Garden with Insight v1.0 Help: Erosion  Water Erosion
The EPIC component for waterinduced erosion simulates erosion caused by rainfall and runoff and by
irrigation (sprinkler and furrow). To simulate rainfall/runoff erosion, EPIC contains six equations  the
USLE (Wischmeier and Smith, 1978), the OnstadFoster modification of the USLE (Onstad and Foster,
1975), the MUSLE (Williams, 1975), two recently developed variations of MUSLE (MUST and MUSS),
and a MUSLE structure that accepts input coefficients (MUSI). Only one of the equations (MUSI)
interacts with other EPIC components. The six equations are identical except for their energy components.
The USLE depends strictly upon rainfall as an indicator of erosive energy. The MUSLE and its variations
use only runoff variables to simulate erosion and sediment yield. Runoff variables increase the prediction
accuracy, eliminate the need for a delivery ratio (used in the USLE to estimate sediment yield), and enable
the equation to give single storm estimates of sediment yields. The USLE give only annual estimates. The
Onstad Foster equation contains a combination of the USLE and MUSLE energy factors.
Water Erosion From Rainfall
The water erosion model uses an equation of the form [Equation 118] and [Equation 119], where Y
is the sediment yield in t/ha, K is the soil erodibility factor, CE is the crop management factor, PE is the
erosion control practice factor, LS is the slope length and steepness factor, ROKF is the coarse fragment
factor, Q is the runoff volume in mm, q(p) is the peak runoff rate in mm/hr, A is the watershed area in ha,
MUST is a new equation theoretically developed from sediment concentration databases, MUSS is a new
equation developed by fitting small watershed data (no channel erosion), and MUSI allows user input of
four coefficients (b(y1)..b(y4)). The PE value is determined initially by considering the conservation
practices to be applied. The runoff model supplies estimates of Q and q(p).
Equation 118
Y = chi * K * CE * PE * LS * ROKF
Code:
same
Variables:
Y = WaterErosion_tPha
chi = energyComponent
K = soilErodibilityFactor
CE = cropManagementFactor
PE = erosionControlPracticeFactor
LS = slopeLengthAndSteepnessFactor
ROKF = coarseFragmentFactor
Now all the energy equations. We will start with the MUSLE and its derivatives then move to the USLE
and OnstadFoster.
MUSLE
Equation 119
chi = 1.586 * power(Q * q(p), 0.65) * power(A, 0.12)
Code:
chi = 1.586 * power(Q * q(p), 0.65) * power(A + 1.0, 0.12)
Variables:
chi = WaterErosionEnergyComponentByMUSLE
Q = runoffVolume_mm
q(p) = peakRunoffRate_mmPhr
A = watershedArea_ha
MUST
Equation 119
chi = 2.5 * sqrt(Q * q(p))
Code:
same
Variables:
chi = WaterErosionEnergyComponentByMUST
Q = runoffVolume_mm
q(p) = peakRunoffRate_mmPhr
MUSS
Equation 119
chi = 0.79 * power(Q * q(p), 0.65) * power(A, 0.009)
Code:
chi = 0.79 * power(Q * q(p), 0.65) * power(A + 1.0, 0.009)
Variables:
chi = WaterErosionEnergyComponentByMUSS
Q = runoffVolume_mm
q(p) = peakRunoffRate_mmPhr
A = watershedArea_ha
MUSI
Equation 119
chi = by(1) * power(Q, by(2)) * power(q(p), by(3)) * power(A, by(4))
Code:
chi = by(1) * power(Q, by(2)) * power(q(p), by(3)) * power(A + 1.0, by(4))
Variables:
chi = WaterErosionEnergyComponentByMUSS
Q = runoffVolume_mm
q(p) = peakRunoffRate_mmPhr
A = watershedArea_ha
by(1)(2)(3)(4) = userCoeffsForMUSIPtr
For the energy component for USLE and OnstadFoster, we have to move forward to equations 125136.
This is going to get a bit mucky...
To estimate the daily rainfall energy in the absence of timedistributed rainfall, it is assumed that the
rainfall rate is exponentially distributed
r(t) = r(p) * exp(t / kappa) (Equation 124)
where r is the rainfall rate at time t in mm/hr, r(p) is the peak rainfall rate in mm/hr and kappa is the
decay constant in hrs. Equation 124 contains no assumption about the sequence of rainfall rates (time
distribution).
The USLE energy equation in metric units is
RE = deltar * (12.1 + 8.9 log (deltar / deltat)) (Equation 125)
where RE is the rainfall energy for water erosion equations and deltar is a rainfall amount in mm during
a time interval deltat in hours. The energy equation can be expressed analytically as
RE = 12.1 * 0~infinity r dt + 8.9 0~infinity log r dt (Equation 126)
Substituting equation 124 into equation 126 and integrating gives the equation for estimating daily rainfal
energy:
RE = R * (12.1 + 8.9 * (log r(p)  0.434)) (Equation 127)
where R is the daily rainfall amount in mm. The rainfall energy factor, EI, is obtained by multiplying
equation 127 by the maximum 0.5hr rainfall intensity (r(0.5)) and converting to the proper units:
[Equation 128].
Equation 128
EI = R * (12.1 + 8.9 * (log(r(p))  0.434)) * r(0.5) / 1000
Code:
r(0.5) = 2.0 * R * alpha(0.5)
they do not mention how to get r(0.5) in publication
also add lower bound at 0
Variables:
EI = RainfallEnergyFactorForUSLE
R = rainfallWithoutSnowmeltForDay_mm
r(p) = peakRainfallRate_mmPhr
r(0.5) = maxRainfallIntensityInHalfHour_mmPhr
Equation 128 USLE part
chi = EI
Code:
chi = EI * 1.292
Variables:
chi = WaterErosionEnergyComponentByUSLE
EI = rainfallEnergyFactorForUSLE
To compute values for r(p), equation 124 is integrated to give
R = r(p) * kappa (Equation 129)
and
R(t) = R * (1  exp(t / kappa)) (Equation 130)
The value of R(0.5) can be estimated by using alpha(0.5) as mentioned in the Hydrology section of this
section:
R(0.5) = alpha(0.5) * R (Equation 131)
To determine the value of r(p), equations 131 and 129 are substituted into equation 130 to give [Equation
132].
Equation 132
r(p) = 2 * R * ln(1.0  alpha(0.5))
Code:
same
Variables:
r(p) = PeakRainfallRate_mmPhr
R = rainfallForDay_mm
alpha(0.5) = propTotalRainFallsInFirstHalfHour_frn
Since rainfall rates vary seasonally, alpha(0.5) is evaluated for each month by using Weather Service
information (U.S. Department of Commerce, 1979). The frequency with which the maximum 0.5hr
rainfall amount occurs is estimated by using the Hazen plotting position equation (Hazen, 1930)
[Equation 133] where F is the frequency with which the largest of a total of tau events occurs. The total
number of events for each month is the product of the number of years of record and the average number
of rainfall events for the month.
Equation 133
F = 1 / (2 * tau)
Code:
same
Variables:
F = FrequencyOfRainfallEvents_Pyr
tau = yearsRecordMaxHalfHourRain
To estimate the mean value of alpha(0.5), it is necessary to estimate the mean value of R(0.5). The value
of R(0.5) can be computed easily if the maximum 0.5hr rainfall amounts are assumed to be exponentially
distributed. From the exponential distribution, the expression for the mean 0.5hr rainfall amount is
R(0.5) = R(0.5F)/log(F) (Equation 134)
where R(0.5) is the mean maximum 0.5hr rainfall amount, R(0.5F) is the maximum 0.5hr rainfall
amount for frequency F, and subscript k refers to the month.
The mean alpha(0.5) is computed with the equation [Equation 135] where R is the mean amount of
rainfall for each event (averagy monthly rainfall/average number of days of rainfall) and subscript k refers
to the month. Daily values of alpha(0.5) are generated from a triangular distribution.
Equation 135
alpha(0.5) = R(0.5) / R
Code:
alpha(0.5) = R(0.5) / log((F / numWetDaysForMonth) * R)
Variables:
F = frequencyOfRainfallEvents
alpha(0.5) = MeanPropTotalRainFallsInFirstHalfHourForMonth_frn
R(0.5) = meanMaxHalfHourRainfallAmountForMonth_mm
R = meanRainfallPerEventForMonth_mm = meanTotalRainfallForMonth_mm / numWetDaysForMonth
The lower limit of alpha(0.5), determined by a uniform rainfall, is to 0.5/24 or 0.0208. The upper limit of
alpha(0.5) is set by considering a large rainfall event. In a large event, it is highly unlikely that all the
rainfall occurs in 0.5 hr (alpha(0.5) = 1.0). The upper limit of alpha(0.5) can be estimated by substituting
a high value for r(p) (250 mm/hr is generally near the upper limit of rainfall intensity) into equation 130
[Equation 136] where alpha(0.5u) is the upper limit of alpha(0.5). The peak of the alpha(0.5) triangular
distribution is alpha(0.5) from equation 135. So the lower limit is 0.0208, the upper limit is the aResult of
equation 136, and the peak is the aResult of equation 135.
Equation 136
alpha(0.5u) = 1.0  exp(125 / R)
where R is mean daily rainfall for the month
Code:
alpha(0.5u) = 1.0  exp(125 / (R + 5))
where R is rainfall for the day
Variables:
alpha(0.5u) = MaxPropTotalRainFallsInFirstHalfHourForMonth_frn
R = meanRainfallPerEventForMonth_mm = meanTotalRainfallForMonth_mm / numWetDaysForMonth
R = rainfallWithoutSnowmeltForDay_mm
For the USLE method, X is the same as EI (equation 118), but for the OnstadFoster method, we need a
function for that part of equation 118.
Equation 118
chi = 0.646 * EI + 0.45 * power(Q * q(p), 0.33)
Code:
same
Variables:
chi = WaterErosionEnergyComponentByOnstadFoster
EI = rainfallEnergyFactorForUSLE
Q = runoffVolume_mm
q(p) = peakRunoffRate_mmPhr
Now to fill in the other parts of the water erosion equation, we go back to equations 120123 and stick
equation 137 on the end.
The value of LS is calculated with the equation (Wischmeier and Smith, 1978) [Equation 120] where S is
the land surface slope in m/m, lamda is the slope length in m, and xi is a parameter dependent upon slope.
Equation 120
LS = pow(lambda / 22.1, xi) * (65.41 * sqr(S) + 4.56 * S + 0.065)
Code:
same
Variables:
LS = SlopeLengthAndSteepnessFactor
lambda = slopeLength_m
xi = slopeLengthFactorParam
S = slopeSteepness_mPm
The value of xi varies with slope and is estimated with the equation [Equation 121].
Equation 121
xi = 0.3 * S / (S + exp(1.47 61.09 * S)) + 0.2
Code:
same
Variables:
xi = SlopeLengthFactorParam
S = slopeSteepness_mPm
The crop management factor is evaluated for all days when runoff occurs by using the equation [Equation
122] where CE(mn,j) is the minimum value of the crop management factor for crop j and CV is the soil
cover (above ground biomass plus residue) in t/ha.
Equation 122
CE = exp[(ln(0.8)  ln(CE(mn,j))) * exp(1.15 * CV) + ln(CE(mn,j))
Code:
same (ln(0.8) is 0.2231)
Variables:
CE = CropManagementFactor
CE(mn,j) = patchMeanMinCropManagementFactor
CV = patchTotalAboveGroundBiomassAndResidue_tPha
The soil erodibility factor, K, is evaluated for the top soil layer at the start of each year of simulation with
the equation [Equation 123] where SAN, SIL, CLA, and C are the sand, silt, clay, and organic carbon
contents of the soil in percent and SN1 = 1  SAN / 100. Equation 123 allows K to vary from about 0.1 to
0.5. The first term gives low K values for soils with high coarsesand contents and high values for soil
with little sand. The fine sand content is estimated as the product of sand and silt divided by 100. The
expression for coarse sand in the first term is simply the difference between sand and the estimated fine
sand. The second term reduces K for soils that have high clay to silt ratios. The third term reduces K for
soils with high organic carbon contents. The fourth term reduces K further for soils with extremely high
sand contents (SAN>70%).
Equation 123
K = [0.2 + 0.3 * exp(0.0256 * SAN * (1  SIL/100))
* pow(SIL / (CLA + SIL), 0.3)
* [1  0.25 * C / (C + exp(3.72  2.95 * C))
* [1  0.7 * (1SAN/100) / ((1SAN/100) + exp(5.51 + 22.9 * (1SAN/100)))
Code:
same
Variables:
K = soilErodibilityFactor
SAN = soilSandContent_pct
SIL = siltContent_pct
CLA = clayContent_pct
C = organicC_pct
SN1 = nonSandContent_frn
The coarse fragment factor is estimated with the equation (Simanton et al. 1984) [Equation 137] where
ROK is the percent of coarse fragments in the surface soil layer.
Equation 137
ROKF = exp(0.03 * ROK)
Code:
same
Variables:
ROKF = CoarseFragmentFactor
ROK = rockContentSurfaceSoilLayer_pct
Water Erosion From Irrigation
Erosion caused by applying irrigation water in furrows is estimated with MUST [Equation 138]
where CE, the crop management factor, has a constant value of 0.5. The volume of runoff is estimated as
the product of the irrigation volume applied and the irrigation runoff ratio. The peak runoff rate is
estimated for each furrow by using Mannings equation and assuming that the flow depth is 0.75 of the
ridge height and that the furrow is triangular. If irrigation water is applied to land without furrows, the
peak runoff rate is estimated by dividing the runoff volume by the duration (24 hrs).
Equation 138
Y = 2.5 * power(Q * q(p), 0.5) * K * CE * PE * LS
Code:
Y = 11.8 * 1000 * power(Q * q(p), 0.56) * K * CE * PE * LS / distance_m
Variables:
Y = WaterErosionFromIrrigation_tPha
Q = furrowVolume_mm
q(p) = peakRunoffRateForFurrow_m3Psec
K = soilErodibilityFactor
CE = kCropManagementFactor = 0.5
PE = erosionControlPracticeFactor
LS = slopeLengthAndSteepnessFactor
