ARS CEAP Watershed Assessment Study

Overview• Mike Shannon and Mark Walbridge

– National Program Leaders for Water Resources

• John Sadler – ARS CEAP Coordinator

Presented at the 2009 Conference of

Original Goals of CEAP (2003)• Quantify and establish the scientific

understanding of the effects of conservation practices at the watershed scale, and

• Estimate conservation effects and benefits at regional and national scales

.

Goals of the Watershed Studies:

• quantify the measurable effects of conservation practices at the watershed scale– in-depth, initially retrospective analyses

• enhance understanding of conservation effects in the biophysical setting of a watershed

CEAP Partnerships• CEAP Steering Committee

– USDA Natural Resources Conservation Service– USDA Agricultural Research Service– USDA Cooperative State Research, Education, and

Extension Service– USDA Farm Service Agency– USDA National Agricultural Library– USDA Economic Research Service– U.S. Forest Service– U.S. Environmental Protection Agency– U.S. Geological Survey– Bureau of Land Management– National Oceanic and Atmospheric Administration

• Other partners: LGUs, SWCS, TNC, NatureServe, AFWA, ESA…

Activities within CEAP

• Bibliographies and Literature Reviews– http://www.nal.usda.gov/wqic/ceap/index.shtml

• National / Regional Assessments– Cropland– Grazing Lands – Wetlands– Wildlife

• Watershed Assessment Studies

ARS Watershed Assessment Study

Objectives:1. Develop and implement a data system.2. Measure effects of conservation practices at the

watershed scale.3. Validate models and quantify uncertainty of

model predictions.4. Develop policy-planning tools to optimize profits

and program efficiency.5. Develop regional watershed models.

Software Interface

*Future integration with USGS and EPA dbs (WQX)

• Project Goal: To quantify the effectiveness of cover crops to reduce sediment and nutrient loadings to the Choptank River.

• Model: ArcSWAT2005

• Approach: 15 non-tidal sub-basins of the Choptank River watershed will be evaluated.

• Current Research: Calibration studies underway including evaluation of streamflow estimation using radar rainfall data (NEXRAD).

Chesapeake Research

Consortium

Chesapeake Research

Consortium

Chesapeake Research

Consortium

Choptank River Watershed

Aisha Sexton, Ali Sadeghi, and Greg McCarty

USDA-ARS, Beltsville Agricultural Research Center, HRSL

Beltsville, Maryland USA

ARS-NRCS

PSWMRU

Cannonsville Reservoir 1965

5,000 ac

100 B gallons

Cannonsville Watershed

ARS-CSREES

355 sq miles

49% forest

48% grass

2% corn

200 large farms >$10K

50 small farms

11,000 dairy

1,500 beef

Temp 46 oF

Precip 45 in

TMDL:20 µgP/L

100 mi

Town Brook

14 sq miles

PSWMRU

Town Brook, New York

BMP Efficiencies Database • created from field study literature• Sediment, P, N

Pre-BMP watershed modeled with SWAT (SWAT * Database) modeling used

• to determine BMP efficiencies for multiple scenarios of BMP placement

• to determine optimal selection and placement of BMPs for watershed with regard to P loss and implementation cost

SWAT modeling, pre- and post-BMP implementation, used to evaluate effect of combined implemented BMPs on subwatershed (Gitau et al. 2008)• DP reduced by an average of 31%• PP by an average of 13%• TP by an average of 21%• (consistent with findings from observed data)

PSWMRU

Crop rotations & contour strip crop

Crop rotations & nutrient management plans

Contour strip crop & nutrient management plans

Nutrient management plans

Riparian forest buffers None

Basic Optimal

BMP placement in Town Brook lowers costs

$34/kg P removal per yr

$24/kg P removal per yr

Upper Big Walnut Creek,Columbus, OH - Kevin King et al.SEW – NRCS-ARS

Cedar Creek Watershed - ARSSt Joseph River Watershed, West Lafayette INHeathman, Flanagan, Larose, Zuercher JSWC

SWAT and AnnAGNPS tested in uncalibrated modes Focus on developing local expertise Testing baselines for sensitivity

analyses

Conservation Effects Assessment – Little River Watershed, Georgia USA – ARS - CSREES

Cause: landmanagement changes

Effects: Changes inHydrology and WQ

Dr. D. Bosch, SEWRL Tifton, GADr. J. Cho, NWRC Boise, IDDr. R. Lowrance, SEWRL Tifton, GADr. T. Strickland, SEWRL Tifton, GADr. G. Vellidis, U. of GA Tifton, GADr. J. Arnold, GSWRL Temple, TXDr. X. Zhang, TX A&M College Station, TXDr. R. Srinivasan, TX A&M College Station, TX

SWAT Calibration / Validation

Monthly streamflow

Monthly total nitrogen

Annual streamflow

Annual total nitrogen

SWAT SimulationEffects of Riparian Buffers – Existing and Enlarged

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Sediment Nitrogen Phosphorous

Modification & Application of SWAT to Landscapes with Subsurface Tiles & Enclosed Depressions (Potholes)

Walnut Creek Watershed, IA - ARS

Dan Jaynes, NSTLAli Saleh, TIAERBing Du, TIAERJeff Arnold, GSWRLEdward Osei, TIAER

Objectives

• To modify SWAT-2000 to better simulate watersheds with tile drains and surface pothole physiography

• To evaluate the modified SWAT using 10 yr (1992 to 2001) of measured flow, NO3 and atrazine data at Walnut Creek watershed, IA

• To evaluate selected management practices (MPs) for N reduction using SWAT model for Walnut Creek watershed

• To evaluate the economic impacts of the selected MPs at various adoption levels in Walnut Creek watershed

SWAT-2000 Modifications

• Depression storage water balance was modified• Pothole/HRU orientation

PotholeHRU

UplandHRU’S

Flow DirectlyTo Stream Network

Surface Runoffand Lateral Soil Flow

Contribution to Pothole

PotholeHRU

UplandHRU’S

Flow DirectlyTo Stream Network

Surface Runoffand Lateral Soil Flow

Contribution to Pothole

SWAT-2000 Modifications

• Restrictive soil layer• Soil profile saturation pattern• Dynamic water table depth

SWAT-2000 Modifications

• A longer pesticide half-life for subsoil layers

00.10.20.3

0.40.50.60.7

Calibration Validation

Nas

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cy, E

Improved SWAT-M SWAT-M

SWAT predicted reduced nutrient losses - 100% adoption of either sidedressing N vs. fall application- fall cover crop of rye after either corn, soybean or both.

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Sediment Organic N Organic P NO3-N PO4-P

Water Quality Indicators

% C

hang

e fro

m C

onve

ntio

nal P

ract

ice

Sidedress

Fall cover after corn

Fall cover after corn & soybean

Sidedress and fall cover after corn

Sidedress and fall cover after corn & soybean

South Fork Iowa River – (ARS–CSREES)SWAT modeling

• Ames: Tomer, Moorman. Temple: Rossi, Arnold.• El Reno: Moriasi. Beltsville: Doraiswami, Sadeghi

• Focus on hydrologic calibrations towards improving simulation of tile drainage at the watershed scale.

• DrainMOD equations are incorporated into SWAT and have been tested in the SFIR, resulting in improved model efficiency.

• Future efforts will be towards improving simulation of ET of corn and soybean canopies using remote sensing data and eddy covariance station data.

DrainMOD improvements to SWAT

O D F A J A O D F A J A O D F A J A O D F A J A O D F A J A O D F A J A O D F A J A O D F A J A O D F A J A

1996 1997 1998 1999 2000 2001 2002 2003 2004

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Measured SWAT2005 Modified SWAT2005

Moriasi et al., in prep.

• ~6,500 km2

• Claypan soils– High runoff potential – Sediment, nutrients,

pesticides• Mark Twain Lake is

the major public water supply in the region– Serves ~42,000 people

Mark Twain Lake/Salt River BasinColumbia MO

Mark Twain/Salt River SWAT Modeling Efforts

• SWAT results to date– Calibrations partially complete (28 sq mi, 180 sq

mi)– Could recreate observed trends in atrazine

• Modeled effects– Model sensitive to BMPs at current adoption– Model sensitive to changes in crop distribution

The crop changes in the last 15 years had greater impact than implemented BMPs.

• Improvements needed – Saturated conditions and lateral flow calculation– Variable management and land use inputs

Landscape Positions

APEX simulations by Mudgal et al (submitted), using data from Jiang et al.

• APEX was able to simulate runoff and atrazine losses on an event basis on claypan soils.

• Runoff and atrazine losses were sensitive to landscape position (Summit – Backslope – Footslope)

• Sequence mattered – reversing footslope and backslope caused significantly more losses

• Seasonal runoff and atrazine load increased up to 89% and 72% respectively, when the back slope length was increased by 20%

Summit Backslope Footslope

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2.5% BMP

5.0% BMP

7.5% BMP

Objective: Evaluate impact of converting percentages of most erosive cropland (in upper portion of the watershed) to grassland (BMP) on suspended sediment delivered downstream.

Results:• Model simulations suggest a reduction of 30-70% in sediment yield in upper part of watershed as increasing percentage of most erosive land is converted from cropland to grassland.

• Reduction of 20-40% in simulated sediment yield at lower end of the watershed.

A

BC

Impact of Converting Erosive Cropland to Grassland on Sediment YieldMichael Van Liew!

USDA-ARS, Grazinglands Research Laboratory, El Reno, Oklahoma

!Now with Montana Dept. of Environmental Quality

Beasley Lake Watershed

AnnAGNPS Validation Results - Runoff

y = 0.7472x + 3.2773R2 = 0.8324

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The Nash-Sutcliffe coefficient of efficiency is 0.81

y = 0.4319x + 135.76R2 = 0.5935

0200400600800

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Observed Sediment (kg/ha)

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The Nash-Sutcliffe coefficient of efficiency is 0.54

AnnAGNPS Validation Results -Sediment

AnnAGNPS Simulation of Annual Average Soil Erosion (1996-2003)

Average Annual Soil Erosion from 1996 to 2003 (Mg/ha.)0.045 - 0.1080.108 - 1.5651.565 - 2.2312.231 - 2.3952.395 - 3.2353.235 - 3.8163.816 - 5.7645.764 - 7.3867.386 - 8.326

FIELDS TRANSPORT OUTLET

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/ha/y

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Landscape soil erosion

Sediment yield

Sediment load

A. Existing (baseline) condition.B 7% of the watershed representing the

highest eroding cropland areas (60.3 ha.) converted to no-till soybean.

C 17% of the watershed (143.8 ha.) converted no-till soybeans.

D 33% of the watershed (281.2 ha.) converted to no-till soybeans.

E All cropland no-tilled soybeans.F All cropland reduced tillage Soybean.G All cropland conventional tillage

Soybean.H All cropland conventional tillage

cotton.I All cropland reduced tillage cotton.J All cropland no-tilled cotton.K 7% of the watershed representing the

highest eroding cropland areas (60.3 ha.) converted to grass land.

L 17% of the watershed representing the highest eroding cropland areas (143.8 ha.) converted to grass land.

M 33% of the watershed representing the highest eroding cropland areas (281.2 ha.) converted to grass land.

N All cropland converted to grass land.

Comparison of results from alternative scenarios

NATURAL RESOURCES CONSERVATION SERVICEU. S. DEPARTMENT OF AGRICULTURE

AnnAGNPS modeling - Percent reduction in sediment loading to outlet by converting from all conventionall tillage to all no-till

5/6/2004100-Yr. climate simulation

4-Yr. Landuse

N

EW

S5 0 5 Miles

UPPER AUGLAIZE WATERSHED

#

Delphos

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Lima

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Wapakoneta

PutnamCounty

AllenCounty

PutnamCounty

Van WertCounty

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MercerCounty Auglaize

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AuglaizeCounty

LoganCounty

ShelbyCounty

% Reduction In SedimentLoading to outlet

County BoundariesWatershed BoundaryStreamsLEGEND

0 - 2525 - 5858 - 7171 - 8484 - 100

% Sediment Load Reduction from Conventional Till to No-Till Using AnnAGNPS and EGEM as standalone computer models

Sediment Load Reductions:70% - Tillage-Induced Gullies 35% - Sheet & Rill Erosion 60% overall sediment loading

Required estimate of gully runoff-sediment discharge relationship from EGEM for each cell.

EGEM was not integrated with AnnAGNPS.

Ohio Upper Auglaize CEAP Special Emphasis AGNPS Watershed Modeling

> 70 % of Total Sediment Load Originates from Gullies

Upper Auglaize WatershedUnit Area Sediment Loading at Ft. Jennings

0.000

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0.408

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Scenario

Sheet & Rill Sediment LoadingEphemeral Gully Sediment Loading

ScenariosA. All fall plow (alt.17)B. Existing (alt.9)C. 12.1% with highest erosion to no-till (alt.10)D. Random 17.4% to no-till, 7.6% to grass (alt.16)E. 7.9% with highest slope to grassland (alt.13)F. 25.7% with highest erosion to no-till (alt.11)G. 39.5% with highest erosion to no-till (alt.12)H. 17.4% with highest slope to grassland (alt.14)I. All cropland no-tilled (alt.18)J. 27.1% with highest slope to grassland (alt.15)K. All cropland converted to trees (alt.19)

Impact of Conservation Practices Identified by Erosion Source

Kansas Cheney Lake CEAP Special Emphasis AGNPS Watershed Modeling

Nearly 1000 tillage-induced gullies identified

Gully Locations

Sediment Load

by Unit Area Ranking Ratio

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10% of the drainage area produces 76% of the sediment load

36% of the sediment load originated as ephemeral gully erosion

Kansas Cheney Lake CEAP Special Emphasis AGNPS Watershed Modeling

RED ROCK SUBWATERSHED OUTLET Sediment Load

by Unit Area Ranking Ratio

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Sed. Load with gullies

Sed. Load, no gullies64% of the sediment load originated as tillage-induced gully erosion

10% of the drainage area produces 70% of the sediment load

Linking Economic and Environmental Models

Economic Model –Environmental Quality Index Model –Conservation Program Efficency Model - SWAT

Models: point vs. spatially distributedWhittaker, G. and Scott, D.W. 1999..

Static Link: Run economic (optimization) model, use results in environmental modelWhittaker, G., 2005.

Dynamic Link: Information passed between environmental model and economic model during optimization.Whittaker, G., et al. 2007

Modeling Summary• SWAT calibrated and validated

– Leon (TX), Little (GA), Town Brook (PA/NY), Upper Big Walnut (OH), Mahantango (PA), Ft. Cobb (OK), Cedar Creek (IN/OH), South Fork (IA), Goodwater Creek (MO), Walnut Creek (IA)

– Underway at Choptank (MD)– Scaling up at several sites– Several scenario analyses

• Links with SWAT – REMM – APEX– Economic models

• AnnAGNPS calibrated and validated – Beasley, Goodwin and Yalobusha (MS), Upper

Auglaize (OH), Cheney Lake (KS) [NRCS Spec Emph]– Also work at Walnut Creek (IA), Cedar Creek (IN/OH)