Package 'VFS'

Vegetated Filter Strip and Erosion Model

Description

Empirical models for runoff, erosion, and phosphorus loss across a vegetated filter strip, given slope, soils, climate, and vegetation (Gall et al., 2018) <doi:10.1007/s00477-017-1505-x>. It also includes functions for deriving climate parameters from measured daily weather data, and for simulating rainfall. Models implemented include MUSLE (Williams, 1975) and APLE (Vadas et al., 2009 <doi:10.2134/jeq2008.0337>).

Details

The DESCRIPTION file:

Package:	VFS
Title:	Vegetated Filter Strip and Erosion Model
Version:	1.0.4
Date:	2018-10-01
Authors@R:	c(person("Sarah", "Goslee", role = c("aut", "cre"), email = "[email protected]"), person("Heather", "Gall", role = "aut"), person("Tamie", "Veith", role = "aut"))
Depends:	R (>= 3.4.0)
Imports:	stats, graphics, e1071, nleqslv (>= 3.3.0)
Suggests:	knitr, rmarkdown, testthat
VignetteBuilder:	knitr
Description:	Empirical models for runoff, erosion, and phosphorus loss across a vegetated filter strip, given slope, soils, climate, and vegetation (Gall et al., 2018) <doi:10.1007/s00477-017-1505-x>. It also includes functions for deriving climate parameters from measured daily weather data, and for simulating rainfall. Models implemented include MUSLE (Williams, 1975) and APLE (Vadas et al., 2009 <doi:10.2134/jeq2008.0337>).
License:	GPL-3
BugReports:	https://github.com/sgoslee/VFS/issues
LazyData:	true
Repository:	https://sarahgoslee-usda.r-universe.dev
RemoteUrl:	https://github.com/sarahgoslee-usda/vfs
RemoteRef:	HEAD
RemoteSha:	f2592289308916a3a0d781fe8e4bb63a8e489602
Author:	Sarah Goslee [aut, cre], Heather Gall [aut], Tamie Veith [aut]
Maintainer:	Sarah Goslee <[email protected]>

Index of help topics:

APLE                    Agricultural Phosphorus Loss Estimator
MUSLE                   Modified Universal Soil Loss Equation
MUSLE.K                 Estimate soil erodibility factor K.
MUSLE.LS                Estimate landscape factor LS
USC00368449.dly         GHCN Data for State College, PA, 1980-2009
VFS                     Vegetated filter strip and erosion model
VFS-package             Vegetated Filter Strip and Erosion Model
VFSAPLE                 Link the VFS and APLE models.
bufferdat               Parameters for vegetated buffers
peak                    Rational method to calculate peak discharge
print.APLE              Printing the result of APLE
print.VFS               Printing the result of VFS
rainfall                Generate simulated daily rainfall
read.dly                Read GHCN DLY daily weather file into a data
                        frame
soildat                 Soil texture class properties
summary.APLE            Summarize the result of APLE
summary.VFS             Summarize the result of VFS
temperature             Generate simulated mean temperature
weather                 Ten years of daily weather data
wth.param               Calculate weather parameters from daily data
                        for use in climate simulations

This package implements runoff, erosion, filter strip, and phosphorus loss models in R.

Author(s)

Sarah Goslee [aut, cre], Heather Gall [aut], Tamie Veith [aut]

Maintainer: Sarah Goslee <[email protected]>

References

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

Examples

# 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*3, weather.param)
temp.compare <- temperature(365*3, weather.param)

data(soildat)
data(bufferdat)

# bluegrass buffer, clay loam soil
vfs.CL <- VFS(nyears = 3, 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)

---

Agricultural Phosphorus Loss Estimator

Description

Agricultural loss of phosphorus based on soil phosphorus level, additions of fertilizer and manure, and erosion.

Usage

APLE(soilP, clay, OM, precip, runoff, erosion, manureP = 25,
    manureSolids = 25, manureWEP = 50, manureIn = 40,
	fertP = 10, fertIn = 40)

Arguments

soilP	soil test Mehlich 3 phosphorus (mg/kg).
clay	soil clay (%).
OM	soil organic matter (%).
precip	annual precipitation (in).
runoff	annual runoff (in)
erosion	annual erosion (ton/ac).
manureP	manure P applied (kg/ha).
manureSolids	manure solids (%).
manureWEP	manure water-extractable phosphorus/TP (%).
manureIn	manure incorporated (%).
fertP	fertilizer phosphorus applied (kg/ha).
fertIn	fertilizer incorporated (%).

Details

This function implements the basic version of the spreadsheet-based Agricultural Phosphorus Loss Estimator model (APLE) in R, and is vectorized. This model calculates annual phosphorus loss by compartment (due to erosion, dissolved soil phosphorus, dissolved manure, dissolved fertilizer) based rainfall, soil properties, and management. The units match those of the original spreadsheet.

Value

lossErosion	soil erosion phosphorus loss (kg/ha).
lossDissolvedSoil	soil dissolved phosphorus loss (kg/ha).
lossDissolvedManure	manure dissolved phosphorus loss (kg/ha).
lossDissolvedFertilizer	fertilizer dissolved phosphorus loss (kg/ha).
lossTotal	total phosphorus loss (kg/ha).

Author(s)

Sarah Goslee

References

Vadas, P. A., Good, L. W., Moore, P. A., Jr. and Widman, N. (2009) Estimating phosphorus loss in runoff from manure and fertilizer for a phosphorus loss quantification tool. J Environ Qual 38, 1645–1653. doi:10.2134/jeq2008.0337

See Also

VFSAPLE

Examples

APLE(soilP = 127, clay = 17, OM = 6, precip = 35, runoff = 6,
erosion = 7, manureP = 25, manureSolids = 25, manureWEP = 50,
manureIn = 40, fertP = 10, fertIn = 40)

---

Parameters for vegetated buffers

Description

Contains parameters describing vegetated filter strips for use in VFS modeling.

Usage

data("bufferdat")

Format

A data frame with 2 observations on the following 3 variables.

Species

Type of buffer

bg

Average stem spacing (cm)

n

Manning's roughness coefficient (s m^(-1/3))

Details

Currently contains data for a cool-season and a warm-season grass buffer.

Source

Haan CT, Barfield BJ, Hayes JC (1994) Design hydrology and sedimentology for small catchments. Acad. Press, San Diego

Examples

# 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)

---

Modified Universal Soil Loss Equation

Description

Simulation of soil erosion on a daily timestep.

Usage

MUSLE(Q, qp, A, K, LS, C = 0.085, P = 0.40, a = 11.8, b = 0.56)

Arguments

Q	Runoff volume (m^3/d).
qp	Runoff peak discharge (m^3/s).
A	Field area (ha).
K	Soil erodibility factor.
LS	Landscape factor.
C	Crop management factor. Default is for a corn field.
P	Erosion control practice factor.
a	Location coefficient. Default value from Williams 1975.
b	Location coefficient. Default value from Williams 1975.

Details

Uses the Modified Universal Soil Loss Equation to estimate daily sediment yield. If K and LS are not known, they can be estimated from soil or field properties using MUSLE.K and MUSLE.LS.

If the location coefficients are known from measured sedimentation data, more accurate estimates can be made.

Value

Sediment yield (t/day).

Author(s)

Sarah Goslee

References

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.

See Also

MUSLE.K, MUSLE.LS, peak

Examples

# Approximate erodibility factor from soil texture.
Kf <- MUSLE.K(.3, .5, .2)

# Calculate landscape factor from field size and shape.
# 100-m field length with 2% slope
# note that MUSLE.LS takes feet
LS <- MUSLE.LS(100 * 3.28, .02)

# assume 0.4 ha cornfield with known rainfall intensity
peakd <- peak(intensity = 55, area = 0.4)

SedYield <- MUSLE(85, qp = peakd, A = .4, K = Kf, LS = LS)

---

Estimate soil erodibility factor K.

Description

Estimates MUSLE soil erodibility from a multiple regression model of soil texture.

Usage

MUSLE.K(fc, fm, ff)

Arguments

fc	Fraction of coarse material (sand) in the soil (0-1).
fm	Fraction of medium material (silt) in the soil (0-1).
ff	Fraction of fine material (clay) in the soil (0-1).

Details

If K is not available from other sources, it can be estimated from soil texture (Goslee, in review).

Value

Returns the soil erodibility factor K.

Author(s)

Sarah Goslee

References

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.

See Also

MUSLE

Examples

# Approximate erodibility factor from soil texture.
Kf <- MUSLE.K(.3, .5, .2)

# Calculate landscape factor from field size and shape.
# 100-m field length with 2% slope
# note that MUSLE.LS takes feet
LS <- MUSLE.LS(100 * 3.28, .02)

# assume 0.4 ha cornfield with known rainfall intensity
peakd <- peak(intensity = 55, area = 0.4)

SedYield <- MUSLE(85, qp = peakd, A = .4, K = Kf, LS = LS)

---

Estimate landscape factor LS

Description

Estimates MUSLE landscape factor for a homogeneous field from the field length and slope.

Usage

MUSLE.LS(FieldLength, FieldSlope)

Arguments

FieldLength	Field length (ft).
FieldSlope	Field slope (ft/ft).

Value

MUSLE landscape factor LS.

Author(s)

Sarah Goslee

References

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.

See Also

MUSLE

Examples

# Approximate erodibility factor from soil texture.
Kf <- MUSLE.K(.3, .5, .2)

# Calculate landscape factor from field size and shape.
# 100-m field length with 2% slope
# note that MUSLE.LS takes feet
LS <- MUSLE.LS(100 * 3.28, .02)

# assume 0.4 ha cornfield with known rainfall intensity
peakd <- peak(intensity = 55, area = 0.4)

SedYield <- MUSLE(85, qp = peakd, A = .4, K = Kf, LS = LS)

---

Rational method to calculate peak discharge

Description

Very simple estimate of peak discharge.

Usage

peak(intensity, area, c = 0.25)

Arguments

intensity	Precipitation intensity (mm/hr).
area	Field area (ha).
c	Runoff coefficient, related to slope, soil type, and land cover (0-1). Forest may be 0.05 - 0.25, while paved surfaces may be 0.95.

Value

Peak discharge (m^3/s).

Author(s)

Sarah Goslee

References

Hromadka, T. V, II and Whitley, R. J. (1994) The rational method for peak flow rate estimation. Water Res Bull 30(6), 1001–1009.

Examples

# peak discharge from a grassy meadow
    peakd.meadow <- peak(intensity = 55, area = 1, c = 0.1)

    # peak discharge from an urban area
    peakd.urban <- peak(intensity = 55, area = 1, c = 0.8)

---

Printing the result of APLE

Description

Print method for APLE objects.

Usage

## S3 method for class 'APLE'
print(x, ...)

Arguments

x	An APLE object produced by APLE or VFSAPLE.
...	Other arguments to print.

Details

Prints the annual mean for erosion, soil dissolved, manure dissolved, fertilizer dissolved, and total phosphorus losses.

Value

Returns the APLE object x invisibly.

Author(s)

Sarah Goslee

See Also

APLE, VFSAPLE, summary.APLE,

Examples

x <- APLE(soilP = 127, clay = 17, OM = 6, precip = 35, runoff = 6,
	erosion = 7, manureP = 25, manureSolids = 25, manureWEP = 50,
	manureIn = 40, fertP = 10, fertIn = 40)

print(x)
summary(x)

---

Printing the result of VFS

Description

Print method for VFS objects.

Usage

## S3 method for class 'VFS'
print(x, ...)

Arguments

x	A VFS x produced by VFS().
...	Other arguments to print.

Details

If the VFS object has a modeled filter strip, the mean annual loads in and out, as well as removal efficiencies are printed. Otherwise only the erosion values from the field are printed.

Value

It returns the VFS object invisibly.

Author(s)

Sarah Goslee

See Also

VFS, summary.VFS

Examples

# 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)

---

Generate simulated daily rainfall

Description

Generates simulated daily rainfall based on parameters derived from daily weather data.

Usage

rainfall(ndays, thiswth, months)

Arguments

ndays	Number of days to simulate.
thiswth	Output of wth.param The choice of method in wth.param() determines what coefficients are provided, and thus which rainfall simulation method is used.
months	If the rainfall simulation method uses monthly statistics (Markov), a vector of month numbers of length(ndays) may be provided. If it is missing, then January 1 is assumed to be the first day of a 365-day year.

Details

The rainfall simulation currently offers choice of two methods: the simple Poisson model of Rodriguez-Iturbe et al. (1999), and the Markov chain model of Nicks (1974). The latter rainfall calculation is used by the APEX farm model, among others, and is based on monthly statistics.

Value

A vector of daily rainfall totals.

Author(s)

Heather Gall and Sarah Goslee

References

Rodriguez-Iturbe, I., Porporato, A., Ridolfi, L., Isham, V. and Coxi, D. R. (1999) Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation. Proc Royal Soc A 455, 269–288.

Nicks, A. D. (1974) Stochastic generation of the occurrence, pattern and location of maximum amount of daily rainfall. Pp. 154–171 in: Proceedings Symposium on Statistical Hydrology. USDA Agricultural Research Service Miscellaneous Publication No. 1275, Washington, DC.

See Also

wth.param, temperature

Examples

# GHCN daily weather file for State College, PA
# subset of data (2000-2009) for station USC00368449
#
data("weather") # same object


# calculate parameters for the poisson model
# using 0.3 mm as the lower limit for wet days.
weather.param.p <- wth.param(weather, method = "poisson", llim = 0.3)

# simulate ten years of rainfall
rain10.p <- rainfall(365*10, weather.param.p)

# increase per-event rainfall by 5 mm
weather.param.p5 <- weather.param.p
weather.param.p5$params$depth <- weather.param.p5$params$depth + 5
rain10.p5 <- rainfall(365*10, weather.param.p5)

# calculate parameters for the Markov chain model
# using 0.3 mm as the lower limit for wet days.
weather.param.m <- wth.param(weather, method = "markov", llim = 0.3)

# rainfall() selects Markov model based on input parameter types
rain10.m <- rainfall(365*10, weather.param.m)

# simulate 10 years of temperature
temp10 <- temperature(365*10, weather.param.p)

---

Read GHCN DLY daily weather file into a data frame

Description

Imports daily data files from the Global Historical Climatology Network (GHCN), replaces nodata values with NA, and converts precipitation to mm and temperature to C.

Usage

read.dly(filename)

Arguments

filename

Filename or URL of a GHCN DLY file.

Details

All GHCN DLY files should have these five elements: PRCP (precipitation, originally tenths of a mm but mm in the function output); SNOW (snowfall, mm); SNWD (snow depth, mm), TMAX (maximum temperature, originally tenths of degree C but C in the function output), and TMIN (minimum temperature, originally tenths of degree C but C in the function output).

Depending on the station, there may be many other recorded variables. Each variable is accompanied by a series of quality flags, which are preserved from the original file.

Data are in a complex fixed-width format. Please see the GHCN readme for details.

Value

Returns a data frame with date as three columns, YEAR, MONTH, DAY, and each data value present in the original file along with its quality flags. Please see the GHCN readme for details.

Note that units for temperature and precipitation have been converted from the GHCN values.

These columns will always be present in the output:

YEAR

Year.

MONTH

Month number.

DAY

Day of month.

PRCP

Precipitation (mm).

TMAX

Maximum temperature (C).

TMIN

Minimum temperature (C).

Author(s)

Sarah Goslee

References

GHCN data comprises both current and historical weather station data world-wide.

See Also

wth.param, weather

Examples

# ten years of daily weather data, 2000-2009, for State College, PA
weather <- read.dly(system.file("extdata", "USC00368449.dly", package = "VFS"))

# could also use: 
# weather <- read.dly("ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/all/USC00368449.dly")
# weather <- subset(weather, YEAR >= 2000 & YEAR <= 2009)

# daily precipitation
summary(weather$PRCP.VALUE)

# monthly average maximum temperature
aggregate(TMAX.VALUE ~ MONTH, FUN = mean, data = weather)

# generate simulation values
weather.params <- wth.param(weather)

---

Soil texture class properties

Description

Basic hydrologic properties for twelve soil texture classes.

Usage

data("soildat")

Format

A data frame with 12 observations on the following 9 variables.

Soil

Texture class abbreviation.

SoilName

Texture class name.

Ksat

Saturated hydraulic conductivity (mm d^(-1)).

ThetaSAT

Water potential at saturation.

ThetaFC

Water potential at field capacity.

ThetaWP

Water potential at wilt point.

ff

Fraction of fine (clay) particles.

fm

Fraction of medium (silt) particles.

fc

Fraction of coarse (sand) particles.

Source

Clapp, RB, Hornberger, GM. 1978. Empirical equations for some soil hydraulic properties. Water Resour Res 14:601-604. DOI: 10.1029/WR014i004p00601.

Karkanis, PG. 1983. Determining field capacity and wilting point using soil saturation by capillary rise. Can Agr Eng 25:19-21.

Examples

# 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)

---

Summarize the result of APLE

Description

Summary method for APLE objects.

Usage

## S3 method for class 'APLE'
summary(object, ...)

Arguments

object	APLE object produced by APLE.
...	Other arguments to summary

Details

Calculates means for phosphorus loss.

Value

Summary of APLE object.

Author(s)

Sarah Goslee

See Also

APLE, VFSAPLE, print.APLE,

Examples

x <- APLE(soilP = 127, clay = 17, OM = 6, precip = 35, runoff = 6,
	erosion = 7, manureP = 25, manureSolids = 25, manureWEP = 50,
	manureIn = 40, fertP = 10, fertIn = 40)

print(x)
summary(x)

---

Summarize the result of VFS

Description

Summary method for VFS objects.

Usage

## S3 method for class 'VFS'
summary(object, ...)

Arguments

object	A VFS object produced by VFS().
...	Other arguments to summary

Details

Calculates means and standard deviations for annual removal efficiencies. If model type is erosion-only (no vegetated filter strip simulated), then only the relevant variables describing erosion from the field are returned.

Value

ALR	Annual load reduction.
ALRsd	SD of annual load reduction.
ALR1000	Load reduction across full timespan.
ALR1000sd	SD of load reduction across full timespan.
APEA	Annual per-event average reduction.
APEAsd	SD of annual per-event average reduction.
SedIn	Sediment entering the vegetated filter strip per year.
SedInsd	SD of sediment entering the vegetated filter strip per year.
SedLoss	Sediment lost per year.
SedLosssd	SD of sediment lost per year.
TLR	Total load reduction.
RunoffDays	Days per year with runoff.
RunoffDayssd	SD of days per year with runoff.
Days	Total number of days.

Author(s)

Sarah Goslee

See Also

VFS, print.VFS

Examples

# 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)

---

Generate simulated mean temperature

Description

Generates simulated daily temperature minimum and maximum based on parameters derived from daily weather data.

Usage

temperature(ndays, thiswth)

Arguments

ndays	number of days to simulate
thiswth	list output of wth.param.

Details

This is a very simple temperature simulation, using three parameters derived from daily weather data and the day of year to calculate a smooth annual temperature change derived from the first harmonic of a Fourier function.

Value

Returns a vector of daily mean temperature (X).

Author(s)

Heather Gall and Sarah Goslee

References

Grimenes, A. and Nissen, O. (2004) Mathematical modeling of the annual temperature wave based on monthly mean temperatures, and comparisons between local climate trends at seven Norwegian stations. Theor Appl Climatol 78, 229–246. doi:10.1007/s00704-004-0036-9

See Also

wth.param, rainfall

Examples

# A sample GHCN daily weather file for State College, PA, is included with this package.
# This file contains a subset of data (1980-2009) for station USC00368449
data("weather") 

# calculate parameters for the poisson model, using 0.3 mm as the lower limit for wet days.
weather.param.p <- wth.param(weather, method = "poisson", llim = 0.3)

# simulate 10 years of temperature
temp10 <- temperature(365*10, weather.param.p)

---

GHCN Data for State College, PA, 1980-2009

Description

A ten-year sample of Global Historical Climatology Network (GHCN) daily weather data for State College, PA (station USC00368449). This is in a complex fixed-width format. Please see the GHCN readme for details.

Usage

"USC00368449.dly"

Format

A complex fixed-width ASCII file.

Source

GHCN data comprises both current and historical weather station data world-wide.

See Also

read.dly

Examples

# A sample DLY file for State College, PA, is included with this package.
# This file contains a subset of data (1980-2009) for station USC00368449
#
weather <- read.dly(system.file("extdata", "USC00368449.dly", package = "VFS"))
# identical to data("weather")

---

Vegetated filter strip and erosion model

Description

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

Usage

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)

Arguments

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.

Details

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.

Value

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.

Author(s)

Heather Gall, Sarah Goslee, and Tamie Veith

References

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.

See Also

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

Examples

# 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)

---

Link the VFS and APLE models.

Description

Uses the erosion and runoff output of VFS as input to APLE.

Usage

VFSAPLE(x, soilP, OM, manureP = 25, manureSolids = 25, manureWEP = 50,
    manureIn = 40, fertP = 10, fertIn = 40)

Arguments

x	VFS object.
soilP	soil test Mehlich 3 phosphorus (mg/kg).
OM	soil organic matter (%).
manureP	manure P applied (kg/ha).
manureSolids	manure solids (%).
manureWEP	manure water-extractable phosphorus/TP (%).
manureIn	manure incorporated (%).
fertP	fertilizer phosphorus applied (kg/ha).
fertIn	fertilizer incorporated (%).

Details

The VFSAPLE function handles unit conversion and parameter estimation. Erosion, precipitation, runoff, and field parameters from VFS are passed to APLE. It also runs APLE for both pre- and post-filter strip erosion values, so the efficacy of the filter strip at phosphorus removal can be calculated.

Value

preVFS	APLE object for pre-filter strip erosion values.
postVFS	APLE object for post-filter strip erosion values.
pErosion	Efficacy of the filter strip at removing erosion phosphorus (%).
pTotal	Efficacy of the fiter strip at removing total phosphorus (%).

Author(s)

Sarah Goslee

See Also

APLE, VFS

Examples

# 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)

---

Ten years of daily weather data

Description

The VFS offers the capability of importing weather data from the GHCN, either from local files or the online repository, but this import is slow, so the result of the import is saved as an R object for those examples that need it.

The original GHCN file has many more columns.

Usage

data("weather")

Format

A data frame with 3653 observations on the following 8 variables.

YEAR

a numeric vector

MONTH

a numeric vector

DAY

a numeric vector

PRCP.VALUE

a numeric vector

SNOW.VALUE

a numeric vector

SNWD.VALUE

a numeric vector

TMAX.VALUE

a numeric vector

TMIN.VALUE

a numeric vector

Source

GHCN data comprises both current and historical weather station data world-wide.

See Also

wth.param, read.dly

Examples

# state college GHCN data
#
# created by:
# weather <- read.dly(system.file("extdata", "USC00368449.dly", package = "VFS"))
data("weather") # same object: 10 years of daily weather data

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

rain.compare <- rainfall(365*3, weather.param)
temp.compare <- temperature(365*3, weather.param)

---

Calculate weather parameters from daily data for use in climate simulations

Description

The climate generation functions for rainfall and temperature require parameters calculated from GHCN daily weather data, or from any data frame with columns containing year, month, day, precipitation, and minimum and maximum temperature. Partial years at the beginning or end of the dataset are removed. Leap days are also removed to standardize day-of-year calculation.

Usage

wth.param(dly, llim = 0, method = "poisson", year.col = "YEAR", 
	month.col = "MONTH", day.col = "DAY", prcp.col = "PRCP.VALUE", 
	tmin.col = "TMIN.VALUE", tmax.col = "TMAX.VALUE")

Arguments

dly	A data frame, such as the output of read.dly(), with days as rows and columns including YEAR, MONTH, DAY, PRCP.VALUE, TMIN.VALUE, TMAX.VALUE (for GHCN data), or with those columns having names specivied by arguments.
llim	The minimum daily rainfall for a wet day to be counted.
method	Choice of model for which to calculate parameters, either "poisson" or "markov".
year.col	Name of the column containing year number.
month.col	Name of the column containing month number.
day.col	Name of the column containing day number.
prcp.col	Name of the column containing daily precipitation.
tmin.col	Name of the column containing daily minimum temperature.
tmax.col	Name of the column containing daily maximum temperature.

Details

The rainfall simulation currently offers choice of two methods: the simple Poisson model of Rodriguez-Iturbe et al. (1999), and the Markov chain model of Nicks (1974). The latter rainfall calculation is used by the APEX farm model, among others, and is based on monthly statistics. NOTE: For reasons of time and space, the example contains only ten years of daily weather data. We suggest using thirty years for estimating parameter values.

Value

params	Parameters for simulating long-term point rainfall. For method = "poisson", a list of parameters containing: lambda: Mean rainfall inter-arrival frequency (d-1). depth: Mean rainfall depth (mm). A: Mean annual temperature (C). B: Temperature half-amplitude (C). C: Day of the year with minimum temperature (DOY). start: First full year of the weather data. end: Last full year of the weather data. For method = "markov", a data frame with one row per month and columns: tmin: Minimum temperature. tminsd: Maximum temperature. tmax: Minimum temperature standard deviation. tmaxsd: Maximum temperature standard deviation. prcp: Monthly precipitation. prcpmean: Mean size of a precipitation event. prcpmax: Maximum size of a precipitation event. prcpsd: Standard deviation of precipitation event sizes. prcpskew: Skew of precipitation event sizes. prcpwet: Number of wet days (greater than llim). prcpww: Probability of a wet day following a wet day. prcpdw: Probability of a wet day following a dry day.
temperature	Parameters for simulating long-term daily temperature.
llim	Minimum daily rainfall for a wet day.
start	First full year of weather data
end	Last full year of weather data

Author(s)

Sarah Goslee

References

Rodriguez-Iturbe, I., Porporato, A., Ridolfi, L., Isham, V. and Coxi, D. R. (1999) Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation. Proc Royal Soc A 455, 269–288.

Nicks, A. D. (1974) Stochastic generation of the occurrence, pattern and location of maximum amount of daily rainfall. Pp. 154–171 in: Proceedings Symposium on Statistical Hydrology. USDA Agricultural Research Service Miscellaneous Publication No. 1275, Washington, DC.

See Also

read.dly, rainfall, temperature

Examples

# GHCN daily weather file for State College, PA
# subset of data (2000-2009) for station USC00368449
#
data("weather") 

# calculate parameters for the poisson model
# using 0.3 mm as the lower limit for wet days.
weather.param.p <- wth.param(weather, method = "poisson", llim = 0.3)

# simulate ten years of rainfall
rain10.p <- rainfall(365*10, weather.param.p)

# increase per-event rainfall by 5 mm
weather.param.p5 <- weather.param.p
weather.param.p5$params$depth <- weather.param.p5$params$depth + 5
rain10.p5 <- rainfall(365*10, weather.param.p5)

# calculate parameters for the Markov chain model
# using 0.3 mm as the lower limit for wet days.
weather.param.m <- wth.param(weather, method = "markov", llim = 0.3)

# rainfall() selects Markov model based on input parameter types
rain10.m <- rainfall(365*10, weather.param.m)

# simulate 10 years of temperature
temp10 <- temperature(365*10, weather.param.p)

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