VFS: Vegetated filter strip and erosion model
In VFS: Vegetated Filter Strip and Erosion Model

Description Usage Arguments Details Value Author(s) References See Also Examples

Simulated erosion and runoff given climate and soil texture, with or without a vegetated filter strip in place.

VFS(nyears = 1000, thissoil, thisbuffer, rain, temperature, 
  Duration = 2, FieldArea = 4000, VFSwidth = 10.7, VFSslope = 0.02,
  FieldSlope,  z = 1000, a = 1, b = 1.5,
  carrysoilwater = TRUE, runoffcalc = TRUE)

`nyears`	Number of years to simulate.
`thissoil`	Soil properties for the site, as from soildat.
`thisbuffer`	Vegetation properties for the buffer strip, as from bufferdat.
`rain`	Daily rainfall (mm).
`temperature`	Daily mean temperature (C).
`Duration`	Rainfall event length. Default is 2 hours.
`FieldArea`	Field area (m^2).
`VFSwidth`	Filter strip width (m).
`VFSslope`	Filter strip slope (m/m).
`FieldSlope`	Optional field slope (m/m). If missing, VFSslope will be used.
`z`	Rooting zone depth (mm). Default is 1000 mm.
`a`	Empirical parameter that relates concentration and flow in the concentration-discharge relationship, C = aQ^b.
`b`	Empirical parameter that relates concentration and flow in the concentration-discharge relationship, C = aQ^b. May be a single value or a vector of values.
`carrysoilwater`	Boolean describing whether to store soil water; if FALSE, soil is always at field capacity. This option allows the effect of soil water storage to be quantified.
`runoffcalc`	Boolean describing whether to use intensity and saturation exceedances; if FALSE, all rainfall becomes runoff. This option allows the effect of runoff calculation to be quantified.

The concentration-discharge (C-Q) model of erosion is intended to produce relative erosion values, rather than absolute values, but will produce absolute values if a and b are known. The MUSLE field erosion model is run alongside the C-Q model. The K factor is estimated from soil texture data using MUSLE.K, and the LS factor from field properties using MUSLE.LS. Blaney-Criddle coefficients for evapotranspiration calculations from a cornfield are hard-coded; a future update will allow for varying the type of field.

Returns an object of class VFS, comprising:

daily

Daily output of all public variables that change as a function of time. The data frame has columns:

rain: precipitation (mm).
temperature: mean temperature (C).
S: soil water storage, (mm).
kt: Blaney-Criddle temperature coefficient.
ET: evapotranspiration (mm).
intensity: rainfall intensity (mm).
runoff: runoff (mm).
Q: discharge (ft^3/s).
fd: flow depth through VFS (ft).
R: hydraulic radius of filter strip (ft).
Vm: Manning's velocity (ft/s).
Re: Reynold's number.
Va: actual shear stress (ft/s).
Nfc: Fall number for coarse particles.
Nfm: Fall number for medium particles.
Nff: Fall number for fine particles.
fdc: Trapping efficiency for coarse particles.
fdm: Trapping efficiency for medium particles.
fdf: Trapping efficiency for fine particles.
Ft: Total trapping efficiency of filter strip.
peakflow: peak flow (m^3/s).

field

Data on the field being modeled:

clay: soil clay content (%.)
area: field area (m^2).

`Conc`	Sediment concentration (in mass/volume) as calculated by the relationship C = aQ^b; specific units depend on units conversions included in the value of a.
`MassIn`	Sediment load (mass) from the C-Q model, as calculated by multiplying concentration and runoff volume. If concentration is assumed to be in g/L, then the load is calculated in g.
`MassOut`	Sediment mass from the C-Q model that leaves the vegetated filter strip at the end of a runoff event (i.e., the mass that is not removed).
`MassRemoved`	Sediment mass from the C-Q model that remains in the vegetated filter strip at the end of a runoff event.
`AnnualMassIn`	Sum of the sediment loads from the C-Q model entering the vegetated filter strip over the course of one year.
`AnnualMassOut`	Sum of the sediment loads from the C-Q model leaving the vegetated filter strip over the course of one year.
`AnnualRemovalEfficiency`	The removal effficiency from the C-Q model of the vegetated filter strip at an annual time scale (%).
`MassInMUSLE`	Sediment mass from the MUSLE model leaving the crop field .
`MassOutMUSLE`	Sediment mass from the MUSLE model that leaves the vegetated filter strip at the end of a runoff event (t/day).
`MassRemovedMUSLE`	Sediment mass that remains in the vegetated filter strip at the end of a runoff event (t/day).
`AnnualMassInMUSLE`	Sum of the sediment loads entering the vegetated filter strip over the course of one year (t).
`AnnualMassOutMUSLE`	Sum of the sediment loads leaving the vegetated filter strip over the course of one year (t).
`AnnualRemovalEfficiencyMUSLE`	The removal effficiency of the vegetated filter strip at an annual time scale (%).
`Ftannual`	Filter strip removal efficiency.
`Ftannualavg`	The average of all per-event trapping efficiencies over the course of one year.

Heather Gall, Sarah Goslee, and Tamie Veith

Gall, H. E., Schultz, D., Veith, T. L, Goslee, S. C., Mejia, A., Harman, C. J., Raj, C. and Patterson, P. H. (2018) The effects of disproportional load contributions on quantifying vegetated filter strip sediment trapping efficiencies. Stoch Environ Res Risk Assess 32(8), 2369–2380. doi: 10.1007/s00477-017-1505-x

Haan C. T., Barfield B. J. and Hayes J. C. (1994) Design hydrology and sedimentology for small catchments. Acad Press, San Diego.

Williams, J. R. (1975) Sediment-yield prediction with universal equation using runoff energy factor. Pp. 244–251 in: Present and prospective technology for predicting sediment yield and sources. ARS.S-40, US Gov. Print. Office, Washington, DC. 244-252.

Wischmeier, W. H. and Smith, D. D. (1978) Predicting rainfall erosion losses-a guide to conservation planning. U.S. Department of Agriculture, Agriculture Handbook No. 537.

print.VFS, summary.VFS, wth.param, soildat, bufferdat,

# state college GHCN data
#
# weather <- read.dly(system.file("extdata", "USC00368449.dly", package = "VFS"))
data("weather") # same object

weather.param <- wth.param(weather, method="markov")

rain.compare <- rainfall(365*2, weather.param)
temp.compare <- temperature(365*2, weather.param)

data(soildat)
data(bufferdat)

# bluegrass buffer, clay loam soil
# short simulation to cut down on time required
vfs.CL <- VFS(nyears = 2, thissoil = subset(soildat, Soil == "CL"), 
rain=rain.compare, temperature=temp.compare, 
thisbuffer = subset(bufferdat, Species == "bluegrass"), Duration = 2, 
FieldArea = 4000, VFSwidth = 10.7, VFSslope = 0.02, 
z = 1000, b = 1.5)

print(vfs.CL)
summary(vfs.CL)

aple.CL <- VFSAPLE(vfs.CL, soilP = 120, OM = 2)

print(aple.CL)
summary(aple.CL)