Blanchard Watershed Modeling

Laura Weintraub, Amanda Flynn, Joe DePinto

Great Lakes Tributary Modeling Program 516(e) Meeting

May 18, 2011

2

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

Sources to Western Basin of Lake Erie (2005)

Blanchard River Watershed: Project Overview

4

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

Integrated Project Team

5

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

Blanchard River Watershed

6

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

7

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

8

Sheet and Rill Erosion Ephemeral Gully Erosion

AnnAGNPS Data Requirements

9

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

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

2005-2008 Crop and Tillage Rotation

11

• 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

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

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

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

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

15

Time NSE R2

Annual 0.86 0.90

Monthly 0.39 0.40

Daily 0.50 0.51

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

160

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

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

17

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

Example BMP ScenarioConvert dominant highly erodible cells to improved

rotation and tillage

19

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

Sediment Alternative Scenario ResultsBase versus Combined Management

• Random cropland conversion = -2%• Targeted cropland conversion = -54%• Combined management = -60%

Sediment Maps

21

Base Case Combined Management Scenario

Example: Sediment load reduction in Lye Creek Watershed due to improved land management practices

0

100

200

300

400

500

600

700

800

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

Nitrogen Alternative Scenario ResultsBase versus Combined Management

• Conservation tillage = -24%• Cover crops across all conventional tilled land = -39%• Combined management = -75%

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)

24

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

25