
real
m/s
0.002
0.0-0.50
Vapor pressure deficit corresponding to the 2nd point on the stomatal conductance curve
real
kPa
4.0
1.5-6.0
Fraction of maximum stomatal conductance corresponding to the 2nd point on the stomatal conductance curve
read
fraction
0.75
0.0-1.0
Rate of decline in radiation use efficiency per unit increase in vapor pressure deficit
real
none
8.0
0.0-50.0
Elevated CO2 atmospheric concentration corresponding the 2nd point on the radiation use efficiency curve
real
μL CO2/L air
660.0
300.0-1000.0
Biomass-energy ratio corresponding to the 2nd point on the radiation use efficiency curve
real
(kg/ha)/(MJ/m^2)
16.0
5.0-100.0
Plant residue decomposition coefficient
real
none
0.05
0.01-0.099
Minimum LAI during dormant period
real
m^2/m^2
0.75
0.00-0.99
Fraction of biomass accumulated each year
real
fraction
0.30
0.0-1.0
Years to maturity
integer
years
10
0-100
Maximum forest biomass
real
metric tons/ha
1000.0
0.0-5000.0
Light extinction coefficient
real
none
0.65
0.0-2.0
Perennial leaf turnover rate with minimum stress
real
12.0
Perennial leaf turnover rate with maximum stress
real
3.0
Above-ground biomass that dies off at dormancy
real
fraction
1.0
0.0-1.0
Root to shoot ratio at the beginning of the growing season
real
fraction
0.
Root to shoot ratio at the end of the growing season
real
fraction
0.
Plant population corresponding to the 1st point on the population LAI curve
real
plants/m^2
0.
Fraction of the maximum leaf area index corresponding to the 1st point on the leaf area development curve
real
fraction
0.
0.0-1.0
Plant population corresponding to the 2nd point on the population LAI curve
real
plants/m^2
0.0
Fraction of the maximum leaf area index corresponding to the 2nd point on the leaf area development curve
real
fraction
0.0
0.0-1.0
Fraction of field capacity to initiate growth of tropical plants during monsoon season
real
fraction
0.5
0.0-1.0
Aeration stress factor
real
0.0
Wind erosion factor for standing dead residue
real
0.0
Wind erosion factor for flat residue
real
0.0
Phenology trigger
Nitrogen fixation coefficient
The nitrogen fixation coefficient in the SWAT+ plant database is 0.5 for legumes and 0.0 for non-legumes.
Days to maturity
We took the PHU program and incorporated it directly into SWAT+. We changed the heat units to maturity to days to maturity (days_mat). The concept of heat units to maturity was developed for annual crops and we use heat units for the entire growing season for native perennials and native annuals. By inputting days to maturity, we can include different crop varieties as defined by length of growing season (e.g., 120-, 110-, 100- and 90-day varieties for corn). The heat units to maturity calculation in the model first computes base zero heat units for the entire year and assumes a planting date when heat units exceed 0.15 * base zero. Then, the model calculates heat units from planting date through the days to maturity using the crop's base temperature as specified by tmp_base. If the maximum days for a crop are input, e.g., 120 days for corn, and the growing season is less than 120 days, the model essentially sums heat units for the entire growing season which represents (and estimates) the maximum days to maturity. This is the exact procedure used in the PHU program.
The algorithm currently uses monthly weather generator parameters but could be modified to alternatively use daily temperature inputs. The model provides heat unit estimates in both the northern and southern hemispheres.
There are several advantages to incorporating the heat unit program into SWAT+ including:
It eliminates the need for running an external program when developing inputs,
allows input of a commonly understood variable (days) instead of a variable that is not commonly known at every location (heat units),
allows the model to calculate heat units for native perennials and annuals that are location dependent,
a database (plants.plt) can be maintained and supported that includes different crop varieties, and
by inputting the maximum growing season for a crop, the model will calculate appropriate heat units for that crop anywhere in the northern or southern hemisphere.
Plant/landcover type
Fall-planted land covers will go dormant when daylength is less than the threshold daylength.
Name of the plant/landcover
The names in the plant database are also used by QSWAT+ to link the grid codes in land use/land cover maps to SWAT+ land use types.
Harvest index for optimal growth conditions
To determine the harvest index, the plant biomass removed during the harvest operation is dried at least 2 days at 65°C and weighed. The total aboveground plant biomass in the field should also be dried and weighed. The harvest index is then calculated by dividing the weight of the harvested portion of the plant biomass by the weight of the total aboveground plant biomass. Plants will need to be grown in two different plots where optimal climatic conditions and stressed conditions are produced to obtain values for both harvest indices.
Biomass-energy ratio
The biomass-energy ration or radiation-use efficiency (RUE) is the amount of dry biomass produced per unit intercepted solar radiation. It is assumed to be independent of the plant’s growth stage. The variable bm_e represents the potential or unstressed growth rate (including roots) per unit of intercepted photosynthetically active radiation.
This parameter can greatly change the rate of growth, incidence of stress during the season, and the resultant yield. It should be one of the last to be adjusted. Adjustments should be based on research results. Care should be taken to make adjustments based only on data with no drought, nutrient, or temperature stress.
The following overview of the methodology used to measure RUE was summarized from Kiniry et al. (1998) and Kiniry et al. (1999).
To calculate RUE, the amount of photosynthetically active radiation (PAR) intercepted and the mass of aboveground biomass is measured several times throughout a plant’s growing season. The frequency of the measurements taken will vary, but in general 4 to 7 measurements per growing season are considered to be adequate. As with leaf area determinations, the measurements should be performed on non-stressed plants. Intercepted radiation is measured with a light meter. Whole spectrum and PAR sensors are available and calculations of RUE will be performed differently depending on the sensor used. A brief discussion of the difference between whole spectrum and PAR sensors and the difference in calculations is given in Kiniry (1999). The use of a PAR sensor in RUE studies is strongly encouraged.
When measuring radiation, three to five sets of measurements are taken rapidly for each plant plot. A set of measurements consists of 10 measurements above the leaf canopy, 10 below, and 10 more above. The light measurements should be taken between 10:00 am and 2:00 pm local time. The measurements above and below the leaf canopy are averaged and the fraction of intercepted PAR is calculated for the day from the two values. Daily estimates of the fraction of intercepted PAR are determined by linearly interpolating the measured values. The fraction of intercepted PAR is converted to an amount of intercepted PAR using daily values of incident total solar radiation measured with a standard weather station. To convert total incident radiation to total incident PAR, the daily solar radiation values are multiplied by the percent of total radiation that has a wavelength between 400 and 700 mm. This percent usually falls in the range 45 to 55% and is a function of cloud cover. 50% is considered to be a default value. Once daily intercepted PAR values are determined, the total amount of PAR intercepted by the plant is calculated for each date on which biomass was harvested. This is calculated by summing daily intercepted PAR values from the date of seedling emergence to the date of biomass harvest.
To determine biomass production, aboveground biomass is harvested from a known area of land within the plot. The plant material should be dried at least 2 days at 65°C and then weighed.
RUE is determined by fitting a linear regression for aboveground biomass as a function of intercepted PAR. The slope of the line is the RUE. The figure below shows the plots of aboveground biomass and summed intercepted photosynthetically active radiation for Eastern gamagrass. Note that the units for RUE values in the graph, as well as values typically reported in literature, are different from those used by SWAT+. To obtain the value used in SWAT+, multiply by 10.
Kiniry et al. (1998)
Kiniry (1999)
Kiniry et al. (1999)
Option | Description |
---|---|
The name of the plant/landcover is a primary key referenced by in . All names in the plants.plt database must be unique.
Name | Description |
---|
The harvest index defines the fraction of the aboveground biomass that is removed in a harvest operation. This value defines the fraction of plant biomass that is “lost” from the system and unavailable for conversion to residue and subsequent decomposition. For crops where the harvested portion of the plant is aboveground, the harvest index is always a fraction less than 1. For crops where the harvested portion is belowground, the harvest index may be greater than 1. Two harvest indices are provided in the database, the harvest index for optimal growing conditions and the harvest index under highly stressed growing conditions ().
perennial
Perennial plants
warm_annual
Warm-season annuals
cold_annual
Cold-season annuals
agrc | Generic agricultural land |
agrl | Agricultural land with row crops |
agrr | Close-grown agricultural land |
alfa | Alfalfa |
almd | Almonds |
appl | Apple |
aspn | Aspen |
aspr | Asparagus |
bana | Banana |
barl | Barley |
barl100 | Barley - 100-day variety |
barl105 | Barley - 105-day variety |
bbls | Little bluestem |
berm | Bermudagrass |
blug | Kentucky bluegrass |
bocu | ??? |
broc | Broccoli |
brom | Meadow bromegrass |
bros | Smooth bromegrass |
bsvg | Barren or sparsely vegetated land |
cabg | Cabbage |
cana | Spring canola Argentina |
cang | Canary Grass |
canp | Spring canola Poland |
cant | Cantaloupe |
cash | Cashews |
cauf | Cauliflower |
cedr | Cedar |
celr | Celery |
clva | Alsike clover |
clvr | Red clover |
clvs | Sweet clover |
cngr | Canada grass |
cocb | Cockle burr |
coco | Coconut |
coct | Cocoa |
coff | Coffee |
cont | ??? |
corn | Corn |
corn100 | Corn - 100-day variety |
corn110 | Corn - 110-day variety |
corn120 | Corn - 120-day variety |
corn50 | Corn - 50-day variety |
corn90 | Corn - 90-day variety |
cotp | Cotton |
cotp135 | Cotton - 135-day variety |
cotp145 | Cotton - 145-day variety |
cotp155 | Cotton - 155-day variety |
cotp180 | Cotton - 180-day variety |
cots | Cotton |
cots135 | Cotton - 135-day variety |
cots145 | Cotton - 145-day variety |
cots155 | Cotton - 155-day variety |
cots180 | Cotton - 180-day variety |
crdy | Dryland cropland and pasture |
crgr | Cropland/grassland mosaic |
crir | Irrigated cropland and pasture |
crrt | Carrot |
crwo | Cropland/woodland mosaic |
csil | Corn silage |
csil100 | Corn silage - 100-day variety |
csil110 | Corn silage - 110-day variety |
csil120 | Corn silage - 120-day variety |
csil90 | Corn silage - 90-day variety |
cucm | Cucumber |
cwgr | Crested wheatgrass |
cwps | Cowpeas |
deil | ??? |
dwht | Durum wheat |
dwht110 | Durum wheat - 110-day variety |
dwht120 | Durum wheat - 120-day variety |
egam | Eastern Gamagrass |
eggp | Eggplant |
fesc | Tall Fescue |
flax | Flax |
fodb | Deciduous broadleaf forest |
fodn | Deciduous needleleaf forest |
foeb | Evergreen broadleaf forest |
foen | Evergreen needleleaf forest |
fomi | Mixed forest |
fpea | Field peas |
frsd | Deciduous forest |
frsd_suhf | Deciduous forest - subtropical humid |
frsd_sums | Deciduous forest - subtropical mountain systems |
frsd_sust | Deciduous forest - subtropical steppe |
frsd_tecf | Deciduous forest - temperate continental |
frsd_tems | Deciduous forest - temperate mountain systems |
frsd_teof | Deciduous forest - temperate oceanic |
frsd_test | Deciduous forest - temperate steppe |
frse | Evergreen forest |
frse_sudrf | Evergreen forest - subtropical desert |
frse_suds | Evergreen forest - subtropical dry |
frse_suhf | Evergreen forest - subtropical humid |
frse_sums | Evergreen forest - subtropical mountain systems |
frse_sust | Evergreen forest - subtropical steppe |
frse_tecf | Evergreen forest - temperate continental |
frse_teds | Evergreen forest - temperate desert |
frse_tems | Evergreen forest - temperate mountain systems |
frse_teof | Evergreen forest - temperate oceanic |
frse_test | Evergreen forest - temperate steppe |
frst | Mixed forest |
frst_suhf | Mixed forest - subtropical humid |
frst_sums | Mixed forest - subtropical mountain systems |
frst_sust | Mixed forest - subtropical steppe |
frst_tecf | Mixed forest - temperate continental |
frst_tems | Mixed forest - temperate mountain systems |
frst_teof | Mixed forest - temperate oceanic |
frst_test | Mixed forest - temperate steppe |
grap | Grape |
grar | Red grape |
gras | Grassland |
grbn | Green beans |
grsg | Grain sorghum |
grsg100 | Grain sorghum - 100-day variety |
grsg105 | Grain sorghum - 105-day variety |
grsg110 | Grain sorghum - 110-day variety |
grsg95 | Grain sorghum - 95-day variety |
hay | Hay |
hmel | Honeydew melon |
indn | Indianagrass |
jhgr | Johnsongrass |
ldgp | Lodgepole pine |
lent | Lentils |
lett | Head lettuce |
lima | Lima beans |
mapl | Maple |
mesq | Honey mesquite |
migs | Mixed grassland/shrubland |
mint | Mint |
mixc | Mixed dryland/irrigated cropland |
mung | Mung beans |
oak | Oak |
oats | Oats |
oats110 | Oats - 110-day variety |
oats120 | Oats - 120-day variety |
oilp | Oil palm |
oliv | Olives |
onio | Onions |
oran | Oranges |
orcd | Orchard |
papa | Papaya |
part | Parthenium |
past | Pasture |
peas | Garden peas |
pepp | Pepper |
pepr | Bell pepper |
pine | Pine |
pinp | Pineapple |
plan | Plaintains |
pmil | Pearl millet |
pmil100 | Pearl millet - 100-day variety |
pmil105 | Pearl millet - 105-day variety |
pmil110 | Pearl millet - 110-day variety |
pmil95 | Pearl millet - 95-day variety |
pnut | Peanut |
popl | Poplar |
popy | Poppy |
pota | Potato |
ptbn | Pinto beans |
radi | Radish |
rice | Rice |
rice120 | Rice - 120-day variety |
rice140 | Rice - 140-day variety |
rice160 | Rice - 160-day variety |
rice180 | Rice - 180-day variety |
rngb | Range brush |
rngb_sudrf | Range brush - subtropical dry forest |
rngb_suds | Range brush - subtropical desert |
rngb_suhf | Range brush - subtropical humid forest |
rngb_sums | Range brush - subtropical mountain systems |
rngb_sust | Range brush - subtropical steppe |
rngb_tecf | Range brush - temperate continental forest |
rngb_teds | Range brush - temperate desert |
rngb_tems | Range brush - temperate mountain systems |
rngb_teof | Range brush - temperate oceanic forest |
rngb_test | Range brush - temperate steppe |
rnge | Range grasses |
rnge_sudrf | Range grasses - subtropical dry forest |
rnge_suds | Range grasses - subtropical desert |
rnge_suhf | Range grasses - subtropical humid forest |
rnge_sums | Range grasses - subtropical mountain systems |
rnge_sust | Range grasses - subtropical steppe |
rnge_tecf | Range grasses - temperate continental forest |
rnge_teds | Range grasses - temperate desert |
rnge_tems | Range grasses - temperate mountain systems |
rnge_teof | Range grasses - temperate oceanic forest |
rnge_test | Range grasses - temperate steppe |
rubr | Rubber |
rye | Rye |
rye90 | Rye - 90-day variety |
ryea | Altai wildrye |
ryeg | Italian annual ryegrass |
ryer | Russian wildrye |
saaz | ??? |
sava | Savanna |
scrn | Sweetcorn |
scsc | ??? |
sept | Septic area |
sesb | Sesban |
sgbt | Sugarbeet |
sghy | Sorghum hay |
shrb | Shrubland |
side | Sideoats grama |
soct | ??? |
sont | ??? |
sonu | ??? |
sosa | ??? |
soyb | Soybeans |
soyb100 | Soybeans - 100-day variety |
soyb105 | Soybeans - 105-day variety |
soyb110 | Soybeans - 110-day variety |
soyb115 | Soybeans - 115-day variety |
soyb120 | Soybeans - 120-day variety |
spas | Summer pasture |
spin | Spinach |
spot | Sweet potato |
strw | Strawberry |
sugc | Sugarcane |
sunf | Sunflower |
sunf100 | Sunflower - 100-day variety |
sunf110 | Sunflower - 110-day variety |
sunf90 | Sunflower - 90-day variety |
swch | Alamo switchgrass |
swgr | Slender wheatgrass |
swht | Spring wheat |
swht110 | Spring wheat - 110-day variety |
swht120 | Spring wheat - 120-day variety |
swrn | Southwestern US ??? |
teff | Teff |
timo | Timothy grass |
tobc | Tobacco |
toma | Tomato |
tral | ??? |
trit | Triticale |
tubg | Bare ground tundra |
tuhb | Herbaceous tundra |
tumi | Mixed tundra |
tuwo | Wooded tundra |
urbn_cool | Cool-season grass in urban areas |
urbn_warm | Warm-season grass in urban areas |
waln | Walnut |
wbar | Winter barley |
wehb | Herbaceous wetland |
wetf | Forested wetland |
wetl | Wetland |
wetn | Non-forested wetland |
wewo | Wooded wetland |
will | Willow |
wmel | Watermelon |
wpas | Winter pasture |
wspr | White spruce |
wwgr | Western wheatgrass |
wwht | Winter wheat |
wwht150 | Winter wheat - 150-day variety |
wwht160 | Winter wheat - 160-day variety |
wwht170 | Winter wheat - 170-day variety |
Maximum potential leaf area index
The variable lai_pot is one of six parameters used to quantify leaf area development of a plant species during the growing season. The figure below illustrates the relationship of the database parameters to the leaf area development modeled by SWAT+.
The values for lai_pot in the plant growth database are based on average plant densities in dryland (rainfed) agriculture. The parameter may need to be adjusted for drought-prone regions where planting densities are much smaller or irrigated conditions where densities are much greater.
To identify the leaf area development parameters, record the leaf area index and number of accumulated heat units for the plant species throughout the growing season and then plot the results. For best results, several years worth of field data should be collected. At the very minimum, data for two years is recommended. It is important that the plants undergo no water or nutrient stress during the years in which data is collected.
The leaf area index incorporates information about the plant density, so field experiments should either be set up to reproduce actual plant densities or the maximum LAI value for the plant determined from field experiments should be adjusted to reflect plant densities desired in the simulation. Maximum LAI values in the default database correspond to plant densities associated with rainfed agriculture.
The leaf area index is calculated by dividing the green leaf area by the land area. Because the entire plant must be harvested to determine the leaf area, the field experiment needs to be designed to include enough plants to accommodate all leaf area measurements made during the year.
Although measuring leaf area can be laborious for large samples, there is no intrinsic difficulty in the process. The most common method is to obtain an electronic scanner and feed the harvested green leaves and stems into the scanner. Older methods for estimating leaf area include tracing of the leaves (or weighed subsamples) onto paper, the use of planimeters, the punch disk method of Watson (1958) and the linear dimension method of Duncan and Hesketh (1968).
Chapter 5:1 in the Theoretical Documentation reviews the methodology used to calculate accumulated heat units for a plant at different times of the year as well as determination of the fraction of total, or potential, heat units that is required for the plant database.
Watson (1958)
Duncan and Hesketh (1968)
Fraction of the plant growing season or the total potential heat units corresponding to the 1st point on optimal leaf area development curve
Fraction of the maximum leaf area index corresponding to the 1st point on optimal leaf area development curve
Please see the explanation given for parameter to obtain additional information about this parameter and methods used to measure it.
Please see the explanation given for parameter to obtain additional information about this parameter and methods used to measure it.
Maximum canopy height
Maximum canopy height is a straightforward measurement. The canopy height of non-stressed plants should be recorded at intervals throughout the growing season. The maximum value recorded is used in the database.
Fraction of the plant growing season or the total potential heat units corresponding to the 2nd point on optimal leaf area development curve
Please see the explanation given for parameter lai_pot to obtain additional information about this parameter and methods used to measure it.
Fraction of the maximum leaf area index corresponding to the 2nd point on optimal leaf area development curve
Please see the explanation given for parameter lai_pot to obtain additional information about this parameter and methods used to measure it.
Fraction of growing season when leaf area begins to decline
Please see the explanation given for parameter lai_pot to obtain additional information about this parameter and methods used to measure it.
Minimum temperature for plant growth
SWAT+ uses the base temperature to calculate the number of heat units accrued every day. The minimum or base temperature for plant growth varies with growth stage of the plant. However, this variation is ignored by the model - SWAT+ uses the same base temperature throughout the growing season.
Base temperature is measured by growing plants in growth chambers at several different temperatures. The rate of leaf tip appearance as a function of temperature is plotted. Extrapolating the line to the leaf tip appearance rate of 0.0 leaves/day gives the base or minimum temperature for plant growth. The figure below plots data for corn. Note that the line intersects the x-axis at 8°C.
Maximum rooting depth
To determine maximum rooting depth, plant samples need to be grown on soils without an impermeable layer. Once the plants have reached maturity, soil cores are taken for the entire depth of the soil. Each 0.25-meter increment is washed and the live plant material collected. Live roots can be differentiated from dead roots by the fact that live roots are whiter and more elastic and have an intact cortex. The deepest increment of the soil core in which live roots are found defines the maximum rooting depth.
Normal fraction of phosphorus in yield
In addition to the amount of plant biomass removed in the yield, SWAT+ needs to know the amount of nitrogen and phosphorus removed in the yield. The harvested portion of the plant biomass is sent to a testing laboratory to determine the fraction of nitrogen and phosphorus in the biomass.
This value is estimated on a dry weight basis.
Normal fraction of nitrogen in yield
In addition to the amount of plant biomass removed in the yield, SWAT+ needs to know the amount of nitrogen and phosphorus removed in the yield. The harvested portion of the plant biomass is sent to a testing laboratory to determine the fraction of nitrogen and phosphorus in the biomass.
This value is estimated on a dry weight basis.
Optimal temperature for plant growth
Both optimal and base temperatures are very stable for cultivars within a species.
Optimal temperature for plant growth is difficult to measure directly. Looking at the Figure below, one might be tempted to select the temperature corresponding to the peak of the plot as the optimal temperature. This would not be correct. The peak of the plot defines the optimal temperature for leaf development—not for plant growth.
If an optimal temperature cannot be obtained through a review of literature, use the optimal temperature listed for a plant already in the database with similar growth habits.
Review of temperatures for many different plants have provided generic values for base and optimal temperatures as a function of growing season. In situations, where temperature information is unavailable, these values may be used. For warm season plants, the generic base temperature is ~8ºC and the generic optimal temperature is ~25ºC. For cool season plants, the generic base temperature is ~0ºC and the generic optimal temperature is ~13ºC.
Normal fraction of nitrogen in plant biomass at 50% maturity
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: frac_n_em, frac_n_50, frac_n_mat, frac_p_em, frac_p_50, and frac_p_mat. Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Normal fraction of nitrogen in plant biomass at maturity
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: frac_n_em, frac_n_50, frac_n_mat, frac_p_em, frac_p_50, and frac_p_mat. Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Normal fraction of phosphorus in plant biomass at emergence
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: frac_n_em, frac_n_50, frac_n_mat, frac_p_em, frac_p_50, and frac_p_mat. Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Normal fraction of phosphorus in plant at 50% maturity
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: frac_n_em, frac_n_50, frac_n_mat, frac_p_em, frac_p_50, and frac_p_mat. Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
where is the minimum C factor for the land cover and is the average annual C factor for the land cover.
Harvest index that represents the lowest harvest index expected due to water stress
The value between 0.0 and harv_idx that represents the lowest harvest index expected due to water stress.
The harvest index defines the fraction of the aboveground biomass that is removed in a harvest operation. This value defines the fraction of plant biomass that is “lost” from the system and unavailable for conversion to residue and subsequent decomposition. For crops where the harvested portion of the plant is aboveground, the harvest index is always a fraction less than 1. For crops where the harvested portion is belowground, the harvest index may be greater than 1. Two harvest indices are provided in the database, the harvest index for optimal growing conditions (harv_idx) and the harvest index under highly stressed growing conditions.
To determine the harvest index, the plant biomass removed during the harvest operation is dried at least 2 days at 65°C and weighed. The total aboveground plant biomass in the field should also be dried and weighed. The harvest index is then calculated by dividing the weight of the harvested portion of the plant biomass by the weight of the total aboveground plant biomass. Plants will need to be grown in two different plots where optimal climatic conditions and stressed conditions are produced to obtain values for both harvest indices.
Normal fraction of phosphorus in plant at maturity
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: frac_n_em, frac_n_50, frac_n_mat, frac_p_em, frac_p_50, and frac_p_mat. Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Vapor pressure deficit corresponding to the second point on the stomatal conductance curve
The first point on the stomatal conductance curve is comprised of a vapor pressure deficit of 1 kPa and a fraction of maximum stomatal conductance equal to 1.00.
As with radiation-use efficiency, stomatal conductance is sensitive to vapor pressure deficit. Stockle et al. (1992) compiled a short list of stomatal conductance response to vapor pressure deficit for a few plant species. Due to the paucity of data, default values for the second point on the stomatal conductance vs. vapor pressure deficit curve are used for all plant species in the database.
The fraction of maximum stomatal conductance (frac_stcon) is set to 0.75 and the vapor pressure deficit corresponding to the fraction given by vpd is set to 4.00 kPa. If the user has actual data, they should use those values, otherwise the default values are adequate.
Stockle et al. (1992)
Maximum stomatal conductance at high solar radiation and low vapor pressure deficit
Stomatal conductance of water vapor is used in the Penman-Monteith calculations of maximum plant evapotranspiration. The plant database contains three variables pertaining to stomatal conductance that are required only if the Penman-Monteith equation is used to model evapotranspiration: maximum stomatal conductance and two variables that define the impact of vapor pressure deficit on stomatal conductance (frac_stcon, vpd).
Körner et al. (1979) defines maximum leaf diffusive conductance as the largest value of conductance observed in fully developed leaves of well-watered plants under optimal climatic conditions, natural outdoor CO2 concentrations and sufficient nutrient supply. Leaf diffusive conductance of water vapor cannot be measured directly but can be calculated from measurements of transpiration under known climatic conditions. Various different methods are used to determine diffusive conductance: transpiration measurements in photosynthesis cuvettes, energy balance measurements or weighing experiments, ventilated diffusion porometers, and non-ventilated porometers. Körner (1977) measured diffusive conductance using a ventilated diffusion porometer.
To obtain maximum leaf conductance values, leaf conductance is determined between sunrise and late morning until a clear decline or no further increase is observed. Depending on phenology, measurements are taken on at least three bright days in late spring and summer, preferably just after a rainy period. The means of maximum leaf conductance of 5 to 10 samples each day are averaged, yielding the maximum diffusive conductance for the species. Due to the variation of the location of stomata on plant leaves for different plant species, conductance values should be calculated for the total leaf surface area.
Körner (1977)
Körner et al. (1979)
Rate of decline in radiation use efficiency per unit increase in vapor pressure deficit
Stockle and Kiniry (1990) first noticed a relationship between RUE and vapor pressure deficit and were able to explain a large portion of within-species variability in RUE values for sorghum and corn by plotting RUE values as a function of average daily vapor pressure deficit values. Since this first article, a number of other studies have been conducted that support the dependence of RUE on vapor pressure deficit. However, there is still some debate in the scientific community on the validity of this relationship. If the user does not wish to simulate a change in RUE with vapor pressure deficit, the variable ru_vpd can be set to 0.0 for the plant.
To define the impact of vapor pressure deficit on RUE, vapor pressure deficit values must be recorded during the growing seasons that RUE determinations are being made. It is important that the plants are exposed to no other stress than vapor pressure deficit, i.e. plant growth should not be limited by lack of soil water and nutrients.
Vapor pressure deficits can be calculated from relative humidity (see Chapter 1:2 in Theoretical Documentation) or from daily maximum and minimum temperatures using the technique of Diaz and Campbell (1988) as described by Stockle and Kiniry (1990). The change in RUE with vapor pressure deficit is determined by fitting a linear regression for RUE as a function of vapor pressure deficit. The figure below shows a plot of RUE as a function of vapor pressure deficit for grain sorghum.
From the figure, the rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit, Δruedcl, for sorghum is 8.4x10-1 g*MJ-1*kPa-1. When RUE is adjusted for vapor pressure deficit, the model assumes the RUE value reported for bm_e is the radiation-use efficiency at a vapor pressure deficit of 1 kPa.
The value of ru_vpd varies among species, but a value of 6 to 8 is suggested as an approximation for most plants.
Campbell (1988)
Stockle & Kiniry (1990)
Fraction of maximum stomatal conductance corresponding to the second point on the stomatal conductance curve
The first point on the stomatal conductance curve is comprised of a vapor pressure deficit of 1 kPa and a fraction of maximum stomatal conductance equal to 1.00.
As with radiation-use efficiency, stomatal conductance is sensitive to vapor pressure deficit. Stockle et al. (1992) compiled a short list of stomatal conductance response to vapor pressure deficit for a few plant species. Due to the paucity of data, default values for the second point on the stomatal conductance vs. vapor pressure deficit curve are used for all plant species in the database.
Stockle et al. (1992)
The fraction of maximum stomatal conductance (frac_stcon) is set to 0.75 and the vapor pressure deficit corresponding to the fraction given by is set to 4.00 kPa. If the user has actual data, they should use those values, otherwise the default values are adequate.
Biomass-energy ratio corresponding to the 2nd point on the radiation use efficiency curve
The 1st point on the radiation use efficiency curve is comprised of the ambient CO2 concentration, 330 μL CO2/L air, and the biomass-energy ratio reported for bm_e.
Please read the explanation for parameters co2_hi and bm_e to obtain additional information about this parameter and methods used to measure it.
Plant residue decomposition coefficient
The plant residue decomposition coefficient is the fraction of residue that will decompose in a day assuming optimal moisture, temperature, C:N ratio, and C:P ratio.
A default value of 0.05 is used for all plant species in the database, but users may vary decomposition by plant species.
Elevated CO2 atmospheric concentration corresponding the 2nd point on the radiation use efficiency curve
The 1st point on the radiation use efficiency curve is comprised of the ambient CO2 concentration, 330 μL CO2/L air, and the biomass-energy ratio reported for .
In order to assess the impact of climate change on agricultural productivity, SWAT+ incorporates equations that adjust RUE for elevated atmospheric CO2 concentrations. Values must be entered for co2_hi and in the plant database whether or not the user plans to simulate climate change.
For simulations in which elevated CO2 levels are not modeled, co2_hi should be set to some number greater than 330 ppmv and should be set to some number greater than .
To obtain radiation-use efficiency values at elevated CO2 levels for plant species not currently in the database, plants should be established in growth chambers set up in the field or laboratory where CO2 levels can be controlled. RUE values are determined using the same methodology described in the explanation of .
Light extinction coefficient
This coefficient is used to calculate the amount of intercepted photosynthetically active radiation.
Differences in canopy structure for a species are described by the number of leaves present (leaf area index) and the leaf orientation. Leaf orientation has a significant impact on light interception and consequently on radiation-use efficiency. More erect leaf types spread the incoming light over a greater leaf area, decreasing the average light intensity intercepted by individual leaves (see figure below). A reduction in light intensity interception by an individual leaf favors a more complete conversion of total canopy-intercepted light energy into biomass.
Using the light extinction coefficient value (kℓ) in the Beer-Lambert formula to quantify efficiency of light interception per unit leaf area index, more erect leaf types have a smaller kℓ.
To calculate the light extinction coefficient, the amount of photosynthetically active radiation (PAR) intercepted and the mass of aboveground biomass (LAI) is measured several times throughout a plant’s growing season using the methodology described in the previous sections. The light extinction coefficient is then calculated using the Beer-Lambert equation:
or
where TPAR is the transmitted photosynthetically active radiation, and PAR is the incoming photosynthetically active radiation.
Minimum LAI during dormant period
This variable pertains to perennials and trees only. It is not used for other types of plants.
Please see the explanation given for parameter lai_pot to obtain additional information about this parameter and methods used to measure it.
Fraction of biomass accumulated each year that is converted to residue during dormancy
This variable pertains to trees only. It is not used for other types of plants. It governs the amount of biomass that falls off the tree and is converted to residue when the plant goes dormant in the winter.
Maximum forest biomass
This variable pertains to trees only. It is not used for other types of plants. The maximum biomass for a mature forest stand generally falls in the range of 30-50 metric tons/ha.
Years to maturity
This variable pertains to trees only. It is not used for other types of plants.
Perennial leaf turnover rate with minimum stress
Fraction of the maximum leaf area index corresponding to the 1st point on the leaf area development curve
Plant population corresponding to the 2nd point on the population LAI curve
Aeration stress factor
The urban database summarizes parameters used by the model to simulate different types of urban areas.
Field | Description | Type | Unit | Default | Range |
---|---|---|---|---|---|
Name of the urban land type
string
n/a
n/a
n/a
Fraction of total impervious area in urban land type
real
fraction
0.05
0.0-1.0
Fraction of directly connected impervious area in urban land type
real
fraction
0.05
0.0-1.0
Curb length density
real
km/ha
0.0
0.0-1.0
Wash-off coefficient for removal of constituents from impervious surfaces
real
1/mm
0.0
0.0-1.0
Maximum amount of solids allowed to build up on impervious surfaces
real
kg/curb km
1000.0
0.0-2000.0
Time for amount of solids on impervious areas to build up to 1/2 of maximum level
real
days
1.0
0.0-100.0
Concentration of total N in suspended solid load from impervious areas
real
mg/kg
0.0
0.0-1000.0
Concentration of total P in suspended solid load from impervious areas
real
mg/kg
0.0
0.0-1000.0
Concentration of NO3-N in suspended solid load from impervious areas
real
mg/kg
0.0
0.0-50.0
Moisture condition II curve number for impervious areas
real
none
0.0
30.0-100.0
Fraction of the maximum leaf area index corresponding to the 2nd point on the leaf area development curve
Fraction of total impervious area in urban land type
Urban areas differ from rural areas in the fraction of total area that is impervious. Construction of buildings, parking lots and paved roads increases the impervious cover in a watershed and reduces infiltration. With development, the spatial flow pattern of water is altered and the hydraulic efficiency of flow is increased through artificial channels, curbing, and storm drainage and collection systems.
This fraction includes directly and indirectly connected impervious areas.
Name of the urban land type
The names in the urban database are also used by QSWAT+ to link the grid codes in land use/land cover maps to SWAT+ urban land types.
The name of the urban land type is a primary key referenced by in . All names in the urban.urb database must be unique.
Name | Description |
---|
urhd | Residential - high density |
urmd | Residential - medium density |
urml | Residential - med/low density |
urld | Residential - low density |
ucom | Commercial |
uidu | Industrial |
utrn | Transportation |
uins | Institutional |
urbn | Generic |
Fraction of directly connected impervious area in urban land type
Impervious areas can be differentiated into two groups, the area that is hydraulically connected to the drainage system and the area that is not directly connected. As an example, assume there is a house surrounded by a yard where runoff from the roof flows into the yard where it infiltrates into the soil. The rooftop is impervious, but it is not hydraulically connected to the drainage system. In contrast, a parking lot whose runoff enters a storm water drain is hydraulically connected.
When modeling urban areas, the connectedness of the drainage system must be quantified. The best methods for determining the fraction total and directly connected impervious areas is to conduct a field survey or analyze aerial photographs.
Wash-off coefficient for removal of constituents from impervious surfaces
Wash off is the process of erosion or solution of constituents from an impervious surface during a runoff event. The original default value for urb_wash was calculated as 0.18 mm-1 by assuming that 13 mm of total runoff in one hour would wash off 90% of the initial surface load (Huber and Heaney, 1982). Using sediment transport theory, Sonnen (1980) estimated values for the wash-off coefficient ranging from 0.002-0.26 mm-1. Huber and Dickinson (1988) noted that values between 0.039 and 0.390 mm-1 for the wash-off coefficient give sediment concentrations in the range of most observed values. This variable is used to calibrate the model to observed data.
Huber and Dickinson (1988)
Huber and Heaney (1982)
Sonnen (1980)
Maximum amount of solids allowed to build up on impervious surfaces
Time for amount of solids on impervious areas to build up to 1/2 of maximum level
Number of days for amount of solids on impervious areas to build up from 0 kg/curb km to half the maximum amount of solids allowed (i.e. 0.5*dirt_max).
Curb length density
Curb length may be measured directly by scaling the total length of streets off of maps and multiplying by two. To calculate the density, the curb length is divided by the area represented by the map.
Concentration of total N in suspended solid load from impervious areas
Concentration of total P in suspended solid load from impervious areas
Moisture condition II curve number for impermeable areas
Mixing efficiency of the tillage operation
The mixing efficiency specifies the fraction of materials (residue, nutrients, and pesticides) on the soil surface that are mixed uniformly throughout the soil depth specified by mix_dp. The remaining fraction of residue and nutrients is left in the original location (soil surface or layer).
Name of tillage record
The name of the tillage record is a primary key referenced when tillage operations are scheduled in management.sch or lum.dtl. All names in the tillage.til database must be unique.
Name | Description |
---|---|
fallplow
Generic Fall Plowing Operation
sprgplow
Generic Spring Plowing Operation
constill
Generic Conservation Tillage
zerotill
Generic No-Till Mixing
duckftc
Duckfoot Cultivator
fldcult
Field Cultivator
furowout
Furrow-out Cultivator
marker
Marker (Cultivator)
rollcult
Rolling Cultivator
rowcult
Row Cultivator
discovat
Discovator
leveler
Leveler
harrow
Harrow (Tines)
culmulch
Culti-Mulch Roller
culpkpul
Culti-Packer Pulverizer
landlevl
Land Plane-Leveler
landall
Landall, Do-All
lasrplan
Laser Planer
levpldis
Levee Plow Disc
float
Float
fldcdscr
Field conditioner (Scratcher)
listrmid
Lister (Middle-Buster)
rollgrov
Roller Groover
rolpkrat
Roller Packer Attachment
rolpkrft
Roller Packer Flat Roller
sandfigt
Sand-Fighter
seedroll
Seedbed Roller
crustbst
Crust Buster
rollhrrw
Roller Harrow
triplek
Triple K
finharrw
Finishing Harrow
flexharw
Flex-tine Harrow CL
spiketth
Powered Spike Tooth Harrow
spiktoth
Spike Tooth Harrow
sprgtoth
Spring Tooth Harrow
soilfins
Soil Finisher
rothoe
Rotary Hoe
roterra
Roterra
rototill
Roto-Tiller
rotbeddr
Rotovator-Bedder
rowbuck
Rowbuck
ripper
Ripper
midbst1r
Middle Buster
rodweedr
Rod Weeder
rubwhwpl
Rubber-Wheel Weed Puller
multiwdr
Multi-Weeder
mldboard
Moldboard Plow
chisplow
Chisel Plow
cchplow
Coulter-Chisel
diskplow
Disk Plow
stubmlch
Stubble-Mulch Plow
subchplw
Subsoil Chisel Plow
rowcond
Row Conditioner
hipper
Hipper
riceroll
Rice Roller
paraplow
Paraplow
sbedhipr
Subsoiler-Bedder Hip-Rip
riprsubs
Deep Ripper-Subsoiler
vripper
V-Ripper
bedrollr
Bed Roller
bedderd
Bedder (Disk)
beddhipr
Bedder Disk-Hipper
beddkrow
Bedder Disk-Row
bedders
Bedder Shaper
dskbrmkr
Disk Border Maker
dkchmtil
Disk Chisel (Mulch Tiller)
offsethv
Offset Disk - Heavy Duty
offsetlt
Offset Disk - Light Duty
one-wayt
One-Way (Disk Tiller)
tandempl
Tandem Disk Plow
tandemrg
Tandem Disk Reg
singldis
Single Disk
pwrmulch
Power Mulcher
blade10
Blade 10 ft
furwdike
Furrow Diker
beetcult
Beet Cultivator
cltiweed
Cultiweeder
packer
Packer
The tillage database summarizes parameters used by the model to simulate the effects of different types of tillage equipment.
Name of the fertilizer or manure record
Field | Description | Type | Unit | Default | Range |
---|---|---|---|---|---|
The name of the fertilizer or manure record is a primary key referenced when fertilizer or manure applications are scheduled in or . All names in the fertilizer.frt database must be unique.
Name of the tillage record
string
n/a
n/a
n/a
Mixing efficiency of the tillage operation
real
fraction
0.0
0.0-1.0
Depth of mixing caused by the tillage operation
real
mm
0.0
0.0-750.0
Random roughness
real
mm
0.0
0.0-200.0
Ridge height
real
mm
0.0
Ridge spacing
real
mm
0.0
Fraction of fertilizer that is mineral N (NO3+NH3)
Fraction of fertilizer that is mineral P
Fraction of mineral N content of fertilizer that is NH3
The fertilizer database summarizes parameters used by the model to simulate different types of fertilizer and manure.
Field | Description | Type | Unit | Default | Range |
---|---|---|---|---|---|
Name of the fertilizer record
string
n/a
n/a
n/a
Fraction of fertilizer that is mineral N (NO3+NH3)
real
fraction
1.0
0.0-1.0
Fraction of fertilizer that is mineral P
real
fraction
0.0
0.0-1.0
Fraction of fertilizer that is organic N
real
fraction
0.0
0.0-1.0
Fraction of fertilizer that is organic P
real
fraction
0.0
0.0-1.0
Fraction of mineral N content of fertilizer that is NH3
real
fraction
0.0
0.0-1.0
The pesticide database summarizes parameters used by the model to simulate the fate and transport of different types of pesticides.
Field | Description | Type | Unit | Default | Range |
---|---|---|---|---|---|
Name of the pesticide record
string
n/a
n/a
n/a
Soil adsorption coefficient normalized for soil organic carbon content
real
(mg/kg)/(mg/L)
0.0
1.0-999999999.0
Fraction of pesticide on foliage that is washed off by rainfall event
real
fraction
0.0
0.0-1.0
Half-life of the pesticide on the foliage
real
days
0.0
0.0-10000.0
Half-life of the pesticide in the soil
real
days
0.0
0.0-100000.0
Solubility of the pesticide in water
real
mg/L (ppm)
0.0
Aquatic pesticide reaction coefficient
real
1/day
0.0
Aquatic volatilization coefficient
real
m/day
0.0
Molecular weight to calculate mixing velocity
real
g/mol
0.0
Aquatic resuspension velocity for pesticide sorbed to sediment
real
m/day
0.0
Aquatic settling velocity for pesticide sorbed to sediment
real
m/day
0.0
Depth of the active benthic layer
real
m/day
0.0
Burial velocity in the benthic sediment
real
m/day
0.0
Reaction coefficient in the benthic sediment
real
1/day
0.0
Fraction of pesticide on foliage that is washed off by rainfall event
The wash-off fraction is a function of the nature of the leaf surface, plant morphology, pesticide solubility, polarity of the pesticide molecule, formulation of the commercial product and timing and volume of the rainfall event.
Soil adsorption coefficient normalized for soil organic carbon content
Pesticides in the soil environment can be transported in solution or attached to sediment. The partitioning of a pesticide between the solution and soil phases is defined by the soil adsorption coefficient for the pesticide. The soil adsorption coefficient is the ratio of the pesticide concentration in the soil or solid phase to the pesticide concentration in the solution or liquid phase:
where is the soil adsorption coefficient ((mg/kg)/(mg/L) or m3/ton), is the concentration of the pesticide sorbed to the solid phase (mg chemical/kg solid material or g/ton), and is the concentration of the pesticide in solution (mg chemical/L solution or g/ton). The definition of the soil adsorption coefficient in this equation assumes that the pesticide sorption process is linear with concentration and instantaneously reversible. Because the partitioning of pesticide is dependent upon the amount of organic material in the soil, the soil adsorption coefficient input to the model is normalized for soil organic carbon content. The relationship between the soil adsorption coefficient and the soil adsorption coefficient normalized for soil organic carbon content is:
where is the soil adsorption coefficient ((mg/kg)/(mg/L)), is the soil adsorption coefficient normalized for soil organic carbon content ((mg/kg)/(mg/L) or m3/ton), and is the percent organic carbon present in the soil.
Half-life of the pesticide on the foliage
The half-life for a pesticide defines the number of days required for a given pesticide concentration to be reduced by one-half. The half-life entered for a pesticide is a lumped parameter that includes the net effect of volatilization, photolysis, hydrolysis, biological degradation and chemical reactions.
For most pesticides, the foliar half-life is much less than the soil half-life due to enhanced volatilization and photodecomposition. If the foliar half-life is available for the pesticide this value should be used. If the foliar half-life is not available, the foliar half-life can be estimated using the following rules:
Foliar half-life is assumed to be less than the soil half-life by a factor of 0.5 to 0.25, depending on vapor pressure and sensitivity to photodegradation.
Foliar half-life is adjusted downward for pesticides with vapor pressures less than 10-5 mm Hg.
The maximum foliar half-life assigned is 30 days.
Name of the pesticide record
The name of the urban land type is a primary key referenced when pesticide applications are scheduled in management.sch or lum.dtl. All names in the pesticide.pes database must be unique.
Name | Description |
---|---|
245-tp
Silvex
2plus2
Mecoprop Amine
aatrex
Atrazine
abate
Abate (Temephos)
acaraben
Chlorobenzilate
accelera
Endothall Salt
acclaim
Fenoxaprop-Ethyl
alanap
Naptalam Sodium Salt
alar
Daminozide
aldrin
Aldrin
aliette
Fosetyl-Aluminum
ally
Metsulfuron-Methyl
amiben
Chloramben Salts
amid-thi
NAA Amide
amitrolt
Amitrole
ammo
Cypermethrin
antor
Diethatyl-Ethyl
a-rest
Ancymidol
arsenal
Imazapyr Acid
arsonate
MSMA
asana
Esfenvalerate
assert_m
Imazamethabenz-m
assert_p
Imazamethabenz-p
assure
Quizalofop-Ethyl
asulox
Asulam Sodium Salt
avenge
Difenzoquat
azodrin
Monocrotophos
balan
Benefin
banol
Propamocarb
banvel
Dicamba
basagran
Bentazon
basta
Glufosinate Ammonia
bayleton
Triadimefon
baytex
Fenthion
baythroi
Cyfluthrin
benlate
Benomyl
benzex
BHC
betamix
Phenmedipham
betanex
Desmedipham
bidrin
Dicrotophos
bladex
Cyanazine
bolero
Thiobencarb
bolstar
Sulprofos
borderma
MCPA Ester
botran
DCNA (Dicloran)
bravo
Chlorothalonil
buctril
Bromoxynil Octan. Ester
butyrace
2,4-DB Ester
caparol
Prometryn
carbamat
Ferbam
carsoron
Dichlobenil
carzol
Formetanate Hydrochlor
cerone
Ethephon
chem-hoe
Propham (IPC)
chlordan
Chlordane
chopper
Imazapyr Amine
classic
Chlorimuron-ethyl
cobra
Lactofen
comite
Propargite
command
Clomazone
cotoran
Fluometuron
counter
Terbufos
crossbow
Triclopyr Amine
curacron
Profenofos
cygon
Dimethoate
cyprex
Dodine Acetate
cythion
Malathion
dacamine
2,4-D Acid
dacthal
DCPA
dalapon
Dalapon Sodium Salt
dasanit
Fensulfothion
ddt
DDT
dedweed
MCPA Amine
def
Tribufos
dessican
Arsenic Acid
devrinol
Napropamide
di-systo
Disulfoton
dibrom
Naled
dieldrin
Dieldrin
dimilin
Diflubenzuron
dinitro
Dinoseb Phenol
diquat
Diquat Dibromide
dithane
Mancozeb
dowpon
Dalapon
dropp
Thidiazuron
dsma
Methanearsonic Acid Na
du-ter
Triphenyltin Hydroxide
dual
Metolachlor
dyfonate
Fonofos
dylox
Trichlorfon
dymid
Diphenamid
dyrene
Anilazine
elgetol
DNOC Sodium Salt
epn
EPN
eradican
EPTC
ethanox
Ethion
evik
Ametryn
evital
Norflurazon
far-go
Triallate
fenatrol
Fenac
fenitox
Fenitrothion
fruitone
3-CPA Sodium Salt
fundal
Chlordimeform Hydrocl.
funginex
Triforine
furadan
Carbofuran
fusilade
Fluazifop-P-Butyl
glean
Chlorsulfuron
goal
Oxyfluorfen
guthion
Azinphos-Methyl
harmony
Thifensulfuron-Methyl
harvade
Dimethipin
hoelon
Diclofop-Methyl
hyvar
Bromacil
imidan
Phosmet
isotox
Lindane
karate
Lambda-Cyhalothrin
karathan
Dinocap
karmex
Diuron
kelthane
Dicofol
kerb
Pronamide
krenite
Fosamine Ammon Salt
lannate
Methomyl
larvadex
Cyromazine
larvin
Thiodicarb
lasso
Alachlor
limit
Amidochlor
lontrel
Clopyralid
lorox
Linuron
lorsban
Clorpyrifos
manzate
Maneb
marlate
Methoxychlor
matacil
Aminocarb
mavrik
Fluvalinate
metasyst
Oxydemeton-Methyl
milogard
Propazine
miral
Isazofos
mitac
Amitraz
modown
Bifenox
monitor
Methamidophos
morestan
Oxythioquinox
nemacur
Fenamiphos
nemacurs_ne
Fenamiphos Sulfone
nemacurs_xid
Fenamiphos Sulfoxide
norton
Ethofumesate
octave
Prochloraz
oftanol
Isofenphos
orthene
Acephate
orthocid
Captan
oust
Sulfometuron-Methyl
pay-off
Flucythrinate
penncap-
Methyl Parathion
phenatox
Toxaphene
phosdrin
Mevinphos
phoskil
Parathion (Ethyl)
pipron
Piperalin
pix
Mepiquat Chlor. Salt
plantvax
Oxycarboxin
poast
Sethoxydim
polyram
Metiram
pounce
Permethrin
pramitol
Prometon
prefar
Bensulide
prelude
Paraquat
prime
Flumetralin
princep
Simazine
probe
Methazole
prowl
Pendimethalin
pursuit
AC 263,499
pydrin
Fenvalerate
pyramin
Pyrazon
ramrod
Propaclor
reflex
Fomesafen Salt
rescue
2,4-DB Sodium Amine
ridomil
Metalaxyl
ro-neet
Cycloate
ronstar
Oxadiazon
roundup
Glyphosate Amine
rovral
Iprodione
royalslo
Maleic Hydrazide
rubigan
Fenarimol
sancap
Dipropetryn
savey
Hexythiazox
scepter
Imazaquin Ammonium
sencor
Metribuzin
sevin
Carbaryl
sinbar
Terbacil
slug-get
Methiocarb
sonalan
Ethalfluralin
spectrac
Diazinon
spike
Tebuthiuron
sproutni
Chlorpropham
stam
Propanil
supracid
Methidathion
surflan
Oryzalin
1sutan
Butylate
swat
Phosphamidon
tackle
Acifluorfen
talstar
Bifenthrin
tandem
Tridiphane
tanone
Phenthoate
tattoo
Bendiocarb
tbz
Thiabendazole
temik
Aldicarb
temiksul
Aldicarb Sulfone
temiksulde
Aldicarb Sulfoxide
tenoran
Chloroxuron
terbutre
Terbutryn
terrachl
PCNB
terraneb
Chloroneb
terrazol
Etridiazole
thimet
Phorate
thiodan
Endosulfan
thiram
Thiram
thistrol
MCPB Sodium Salt
tillam
Pebulate
tilt
Propiconazole
tolban
Profluralin
topsin
Thiophanate-Methyl
tordon
Picloram
tralomet
Tralomethrin
treflan
Trifluralin
tre-hold
NAA Ethyl Ester
tupersan
Siduron
turflon
Triclopyr Ester
velpar
Hexazinone
vendex
Fenbutatin Oxide
vernam
Vernolate
volckoil
Petroleum oil
vydate
Oxamyl
weedar
2,4-D Amine
weed-b-g
2,4,5-T Amine
wedone
Dichlorprop Ester
zolone
Phosalone
Half-life of the pesticide in the soil
The half-life for a pesticide defines the number of days required for a given pesticide concentration to be reduced by one-half. The soil half-life entered for a pesticide is a lumped parameter that includes the net effect of volatilization, photolysis, hydrolysis, biological degradation, and chemical reactions.
Molecular weight to calculate mixing velocity
Solubility of the pesticide in water
The water solubility value defines the highest concentration of pesticide that can be reached in the runoff and soil pore water. While this is an important characteristic, researchers have found that the soil adsorption coefficient, , tends to limit the amount of pesticide entering solution so that the maximum possible concentration of pesticide in solution is seldom reached. Reported solubility values are determined under laboratory conditions at a constant temperature, typically between 20°C and 30°C.
Aquatic settling velocity for pesticide sorbed to sediment
Depth of the active benthic layer
Burial velocity in the benthic sediment
Reaction coefficient in the benthic sediment
Name of the septic record
The name of the septic record is a primary key referenced when septic systems are simulated in landuse.lum or lum.dtl. All names in the septic.sep database must be unique.
Name | Description |
---|---|
GCON
Generic type conventional system
GADV
Generic type advanced system
COND
Septic tank with conventional drainfield
SAS1
Septic tank with sand absorption system 1
SAS2
Septic tank with sand absorption system 2
SAS3
Septic tank with in-tank N removal and sand absorption system
SAS4
Septic tank with effluent N removal recycle
SAS5
Septic tank with corrugated plastic trickling filter
SAS6
Septic tank with open-cell form trickling filter
SPF1
Single pass sand filter 1
SPF2
Single pass sand filter 2
SPF3
Single pass sand filter 3
SPF4
Single pass sand filter 4
RCF1
At grade recirculating sand filter
RCF2
Maryland style recirculating sand filter
RCF3
Recirculating sand filter
CWT1
Septic tank w/ constructed wetland and surface water discharge
CWT2
Municipal wastewater w/ constructed wetland and surface water discharge 1
CWT3
Municipal wastewater w/ constructed wetland and surface water discharge 2
CWT4
Municipal wastewater w/ constructed wetland
CWT5
Municipal wastewater w/ lagoon and constructed wetland
BFL1
Waterloo biofilter (plastic media) 1
BFL2
Waterloo biofilter (plastic media) 2
BFL3
Peat biofilter
TXF1
Recirculating textile filter
TXF2
Foam or textile filter effluent
GFL1
Septic, recirculating gravel filter, UV disinfection
USPT
Untreated Effluent - Texas A&M reference
The septic systems database summarizes parameters used by the model to simulate different types of Onsite Wastewater Systems.
Information of water quality or effluent characteristics required to simulate different types of Onsite Wastewater Systems (OWSs) is stored in the septic water quality database. The information contained in the septic water quality database includes the septic tank effluent flow rate for per capita and the effluent characteristics of various septic systems. The database file distributed with SWAT+ includes water quality data for most conventional, advanced, and failing septic systems. It was developed based on the field data summarized by Siegrist et al. (2005), McCray et al. (2005), and OWTS 201 (2005).
Siegrist et al. (2005)
McCray et al. (2005)
OWTS 201 (2005)
Field | Description | Type | Unit | Default | Range |
---|---|---|---|---|---|
Name of the septic record
string
n/a
n/a
n/a
Flow rate of the septic tank effluent
real
m^3/d
0.0
0.0-1.0
7-day Biological Oxygen Demand of the septic tank effluent
real
mg/l
0.0
0.0-300.0
Total suspended solids in the septic tank effluent
real
mg/l
0.0
0.0-300.0
Ammonium nitrogen in the septic tank effluent
real
mg/l
0.0
Nitrate nitrogen in the septic tank effluent
real
mg/l
0.0
Nitrite nitrogen in the septic tank effluent
real
mg/l
0.0
Organic nitrogen in the septic tank effluent
real
mg/l
0.0
Mineral phosphorus in the septic tank effluent
real
mg/l
0.0
Organic phosphorus in the septic tank effluent
real
mg/l
0.0
Number of fecal coliform in the septic tank effluent
real
mg/l
0.0
Normal fraction of nitrogen in plant biomass at emergence
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: frac_n_em, frac_n_50, frac_n_mat, frac_p_em, frac_p_50, and frac_p_mat. Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.