Dennis C. FlanaganResearch Agricultural Engineer

USDA-Agricultural Research ServiceNational Soil Erosion Research Laboratory

West Lafayette, Indiana, USA

Ms. Qiuju WuVisiting Scholar, Purdue University & Ph.D. Candidate, Northwest A&F Univ., China

Chi-hua Huang Research Soil Scientist, USDA-ARS, National Soil Erosion Research Laboratory

NSERL lab staff – 7/7/2014

Description of the problem Experimental approach & results

• Interrill experiments• Rill experiments

Modeling approach & derived USLE K factors Summary and Conclusion

NRCS is moving towards spatial databases for soils that rely upon equations to estimate important parameters, such as USLE soil erodibility “K” factors.

The equations are based upon the soil erodibility nomograph, published with USLE in 1978, which has been noted to not perform well for some soils (soils high in clay, etc.).

State NRCS offices can appeal new determinations of USLE “K” values for problem soils.

An easy way to determine USLE “K” values experimentally is not available.

USLE “K” values historically were determined using observed sediment loss data from tilled fallow erosion plots, over many (10+) years of natural rainfall events.

In some cases, field rainfall simulation studies can be conducted on a reference soil with a known erodibility and then raining on a test soil under the same tilled fallow conditions, to give a relative scaling factor and test soil “K”.

Either natural rainfall plots, or large rainfall simulation studies are difficult, time-consuming, and very expensive.

ARS – NSERL was contacted by NRCS state soil scientists in South Dakota and Vermont in late 2012.

Each state had a problem soil, that using the soil erodibility nomographresulted in greatly reduced “K” values.

Requests were made to help NRCS determine ways to estimate “K” factors experimentally or through modeling.

The 2 states supplied soil to the NSERL to conduct experiments.

Determine baseline WEPP model erodibility parameters (Ki, Kr, τcr).

Interrill erodibility determined using small soil boxes under simulated rainfall.

Rill erodibility and critical shear stress determined using mini-flumes with varying inflows of water.

Study 2 reference soils and 2 test soils.

South Dakota Soil(Opal clay)

Interrill Rill

Ki Kr τcr

Initially Dry ConditionSaturated ConditionDrainage Condition

Indiana Soil(Miami silt loam) y )

Vermont Soil(Vergennes silty

clay loam)

Flow Chart of the Experiment

y )

Missouri Soil(Mexico silty

clay loam)

Equipment - Soil Box

Soil Box Standard: 1.0×0.6×0.4 m3

Soil depth: 0.05 m

Constant head water supply system to saturate soil boxes

Experiment in progress

Slope: 9%Measurements: Runoff Rate

Detachment Rate

Rainfall Intensity(mm h-1)

Duration(min)

Interval(min)

50 60 5

25 20 5

75 10 2

100 10 2

0.00E+00

5.00E-06

1.00E-05

1.50E-05

2.00E-05

2.50E-05

3.00E-05

3.50E-05

0 1000 2000 3000 4000 5000 6000 7000Time (s)

Dry

Drainage

Saturated

(a) Runoff

Run

off R

ate

(m s-1

)

0.00E+00

5.00E-04

1.00E-03

1.50E-03

2.00E-03

2.50E-03

0 1000 2000 3000 4000 5000 6000 7000

Time (s)

Dry

Drainage

Saturated

(b) Sediment Discharge

Sedi

men

t Dis

char

ge R

ate

(kg

m-2

s-1)

(1) Saturated condition had the largest runoff discharge rate.(2) Dry condition had the largest sediment discharge rate.(3) Drainage condition had the lowest sediment discharge rate.

0.00E+00

5.00E-06

1.00E-05

1.50E-05

2.00E-05

2.50E-05

3.00E-05

3.50E-05

0 1000 2000 3000 4000 5000 6000 7000Time (s)

Dry

Drainage

Saturated

(a) Runoff

Run

off R

ate

(m s-1

)

0.00E+00

5.00E-04

1.00E-03

1.50E-03

2.00E-03

2.50E-03

0 1000 2000 3000 4000 5000 6000 7000Time (s)

Dry

Drainage

Saturated

(b) Sediment Discharge

Sedi

men

t Dis

char

ge R

ate

(kg

m-2

s-1)

(1) Saturated condition had the greatest runoff rate; It also had the largest sediment discharge rate.

(2) Drainage condition had the lowest sediment discharge rate.

0.00E+00

5.00E-04

1.00E-03

1.50E-03

2.00E-03

2.50E-03

0 2000 4000 6000 8000 10000Time (s)

Dry

Drainage

Saturated

(b) Sediment Discharge

Sedi

men

t Dis

char

ge R

ate

(kg

m-2

s-1)

0.00E+00

5.00E-06

1.00E-05

1.50E-05

2.00E-05

2.50E-05

3.00E-05

3.50E-05

0 2000 4000 6000 8000 10000Time (s)

Dry

Drainage

Saturated

(a) Runoff

Run

off R

ate

(m s-1

)

(1) Saturated condition had the maximum runoff rate.(2) Dry condition had the maximum sediment discharge rate. (3) Drainage condition had the minimum sediment discharge rate.

(1) Saturated condition had the maximum runoff rate.(2) Dry condition had the maximum sediment discharge rate. (3) Drainage condition had the minimum sediment discharge rate.

(a) Runoff (b) Sediment Discharge

(1)

Di = interrill detachment rate (kg m-2 s-1)Ki = interrill erodibility (kg s m-4)I = rainfall intensity (m s-1)σ = interrill runoff rate (m s-1)Ω = interrill slope angle in degrees

∗ ∗ . .(2)

From WEPP interrill equation (Foster et al., 1995) NSERL Report #10, Chapter 11

IN Soil (Miami sil)

SD Soil (Opal cl)

VT Soil (Vergennes sicl)

MO Soil(Mexico sicl)

Ki-dry x106

(Kg s m-4) 7.33 a* 3.01 b 3.78 a 3.83 a

Ki-drainage x106

(Kg s m-4)3.73 b 1.23 c 1.84 c 2.24 c

Ki-saturated x106

(Kg s m-4)3.80 b 4.00 a 2.79 b 3.11 b

*Means in same column followed by the same lower case letter are not significantly different using Duncan’s multiple range test at α=0.05.

WEPP Interrill erodibilities of the three soils.

Mini-flume: 0.5m × 0.046m × 0.12m

Soil Depth: 0.05 m

Mini-flume Equipment

V-Shape

Water Supply System

Micro-flow meter

Sample Collection

Soil type Moisture Condition

Minimum flow rate(ml min-1)

Maximumflow rate(ml min-1)

SampleNumber Slope

Indiana Soil (Miami silt loam)

Dry 100 700 123%Saturated 100 700 12

Drainage 200 750 12

South Dakota Soil (Opal clay)

Dry 200 750 123%Saturated 100 700 12

Drainage 200 750 12

Vermont Soil (Vergennes siltyclay loam)

Dry 200 1600 122%Saturated 100 1400 12

Drainage 300 1600 11Missouri Soil Dry 200 1600 12(Mexico silty clay Saturated 100 1400 12 2%loam) Drainage 300 1600 12

Kr ( ) (3)

Dc = detachment capacity rate (kg m-2 s-1)Kr = rill erodibility (s m-1)

= flow shear stress (Pa)= critical flow shear stress (Pa)

(4)

From Foster et al. (1995) - NSERL Report #10, Chapter 11

(5)

= specific weight of water (N m-3)= hydraulic radius of the flow (m)

S = Slope gradient of the rill (m m-1)

. // (6)

n = Manning roughness coefficient, n=0.02Q = Flow rate (m3 s-1)

From Shainberg et al., (1994)

Dc = 0.0082(τ - 0.50)R² = 0.5831

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0 0.2 0.4 0.6 0.8 1

Dc

(kg

m-2

s-1)

Shear Stress (Pa)

τcr

Detachment rate vs. Shear stress

Dry soil: Df = 0.0032 (τ - 0.54) __________

Saturated: Df = 0.0040 (τ - 0.60) - - - - - - - - -

Drained: Df = 0.0008 (τ - 0.67) ………………

Dry soil: Df = 0.0021 (τ - 0.51) __________

Saturated: Df = 0.0024 (τ - 0.45) - - - - - - - - -

Drained: Df = 0.0006 (τ - 0.61) ………………

Dry soil: Df = 0.0035 (τ - 0.49) __________

Saturated: Df = 0.0069 (τ - 0.45) - - - - - - - - -

Drained: Df = 0.0013 (τ - 0.54) ………………

Dry soil: Df = 0.0037 (τ - 0.24) __________

Saturated: Df = 0.0054 (τ - 0.24) - - - - - - - - -

Drained: Df = 0.0019 (τ - 0.32) ………………

IN Soil (Miami sil)

SD Soil (Opal c)

VT Soil (Vergennes sicl)

MO Soil (Mexico sicl)

Kr - dry (s m-1) 0.0032 b 0.0021 ab 0.0035 b 0.0037 b

Kr - saturated (s m-1) 0.0040 a 0.0024 a 0.0069 a 0.0054 a

Kr - drainage (s m-1) 0.0008 c 0.0006 c 0.0013c 0.0019 c

τcr - dry (Pa) 0.54 c 0.51 b 0.49 b 0.24 b

τcr - saturated (Pa) 0.60 b 0.45 c 0.45 c 0.24 b

τcr - drainage (Pa) 0.67 a 0.61 a 0.54 a 0.32 a

*Means for each parameter in same column followed by the same lower case letter are not significantly different using Duncan’s multiple range test at α=0.05.

Description of the problem Experimental approach & results Interrill experimentsRill experiments

Modeling approach & derived USLE K factors Summary and Conclusions

Utilize the Water Erosion Prediction Project (WEPP) process-based model

Simulate a USLE unit plot 9% uniform slope, 22.1 m long Continuous tilled fallow

Generate 100 years of climate at location of interest using CLIGEN

Back calculate USLE “K” value using WEPP soil loss prediction and RUSLE “R” factor

A = R K L S C PA = Average annual soil loss (tonnes ha-1)R = Rainfall / runoff erosivity (MJ cm ha-1 h-1)K = Soil erodibility factor (t ha h MJ-1 ha-1 cm-1)L = slope length factor = 1.0 on unit plotS = slope steepness factor = 1.0 on unit plotC = cover and management factor = 1.0 on unit plotP = support practice factor = 1.0 on unit plot

K = A / RA = Average annual soil loss (tonnes ha-1)R = Rainfall/runoff erosivity (MJ cm ha-1 h-1)K = Soil erodibility factor (t ha h MJ-1 ha-1 cm-1)

A is obtained from our WEPP model runs as the long-term average erosion rate from a 100-yr simulation.

R is obtained from the RUSLE iso-erodent maps

1. With WEPP interface, created an interpolated climate station at the location of soil collection.

2. Using CLIGEN v5.3 and Fourier interpolation between months, created a 100-yr climate input file for WEPP for this location.

3. Used results from lab experiments for Ki, Kr and τcrvalues to use in the WEPP soil file.

4. Analysis on topsoil from the USDA-NRCS National Soil Survey Laboratory in Lincoln, Nebraska (tested 12/2013) for other soil inputs to model.

5. Management was a tilled fallow, with a field cultivation operation every month from April through October each year.

6. Ran 100 year model simulations and recorded results.

R Factor(MJ cm ha-1 h-1)

Initially dry soil -Soil Loss(t•ha-1•yr-1)

Saturated soil - Soil

Loss (t•ha-1•yr-1)

Saturated / drained soil -

Soil Loss(t•ha-1•yr-1)

Indiana Soil(Miami sil) 289 145 145 53.4

South Dakota Soil(Opal c) 107 43.8 50.9 14.1

Vermont Soil(Vergennes sicl) 158 113 188 45.9

Missouri Soil(Mexico sicl) 357 170 221 94.2

Location “R” factors and WEPP model simulation results

0.00

0.20

0.40

0.60

0.80

1.00

1.20

IN Soil SD Soil VT Soil MO Soil

K-V

alue

(t·h

a·h·

MJ-1

·ha-1

·cm

-1)

Nomograph KRUSLE2-KDry-KSaturated-KDrainage-K

0.0

0.5

1.0

1.5

0.0 0.5 1.0 1.5

Nom

ogra

ph K

and

RU

SLE

2 da

taba

se K

Initially Dry Soil K - Value

Nomograph K

RUSLE2 K

VT Vergennes silty clay loam

SD Opal clay

2 reference soils (Miami sil, Mexico sicl) and 2 test soils (Opal c, Vergennes sicl) were tested in the lab to determine Ki, Kr, and τcr.

Miami sil soil had the greatest Ki under initially dry soil moisture conditions due to rapid wetting and slaking/sealing of the soil surface.

Saturated soil conditions resulted in the greatest Krvalues for all soils, due to reduced soil cohesion.

Drainage soil conditions resulted in the lowest Krvalues and greatest τcr values over all soils. The slow wetting and then drainage strengthened the soil, making it more resistant to detachment by flow.

τcr ranged from 0.24 to 0.67, which is not a very large range. These values are somewhat lower than typical values measured in WEPP field experiments.

All soils had equivalent back-calculated “K” factors (using initially dry soil lab values) that were similar to the RUSLE2 database values.

The 2 reference soils had nomograph “K” values also similar to the lab/WEPP-derived values.

The 2 test soils (Opal clay, Vergennes silty clay) had nomograph “K” values substantially lower than either the lab/WEPP-derived or RUSLE2 values.

Use of the existing RUSLE2 database values for the Opal clay soil from SD and the Vergennes silty clay soil from VT, instead of using lower “K” values determined from the USLE soil erodibility nomograph, is justified.

Laboratory/modeling procedure developed here is simple, rapid, and provides a linkage between the empirical and process-based erosion prediction models.