ars ceap watershed assessment study overviewcalibrations partially complete (28 sq mi, 180 sq mi)...
TRANSCRIPT
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
0
2
4
6
8
10
12
14
16
No Buffers ExistingBuffers
Continuous 14m Bufers
Sedi
men
t Lo
ad (
tons
/ha)
0
20
40
60
80
100
120
No Buffers ExistingBuffers
Continuous14 m Bufers
Tota
l Nit
roge
n Lo
ad (
kg/h
a)
0
2
4
6
8
10
12
14
16
18
No Buffers ExistingBuffers
Continuous 14m Bufers
Tota
l Pho
spho
rous
Loa
d (k
g/ha
)
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
h-S
utcl
iffe
Effi
cien
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.
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
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
0
20
40
60
80
100
120
140
160
180
Mon
thly
dis
char
ge (m
m)
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
0
10
20
30
40
50
60
70
80
40 60 80 100 120 140
Perc
ent R
educ
tion
in S
edim
ent Y
ield
Watershed Area (km2)
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
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70 80 90
Observed Runoff (mm)
Sim
ula
ted
Ru
no
ff (
mm
)
The Nash-Sutcliffe coefficient of efficiency is 0.81
y = 0.4319x + 135.76R2 = 0.5935
0200400600800
100012001400160018002000
0 1000 2000 3000 4000
Observed Sediment (kg/ha)
Sim
ula
ted
Sed
imen
t (k
g/h
a)
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
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
A B C D E F G H I J K L M N
Scenario
Lan
dscap
e E
rosio
n/S
ed
imen
t L
oad
/Sed
imen
t Y
ield
(T
/ha/y
ear)
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
#
Lima
#
Wapakoneta
PutnamCounty
AllenCounty
PutnamCounty
Van WertCounty
Van
Wer
t Cou
nty
MercerCounty Auglaize
County
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
0.0690.074
0.0990.105
0.110
0.1590.156
0.179
0.226
0.408
0.001
0.0520.059
0.0620.059
0.069
0.0700.074
0.072
0.082
0.116
AB
CD
EF
GH
IJ
K
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
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Contributing Drainage Area [%]
Con
trib
uted
Sed
imen
t Loa
d [%
] Sed. Load with gulliesSed. Load w/o gullies
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
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Contributing Drainage Area [%]
Co
ntr
ibu
ted
Sed
imen
t L
oad
[%
]
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)