Evaluation of WEPP runoff and erosion

prediction: Goodwater Creek, Field One

Nayereh Ghazanfarpour1

Claire Baffaut2

Clark J. Gantzer1

Department of Soil, Environmental and Atmospheric Sciences1

USDA-ARS2

July 2014

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Introduction

Estimating of soil loss by farmers and land managers to target BMP placement.

“ Water Erosion Prediction Project ” WEPP (Lane and Nearing, 1989), physics-based

model for hillslopes / watersheds.

“The Agricultural Policy/Environmental eXtender” APEX (Williams et al., 2008) -

field/watershed scale model.

Apply the WEPP model on the Goodwater Creek Field #1 within the claypan soil

region and compare to APEX results (Mudgal et al. 2010).2

Claypan Soils

• Surface runoff is a main hydrologic process causing excessive losses of NPS pollutants (Lerch et al. 2005).

• Events immediately following herbicide and/or fertilizer application are most risky for the downstream area and water bodies.

• Characterized by a subsoil horizon with large increase in clay content within a short vertical distance in the soil profile.

• During the winter and spring periods, the clays are swollen and causing a high probability of runoff.

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ObjectivesStudies have been done using WEPP model in locations across the U.S. (Savabi, 1993; Savabi et al., 1995; Zhang et al., 1996; Baffaut et al., 1997; Laflen et al., 2004; Laflen, 2011)

Apply the WEPP model for prediction of runoff and soil loss.

Evaluate WEPP’s prediction compared to the APEX model (Mudgal et al. (2010) in the

same area).

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WEPP model advantages

Spatial and temporal variability in topography, soil properties, cropping and management.

Predicting spatial and temporal distributions of net soil loss or gain for any period of time.

Capable of calculating sediment delivery to the stream channel.

May be used both in single-event and continuous simulation mode.

Results allow land managers and conservationists to delineate CMAs to target locations for

BMP placement.

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GeoWEPP

ArcGIS extension for the WEPP model

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Comparing WEPP and APEX

WEPP model APEX model

Rainfall

distributionBreakpoints/ CLIGEN Daily precipitation

Infiltration Green– Ampt Mein Larson (GAML)-Modification of the curve number method

-Green– Ampt infiltration equation

Runoff Solution of the kinematic wave equation Curve number (CN) equation

Erosion Steady state sediment continuity equation Modified USLE equation (Williams 1995)

Field Area

Field 1, a 35 ha (88 ac) agricultural field

Average annual precipitation 968 mm (38.1 in)

Average annual min and max daily temp. 6.3°Cand 16.9°C

Management in a corn–soybean annual crop rotation.

Under mulch tillage, maintaining ~30% residue cover

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Weir and automatic sampler

Weather Station

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WEPP and user inputs

1- Climate data

2- Management input

* Measured Data

(Inputs acquired from the USDA-ARS)

*

A soil survey (1:5,000 scale) in the field during 1997

Soil properties:

Texture, Cation Exchange Capacity (CEC), Organic

carbon content, Sum of bases, and pH in each soil

profile

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WEPP and user inputs: 3-

http://ned.usgs.gov/about.html (2009) 

Topography: (3m)

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WEPP and user inputs: 4-

Tolerable value of erosion

T = 3 t/ac H2

H1

H3

H1 : 2.29 t/ac

H2 : 2.8 t/ac

H3 : 2.62 t/ac

1 T <=Sediment yield < 2 T

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Results

H: Hillslope

Results

y = 1.0286x ‐ 46.69R² = 0.78NSE= 0.69

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0 10000 20000 30000 40000 50000

Simulated

 runo

ff (m

3)

Measured runoff (m3)Period: 1993‐2002

y = 0.67x + 2.13R² = 0.80NSE= 0.78

Pbias= 5.6 %RSR= 0.47

0

50

100

150

0 50 100 150

Simulated

 runo

ff (m

m)

Measured Runoff (mm)Calibration period (1993‐1997) 

y = 0.58x + 3.43R² = 0.79NSE= 0.72

Pbias= 22.7 %RSR= 0.53

0

50

100

150

0 50 100 150

Simulated

 runo

ff (m

m)

Measured runoff (mm)Validation period (1998‐2002)

Runoff – Event based from WEPP

Runoff – Event based from APEX 0604

Not having a calibration and a validation period.

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Runoff – Monthly (total) by WEPP

y = 0.9605x + 411.92R² = 0.84NSE= 0.83

0

10000

20000

30000

40000

50000

60000

70000

80000

0 20000 40000 60000 80000

Simulated

 runo

ff (m

3)

Measured runoff (m3)Period: 1993‐2002

y = 0.7938x + 15364R² = 0.89NSE= 0.88

0

50000

100000

150000

0 50000 100000 150000

Simulated

 runo

ff (m

3)

Measured runoff (m3)Period: 1993‐2002

Results

Runoff –Yearly (total) by WEPP

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y = 0.4522x + 0.2197R² = 0.50NSE= 0.53Pbias= 2 %RSR= 0.69

0

5

10

0 5 10

Simulated

 sed

imen

t yield (t ha‐1)

Measured sediment yield (t ha‐1)Calibration period (1993‐1997)

Sediment yield –event based by WEPP

y = 0.143x + 0.4101R² = 0.01NSE= 1.7

Pbias= ‐140 %RSR= 1.65

0

1

2

3

0 1 2 3

Simulated

 sedimen

t yield (t ha‐1)

Measured sediment yield (t ha‐1)Validation period (1998‐2002)

ResultsSediment yield –event based by APEX 0604

y = 0.4962x + 0.17R² = 0.38NSE= 0.34

0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 6 7 8 9

Simulated

 sedimen

t yield (t ha‐1)

Measured sediment yield (t ha‐1)All data (1993‐2002)

Dates: X: 26 Feb 97/  Y: 29 Jun 98

y = 0.583x + 0.142R² = 0.61NSE= 0.6

0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 6 7 8 9

Simulated

 sedimen

t yield (t ha‐

1)

Measured sediment yield (t ha‐1)removed 2 points (1993‐2002)

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Sediment yield –monthly (total) by WEPP

y = 0.6231x + 3.2089R² = 0.59NSE= 0.48

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14

Simulated

 sed

imen

t yield (t ha‐1)

Measured sediment yield (t ha‐1)All data (1993‐2002)

y = 0.7688x + 2.3873R² = 0.86NSE= 0.75

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14Simulated

 sed

imen

t yield (t ha‐1)

Measured sediment yield (t ha‐1)removed 2 points (1993‐2002)

y = 0.6129x + 0.3536R² = 0.57NSE= 0.55

0

2

4

6

8

10

12

0 2 4 6 8 10 12Simulated

 sed

imen

t yield (t ha‐1)

Measured sediment yield (t ha‐1)removed 2 points (1993‐2002)

ResultsSediment yield– yearly (total) by WEPP

y = 0.5614x + 0.4098R² = 0.42NSE= 0.36

0

2

4

6

8

10

12

0 2 4 6 8 10 12Simulated

 sed

imen

t yield (t ha‐1)

Measured sediment yield (t ha‐1)All data (1993‐2002)

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* Reasonably high R2 and NSE values for the simulation of runoff by WEPP (withoutcalibration process & considering slope of regression).

An important reason: methods in the models; GAML equation for the WEPP modeland curve number for APEX.

Green–Ampt model performs better than SCS (Chahinian et al. 2005; Shen, 2010).

The main criticism of the SCS CN method: The amount of simulated runoff is notsensitive to rainfall intensity and is independent of event duration or rainfall intensity.

Discussion- Runoff

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Discussion- Sediment yield

The analysis showed that WEPP simulated sediment yield better than APEX.R2=0.61, NSE= 0.60

One important reason is the different methods used in the models.

WEPP uses the Steady-state sediment continuity equation to predict soil loss.

APEX sediment yield simulation is the MUSLE module. In MUSLE, the rainfallenergy factor is replaced with a runoff factor that allows to simulate individualstorm events.

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Discussion- WEPP & APEX

It seems difficult to reproduce low sediment yields (<2 T/ha) with WEPP or with APEX.

Uncertainty of the measurement of low sediment yields. The weir always causes some

amount of backwater and slowing down of the flow. When velocities are low (small

events), soil particles tend to precipitate and are not picked up by the sampler. So

simulated values being greater than measured ones (points along the y axis).

In addition, the sampling is proportional to flow, i.e. water samples are collected

according to flow. Thus when flow is low, samples are collected with lower frequency

and there is greater error in the “measured” sediment load.

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Conclusion & Summary

Reasonably high R2 and NSE values for the simulation of runoff and sediment yield (0.78

and 0.69, and 0.61 and 0.6, respectively) showed that the WEPP model performed

satisfactorily.

Overall predictions of event-based runoff by the WEPP (without calibration) and APEX

(after calibration) models during simulation period were satisfactory.

On the basis of R2 and NSE, the WEPP model provided better predictions than the APEX

model for event- based sediment yield.

WEPP performed good: Information have significant implications for management

and could allow land managers and conservationists to delineate critical areas based

on them.

Evaluation and comparison of the WEPP and APEX model will help to select a

model that meet needs based on the amount of available input information and the

capabilities of the personnel in the organizations.

Conclusion & Summary

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Next Goals

Scenario: No tillage with a winter cover crops.

Following soybean in rotation with corn:

1.Overseeded when soybean leaves begin to turn yellow and drop: cereal rye (rye)

2.The cover crop can be controlled in early/mid April.

Following corn in continuous corn or in rotation with soybean:

1.Overseeded: late August/early September one month before harvest - cereal rye (rye)

2.The cover crop can be controlled in early/mid April.

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Acknowledgment• University of Missouri Experiment Station• USDA- NRCS, Conservation Innovation Grant• USDA- ARS, Cropping Systems and Water Quality Research

Nayereh GhazanfarpourPhone: 573-823-6830E-mail: ghazanfarpourn@missouri.edu

Thank you

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