Erosion and runoff evaluation

Erosion and Runoff Evaluation in

Goodwater Creek Experimental

Watershed using the SWAT-T Model

Sitarrine Thongpussawal, PhD Candidate

C. Gantzer, Professor, University of Missouri C. Baffaut, Research hydrologist, USDA- ARSH. Shao, Fellow, University of Guelph, Canada

Soil & Atmospheric Sciences

University of Missouri

Introduction

• Terracing is a conservation practices to reduce

erosion and intercept runoff from steep lands and is

widely used for soil and water conservation (Dorren and

Rey, 2004; Neibling and Thompson, 1992).

• Chow et al. (1999) indicated terracing sloping fields

in combination with grassed waterways and contour

planting in Canada decreased soil loss from an

average of 20 t/ha to less than 1 t/ha, and reduced

25% of the runoff.

Terrace Geometry

Cutslope or Riser

Photo courtesy USDA NRCS

(after Shao et al., 2013)

Frontslope or Bed

Undisturbed

Terrace Unit

LuLb Lb

Lterrace

LrLr Lr

A terrace unit = 3 segments

• An undisturbed segment,• Two riser segments, and• A bed segment

What is SWAT-T ?

• “A modified terrace algorithm of Soil and

Water Assessment Tool (SWAT)” developed

by Shao et al. (2013) and incorporated into

SWAT 2009 to simulate the effects of terraces

for difference practices on erosion and runoff

at a watershed scale.

Comparison of SWAT and SWAT-T

SWAT SWAT-T

Approximately estimates the

effects caused by land shape

changes after installing of

terraces using average slope

length

Estimates specific effect by

defining the slope length and

slope steepness of each

segment of a terrace unit

Lacks simulation of added

infiltration and evaporation of

water trapping and storage in

terraces

Effectively simulate added infiltration and evaporation of water trapping and storage in terraces

• SWAT-T model was evaluated for terraced fields

with different crop managements and soils in

Frankling County, Kansas (after Shao et al., 2013).

• The model showed good performance for runoff and

sediment simulation at field-scale (after Moriasi et al., 2007) .

• However, no watershed-scale evaluation has been

undertaken with SWAT-T.

SWAT-T Evaluation

OBJECTIVE

• To evaluate annual and monthly SWAT-T

performance compared to observed

data of Goodwater Creek Experimental

Watershed (GWEC) from 1993-2010.

Materials and Methods

Goodwater Creek Experimental Watershed

• Boone and Audrain Counties of North Central Missouri

• Area: 73 km2

• No. of Sub basins: 7• No. of Hydrologic

Response Units (HRU): 183

Study Area

Mexico 79.6%

Belknap 2.6%

Putnam 5.2%

Adco 7.1%

Leonard 5.4%

Total 100%

Soils

LegendMexico

Belknap

Putnam

Adco

Leonard

Soil Map of Goodwater Creek

Source: USDA-ARS-Cropping Systems and Water Quality Research Unit (CSWQ)

0-0.5 9.8%

0.5-1.0 2.9%

1.0-2.0 75.8%

> 3.0 2.1%

Total 100%

Slope Slope Classes of Goodwater Creek

Source: USDA-ARS-CSWQ

Agriculture 78.3%

Forest 2.6%

Pasture 9.8%

Residential 4.7%

Other 4.8%

Land Use Land Use of Goodwater Creek


• Problems with degraded water quality from sediment,

nutrients and herbicides.

• High potential of runoff

since the watershed has

mostly claypan soil.

Current Soil and Water Problems of Study Area

Photo courtesy Young, F.J., 1995

Data Source

Digital Elevation Model (DEM) USDA/ARS,Cropping

Systems and

Water Quality

Research Unit

(CSWQ),

Columbia, MO

Digital Soil Map

Digital land use map

Crop management data from 1993-2010

Terrace areas and locations

Sources of Input Data

Data AvailabilityData Year Source

The climate data: -Daily precipitation -Daily temperature-Relative humidity-Solar radiation-Wind speed

1993 - 2010 USDA/ARS,

CSWQ

Hydrologic data

-Daily stream flow data

-Daily sediment data

1993 - 2010

1993 - 2010

Study ProcedureData preparation and Input

Model Setup

Model Simulations

Model Calibration & Performance Evaluation

Model Validation & Performance Evaluation

Analysis & Discussions

DEM, Land Use, Soils, Climate

• Watershed delineation,• Identify terrace fraction,• Define slope length and

steepness

• Adjust sensitive parameters for erosion and runoff

• Evaluate model performance according to R2, NSE, RSR and PBIAS

Location of Goodwater Creek

Terraced FractionSub Basin 2

Crop land 16.3 km2

Terrace area 2.1 km2

Terraced Fraction:

16.30/2.13 = 0.13

LegendStream gages

Weather station

Precipitation

gages

W1

W3

W2


Terrace simulation algorithm in SWAT-T

after Shao et al., 2013

Terrace simulation algorithm in SWAT-T

After Shao et al., 2013

Results of Flow

Results of Average Annual Flow Calibration

Model Performance during Annual Flow Calibration

Variable Period Time Step R2 NSE RSR PBIAS

Flow 1993-2001

Annual 0.94 0.91 0.28 -6.96

Performance Rating VeryGood

VeryGood

VeryGood

VeryGood

Results of Average Annual Flow Validation

Model Performance during Annual Flow Validation


Flow 2002-2010

Annual 0.97 0.95 0.20 5.60


VeryGood

VeryGood

VeryGood

Results of Average Monthly Flow Calibration

Model Performance during Monthly Flow Calibration


Flow 1993-2001

Monthly 0.77 0.71 0.51 -6.85


Good Good VeryGood

Results of Average Monthly Flow Validation

Model Performance during Monthly Flow Validation


Flow 2002-2010

Monthly 0.73 0.66 0.56 5.63

Performance Rating Good Good Good VeryGood

Results of Sediment

Results of Average Annual Sediment Calibration

Model Performance during Annual Sediment Calibration


Sediment 1993-2001

Annual 0.78 0.53 0.64 -33.74


Good Satisfactory Satisfactory

Results of Average Annual Sediment Validation

Model Performance during Annual Sediment Validation

Variable Period Time Step

R2 NSE RSR PBIAS

Sediment 2002-2010

Annual 0.42 0.36 0.76 -12.73

Results of Average Monthly Sediment Calibration

Model Performance during Monthly Sediment Calibration


R2 NSE RSR PBIAS

Sediment 1993-2001

Monthly 0.82 0.49 0.60 -33.75

Performance Rating Very Good Satisfactory Satisfactory

Results of Average Monthly Sediment Validation

Model Performance during Monthly Sediment Validation


R2 NSE RSR PBIAS

Sediment 2002-2010

Monthly 0.29 -0.59 1.21 22.29

Ongoing Work• Cross check observed sediment data with USDA-

ARS.

• Identify outlier values and redo calibration and

validation.

• Compare SWAT-T with SWAT for erosion and runoff

simulation at HRU scale.

Discussions

SUMMARY• SWAT-T is a terraced algorithm incorporated

into SWAT 2009 to simulate the terrace

effects in difference management practices

at watershed scale.

• It provides an alternative to predict terrace

benefit to conservation of soil and water by

modeling terrace effect for terraced HRUs.

Summary

• Results of SWAT-T calibration showed the

feasibility of simulation for erosion and runoff

from terraced fields, but need to improve

validation results.

• Future work, compare SWAT-T with SWAT for

erosion and runoff simulation at HRU scale.

Acknowledgement

Dr. Clark Gantzer University of MissouriDr. Claire Baffaut USDA-ARS Dr. Stephen Anderson University of MissouriDr. Hui Shao University of Guelph, Canada Dr. Fessehaie Ghidey USDA-ARS

Funding and Support: University of Missouri, Mizzou Advantage

Royal Thai Government/Land Development Department (LDD)

University of Missouri, Agric. Expt. Stn.

USDA-ARS Cropping Systems and Water Quality Unit

Questions

The hillside rice field terraces of Thailand

www.reddit.com

HRUSlope Steepness (%) Slope Length (m)

Undisturbed Bed Riser Undisturbed Bed Riser 15 0.015 0.05 0.05 37.0 4.0 4.0 16 0.015 0.05 0.05 37.0 4.0 4.0 17 0.015 0.05 0.05 37.0 4.0 4.0 18 0.015 0.05 0.05 37.0 4.0 4.0 19 0.015 0.05 0.05 37.0 4.0 4.0 20 0.015 0.05 0.05 37.0 4.0 4.0 21 0.015 0.05 0.05 37.0 4.0 4.0 22 0.015 0.05 0.05 37.0 4.0 4.0

Defining slope length and steepness of segments in Sub basin 2

Variable Parameter File Range Default Value

Calibrated Value

Flow SFTMP .bsn 1.0 1.5SMTMP .bsn 0.5 -2.5SMFMN .bsn 4.5 1.5SNOCOVMX .bsn 1.0 25ESCO .bsn, .hru [0,1] 0.95 0.90SURAG .bsn 4.0 1.0SHALLIST .gw 0.5 600ALPHA_BF .gw [0,1] 0.048 0.4QWQMN .gw [0,1,000] 0.00 150GW_REVAP .gw [0.02,0.2] 0.02 0.055*REVAPMN .gw 1.0 125

Default and Calibrated Values of SWAT-T calibration parameters for Flow

* : Hru 15-22

Variable Parameter File Range Default Value

Calibrated Value

Sediment USLP_P .mgt [0,1] 1.0 0.70USLE_K .sol [-25,25] 0.43 0.28USLE_C Crop.dat for corn & soybean were adjusted according to

the values given by Alberts et al. 1984CH_EROD .rte [0,1] 0.0 0.6CH_COV2 .rte [0,1] 0.0 0.6ADJ_PKR .bsn 0.0 0.8PRF .bsn 1.0 0.8SPCON .bsn [0.0001,0.01] 0.0001 0.0055SPEXP .bsn [1,2] 1.0 1.25

Default and Calibrated Values of SWAT-T calibration parameters for Sediment

• A watershed in SWAT is divided into several sub-

basins.

• Sub-basins are further sub-divided into hydrologic

response units (HRUs) that are comprised of

homogeneous soil, land use, and slope.

• The HRUs represent percentages of the sub-basin

area and are not spatially located within a SWAT

simulation (Baffaut et al., 2014; Gassman et al., 2007).

Soil and Water Assessment Tool (SWAT)

• SWAT-T model was tested using annual crop yield

and event runoff and sediment data sets collected

at a six-plot terraced field with different crop

managements and soils in Frankling County,

Kansas (after Shao et al., 2013).

• The runoff simulation was satisfactory (after Moriasi et

al., 2007) with Nash-Sutcliffe model efficiency (NS)

always > 0.6 for both the calibration and validation

simulations

SWAT-T Evaluation

• For the sediment simulation, SWAT-T performed

satisfactory for all calibration of till and no-till plots,

but less satisfactory for the validation of no-till

plots.

• Calibration showed the feasibility of simulating

sediment and runoff from terraced fields using

SWAT-T, but need to do more research with SWAT-

T across a variety of soil and topography condition.

SWAT-T Evaluation

Discussions• SWAT-T performed well to simulate runoff in both

calibration and validation of annual and monthly time

step with all R2 and NS are higher 0.65.

• In sediment simulation, SWAT-T performed

satisfactory during calibration of annual and monthly

time step, but less consistent during validation.

Observed sediment

data

Note:

• Outliers

• Non-linearity

Discussions

• Sediment observed data is not statistical significant

Discussions

Erosion and runoff evaluation

Environment

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