Blanchard Watershed Modeling
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Transcript of Blanchard Watershed Modeling
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Blanchard Watershed Modeling
Laura Weintraub, Amanda Flynn, Joe DePinto
Great Lakes Tributary Modeling Program 516(e) Meeting
May 18, 2011
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Western Basin Lake Erie• Concerns
–Sedimentation–Increasing SRP loads–Algae blooms
•Maumee Basin–Largest tributary sediment source to Lake Erie–Highly agricultural watershed (~80%)–Focus of WLEB Partnership
• Maumee Bay / Toledo Harbor dredging–Annual volume: ~640,000 yd3 (2004-08)–Annual cost: ~$5 million
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Sources to Western Basin of Lake Erie (2005)
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Blanchard River Watershed: Project Overview
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Fine-scale Watershed Models of the Maumee BasinObjectives
• Continue effort to apply fine-scale models to Maumee watersheds (build upon Upper Auglaize)
• Quantify sediment and nutrient loading
• Evaluate land management alternatives to estimate potential benefit from reduced loading
• Support broader sediment and nutrient modeling efforts of the lower Maumee River and Maumee Bay
Funding Under 516(e)Timeline: Jul 2009 to Oct 2010
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Integrated Project Team
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USACE- Buffalo DistrictByron Rupp
Funding, Technical Review, Project Oversight
USACE- ERDCBilly Johnson
Contracting, Technical Review
LimnoTechJoe DePinto, Greg Peterson
Laura WeintraubAmanda Flynn, Pranesh Selvendiran
Technical Lead, Project Management, Reporting
USDA-NRCSJim Stafford, Steve DavisSoils, Crop Management
USDA-ARSRon Bingner, Fred TheurerAnnAGNPS Model Support
Univ. of ToledoKevin Czajkowski, David Dean
GIS Data (Topography, Land Cover, Soils)
Heidelberg Univ.Pete Richards
Historical WQ Data
USGSGreg Koltun
Hydraulic Geometry, Climate
Additional Technical SupportNutrients (OSU – Libby Dayton)
Point Sources (OEPA)
Project Team
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Blanchard River Watershed
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AnnAGNPS Background
Developed by USDA-ARS• Continuous simulation of surface
runoff and pollutant loading• Incorporates revised universal soil
loss equation (RUSLE)• Provides most utility at monthly or
annual scales
Models flow, suspended solids, and nutrients
• Simulates direct surface runoff and tile drain flow based on SCS curve number
• Distinguishes between sheet and rill, ephemeral gully erosion
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AnnAGNPS Sediment Erosion• Sheet and Rill Erosion
– Overland flow or small concentrated flow paths
– Calculated based on RUSLE
– AnnAGNPS algorithmsthoroughly tested
• Ephemeral Gully Erosion– Erosion in deep, narrow
channels– Calculated based on TI-EGEM– Limited testing of AnnAGNPS
algorithms
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Sheet and Rill Erosion Ephemeral Gully Erosion
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AnnAGNPS Data Requirements
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Input Data Type
Data Sources
Topography/DEM USGS
Stream network/NHD USGS
Meteorology NCDC
Soils SSURGO
LULC/Tillage LANDSTAT, USGS, USDA
Reach Geometry USGS
Point Sources EPA PCS
FeedlotsEPA PCS, Watershed
Rapid Assessment, TMDL Report
Fertilizer / Manure
Application
Blanchard Watershed Rapid Assessment
Streamflow Data USGS, Heidelberg University, OEPA
Water Quality Data
Heidelberg University, OEPA
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Spatial Input Data
Soil Name Soil Type % AreaBlount silt loam 39.77%
Pewamo silty clay loam 18.67%Paulding clay 6.45%Toledo silty clay loam 3.31%
Lenawee silty clay loam 3.29%All Other Soils 28.52%
3,830 cellsAverage cell size = 52 ha
Model Cell Delineation with Dominant Soils
Approximately 1500 PEG sitesFunction of:
• CTIndex (1000)• Watershed topography
Potential Ephemeral Gully Locations
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2005-2008 Crop and Tillage Rotation
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• Data from remote sensing - compared with NRCS transect data• Developed a detailed four (4) year crop rotation and tillage
operation sequence for each cropland cell• Removed unrealistic combinations (Example: WNCTCMSN)
Year 2005 2006 2007 2008
Crop Wheat Corn Corn Soybean
Tillage No Till Traditional Till Mulch Till No Till
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Model Calibration/Confirmation Datasets and Time Periods
• Hydrology– USGS (04189000) at Findlay – 1923 to Current (daily)– USGS (01489950) at Cuba – 2005 to 2007 (daily)
• Water Quality (solids, nitrogen, phosphorus)– Heidelberg at Findlay – 2007 to Current (daily)– OEPA seven “sentinel” stations – 2005 to 2006 (~ 2x per month)– OEPA ~100 stations – 1991 to 2008 (variable and infrequent)
• Calibration 2002 – 2009• Confirmation 1995 – 2001
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Hydrology Calibration
• Calibration resulted in a “good” to “very good” prediction of runoff
• Runoff slightly over-predicted at Cuba and slightly under-predicted at Findlay
• Annual performance better than monthly or daily
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
2002 2003 2004 2005 2006 2007 2008 2009
Runo
ff (c
fs)
Blanchard River at CubaAnnual Average Runoff
(2002-2009)HYSEP PART AnnANGPS
0
100
200
300
400
500
600
700
800
900
1,000
2002 2003 2004 2005 2006 2007 2008 2009
Runo
ff (c
fs)
Blanchard River at FindlayAnnual Average Runoff
(2002-2009)
HYSEP PART AnnANGPS
Cuba NSE R2
Time HYSEP PART HYSEP PART
Annual 0.79 0.83 0.86 0.85
Monthly 0.69 0.66 0.69 0.67
Daily 0.60 0.59 0.60 0.59
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Hydrology Calibration (continued)
• Runoff under-predicted late winter/early spring and over-predicted summer/early fall time periods
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
1 2 3 4 5 6 7 8 9 10 11 12
Surf
ace
Runo
ff (
ac-ft
/mon
th)
Month
Average Monthly Runoff (2008)
Observed Simulated
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Water Quality Calibration (Sediment)• Annual performance
“very good”• Monthly and daily
performance less robust ranging from “fair to good”
• Ephemeral gully erosion was 85% of the total landscape erosion
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Time NSE R2
Annual 0.86 0.90
Monthly 0.39 0.40
Daily 0.50 0.51
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0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
TP (l
bs/m
onth
)
Month - Year
Monthly Average TP Load(2007-2009)
Observed Simulated
Water Quality Calibration (Total Phosphorus and Total Nitrogen)
• “Poor” to “fair” performance• Sensitive to initial soil concentrations• Limitations in model capabilities for nutrient cycling• Fertilizer application timing in model may not reflect “on
the ground” practices
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5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
TN (lb
s/m
onth
)
Month - Year
Monthly Average TN Load(2007-2009)
Observed Simulated
Total P Total N
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AnnAGNPS Model Application• Goal: Test the impact of land management
alternatives on watershed loadings• Process:
– Coordinate with stakeholders to develop a set of reasonable BMPs/land management alternativesNRCS, Blanchard River Watershed Partnership, Environmental Defense Fund, Putnam Soil and Water Conservation District, Ohio DNR, Northwest Ohio Flood Mitigation Partnership
– Translate BMPs into model, direct or indirect representations
– Run scenarios and interpret results
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Selected Management Alternatives• Tile Drain Management• Conservation Tillage• Cover Crops• Cropland Conversion to Grassland
– random cropland (~10%) to grassland– targeted cropland (~10%) to grassland
• Improved Nutrient Management• All Natural Watershed• Combined Management
– conservation tillage + cropland to grassland + nutrient management
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Example BMP ScenarioConvert dominant highly erodible cells to improved
rotation and tillage
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Continuous Corn with Traditional Till
(CTCTCTCT or CTCTBNCT)
Continuous Corn with Traditional Till
(CTCTCTCT or CTCTBNCT)
Rotating Corn and Beans with
Conservation Tillage(CMBNCMBN)
Rotating Corn and Beans with
Conservation Tillage(CMBNCMBN)
Moldboard plow
Mulch till
continuous corn with traditional till
corn/bean rotation with conservation till
Converted 7,683 acres• 2.5 % of total crop area• 56 watershed cells
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Sediment Alternative Scenario ResultsBase versus Combined Management
• Random cropland conversion = -2%• Targeted cropland conversion = -54%• Combined management = -60%
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Sediment Maps
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Base Case Combined Management Scenario
Example: Sediment load reduction in Lye Creek Watershed due to improved land management practices
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A B C D E F H I J K
Tot P
(tn/
yr)
A - Base caseB - Drain managementC - Conservation tillageD - Cover cropsE - Random cropland to grassland conversionF - Targeted cropland to grassland conversionH - Nutrient management (fertilizer 80% of base case)I- Nutrient management (fertilizer 60% of base case)J - Nutrient management (fertilizer 40% of base case)K - Combined management
Phosphorus Alternative Scenario Results
• Cover crops across all conventional tilled land = -25%• Reduce fertilizer by 60% = -21%• Combined management = -24%
Base versus Combined Management
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Nitrogen Alternative Scenario ResultsBase versus Combined Management
• Conservation tillage = -24%• Cover crops across all conventional tilled land = -39%• Combined management = -75%
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Project Summary• Fine-scale model adequately simulates runoff and
suspended sediment on annual basis• Less confidence in simulation of TN and TP loading • Potential land management alternatives explored to
estimate possible benefits• Targeting placement of BMPs to highly erodible
areas likely to result in higher reductions of loads• Final report available from GLC (October 19, 2010)
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Recommendations for Future Work• Examine additional management scenarios:
– Seasonal variations of tile drains and nutrient application– Conversion to conservation tillage, cover crops, or grassland
• Investigate and potentially refine nutrient algorithms• Investigate / ground-truth ephemeral gully erosion algorithms• Use model to support watershed action plan development• Apply fine-scale models to other Maumee Basin watersheds
(e.g., Tiffin) • Coordinate with modeling to characterize sediment and
nutrient transport in the lower Maumee River / Toledo Harbor
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