©2003 Institute of Water Research, all rights reserved
Water Quality Modelingfor Ecological Services under
Cropping and Grazing Systems
Da OuyangJon Bartholic
Institute of Water ResearchMichigan State University
©2003 Institute of Water Research, all rights reserved
Water Quality Modeling
Surface Water Quality- Soil Erosion- Sediment Delivery- Nutrient Loading (P, N)
Groundwater Quality- Pesticide / Nutrient Leaching
©2003 Institute of Water Research, all rights reserved
Water Quality Modeling
Surface Water Quality Modeling- RUSLE- SEDMOD- AGNPS / SWAT- MARI & Nutrient Loading Coefficients
Groundwater Quality Modeling- WIN-PST (Pesticide Screening Tool)
©2003 Institute of Water Research, all rights reserved
RUSLE
Revised Universal Soil Loss Equation
©2003 Institute of Water Research, all rights reserved
RUSLE
A = R K LS C P
A = Soil loss in tons per acre per yearR = Rainfall-runoff erosivity factorK = Soil erodibility factorS = Slop steepness factorL = Slope length factorC = Cover-management factorP = Support practice factor
©2003 Institute of Water Research, all rights reserved
SEDMOD
Spatially Explicit Sediment Delivery Model
©2003 Institute of Water Research, all rights reserved
Spatially Explicit Sediment Delivery Model (SEDMOD)
SDR = 39 A –1/8 + DP
Where SDR = sediment delivery ratio
A = watershed area in square km
DP = difference between the composite delivery
potential and its mean value
©2003 Institute of Water Research, all rights reserved
SEDMOD
Delivery Potential composite layer in GRID
DP = (SG)r(SG)w + (SS)r(SS)w + (SR)r(SR)w +
(SP)r(SP)w + (ST)r(ST)w + (OF)r(OF)w
Where SG = slope gradient
SS = slope shape
SR = surface roughness
SP = stream proximity
ST = soil texture
OF = overland flow index
r = parameter rating (1-100)
w = weighting factor (0-1)
©2003 Institute of Water Research, all rights reserved
Sediment Yield
SY = A * SDR
Where SY = Sediment Yield A = Gross Soil Loss SDR = Sediment Delivery Ratio
©2003 Institute of Water Research, all rights reserved
WIN-PST
Window-Based Pesticide Screening Tool
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WIN-PST(Window-Based Pesticide Screening Tool)
Assess relative likelihood of pesticide loss from - field boundaries via runoff - below the root zone via percolation
Overall risk ratings are based on a matrix of- Pesticide (toxicity, application method and rate)- Soil (Soil texture, hydrologic group, slope, water table)
©2003 Institute of Water Research, all rights reserved
MARI
Manure Application Risk Index
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MARI(Manure Application Risk Index)
Identify areas where winter-time spreading of manure may cause potential risk for runoff losses of N or P
12 Field parameters: Soils; Slope; Soil Test P;
Concentration Water Flow; Residue/Cover;
Surface Water Setback; Vegetative Buffer Width;
Manure P / N Application Rate; Manure Application Method;
Others.
©2003 Institute of Water Research, all rights reserved
Data
• DEM (Digital Elevation Model, 30-meter)• SSURGO Soil Data (Soil Survey Geographic Database)• Landuse / Land cover data• Crop Residue Management Data (CTIC)• Other - EPA BASINS
Data and Tools for Water Quality Modeling
Previous Studies
Stony Creek Study
Corn-Corn Corn-Soybean Soybean-Wheat
Erosion 182,000 154,000 67,000
Sediment 61,000 52,000 28,000
Phosphorus 129 112 59
Estimated soil loss, sediment and phosphorus inStony Creek Watershed (tons / year)
y = 2.1343x
R2 = 0.9943
0
5
10
15
20
25
30
0 5 10 15
Suspended Solids (ton)
To
tal P
ho
sp
ho
rus (
kg
)
Measured Phosphorus and Suspended SolidsIn Sycamore Creek Watershed, MI
Findings from other study (Randall et al. 1997)
NO3 – losses from row crops (corn, soybean) were 30-50 times greater than losses from perennial crops such as Alfalfa
Atrazine Leaching Risk Mapping
Great Lakes Basin
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Estimated Potential Sediment LoadingContributed from Cropland (tons/yr.)
Questions & Discussion
(C = Cover-management factor)