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©2003 Institute of Water Research, all rights reserved Water Quality Modeling for Ecological Services under Cropping and Grazing Systems Da Ouyang Jon Bartholic Institute of Water Research Michigan 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 year R = Rainfall-runoff erosivity factor K = Soil erodibility factor S = Slop steepness factor L = Slope length factor C = Cover-management factor P = 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
©2003 Institute of Water Research, all rights reserved 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
©2003 Institute of Water Research, all rights reserved 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
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 in Stony Creek Watershed (tons / year)
Measured Phosphorus and Suspended Solids In Sycamore Creek Watershed, MI
Findings from other study (Randall et al. 1997) NO 3 – 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
©2003 Institute of Water Research, all rights reserved Estimated Potential Sediment Loading Contributed from Cropland (tons/yr.)
Questions & Discussion (C = Cover-management factor)
1 CIG Specialist Introductory Meeting Wednesday, December 20 th, 2006 Computer Lab Room 105 Farrall Agriculture Engineering Hall Michigan State University.
Predicting Sediment and Phosphorus Delivery with a Geographic Information System and a Computer Model M.S. Richardson and A. Roa-Espinosa; Dane County.
1 High Impact Targeting (HIT) “Applying Conservation Tools to the Worst Erosion Areas for Maximum Sediment/Nutrient Reductions“ Glenn O’Neil: Institute.
Soil Water Assessment Tool (SWAT) Model Input Assefa Melesse.
Phosphorus Index for Oregon and Washington Steve Campbell USDA - Natural Resources Conservation Service Portland, Oregon Dan Sullivan Oregon State University.
™ Nutrient Management Planning ¨ Will these be mandated in your state? An emerging national issue is how to account for agricultural non-point source.
1 RUSLE 2 Wisconsin Website da.gov/technical/cons plan/rusle Judy Derricks-WI RUSLE2 MANAGER.
Lecture ERS 482/682 (Fall 2002) Erosion and sediment transport ERS 482/682 Small Watershed Hydrology.
Hydrological modelling in the context of land use change and climate change Emil A. Cherrington Research Associate, CATHALAC
Surface Water Simulation Group. Comprehensive watershed scale model developed and supported by the USDA-ARS capable of simulating surface and groundwater.
Impervious Cover and Erosion: Brushy Creek, Round Rock, Texas GRG 360-G Spring 2004 Beau Barnett Brett Franco.
Estimating Soil Erosion From Water Using RUSLE By: Andrea King USDA-Natural Resource Conservation Service.
An open source version of the Nonpoint-Source Pollution and Erosion Comparison Tool Climate Tools Café Webinar Dave Eslinger, Ph.D. 3 May, 2012.
Phosphorus Indices: an Understanding of Upper Mississippi Strategies John A. Lory, Ph.D. Division of Plant Sciences University of Missouri.
Load Estimation Using Soil and Water Assessment Tool (SWAT)
Using the Missouri P index John A. Lory, Ph.D. Division of Plant Sciences Commercial Agriculture Program University of Missouri.
Lab 13 - Predicting Discharge and Soil Erosion Estimating Runoff Depth using the Curve Number method –Land use or cover type –Hydrologic condition –Soil.
October 5, 2005, The 4th IAHR Symposium on River, Coastal and Estuarine Morphodynamics Field Observation and WEPP Application for Sediment Yield in an.
Assessing Fire Damage and Erosion Potential in Forestland Affected by the Cerro Grande Wildfire of 2000 Rachel Anne Rebecca November 21, 2000.
2013 KY NRCS (590) Nutrient Management Standard Highlights: NRCS 590 is now only required for producers applying to receive NRCS financial or technical.
Developing a GIS-Based Soil Erosion Potential Model for the Jemez Watershed – Using the Revised Universal Soil Loss Equation (RUSLE) Josh Page CE 547 –
Https://engineering.purdue.edu/ecohydrology October 12, 2015 Iowa State University Indrajeet Chaubey Purdue University Water Quality.
FNR 402 – Forest Watershed Management. Forest Watershed Management Course Objective: Understand the purposes and procedures of watershed management, the.
Soil Erosion Estimation TSM 352 Land and Water Management Systems.
The Effect of Compost Application and Plowing on Phosphorus Runoff Charles S. Wortmann Department of Agronomy and Horticulture Nutrient Management for.
ERDS Region 7 GIS-Assisted Approach in Soil Erosion Assessment using Manifold Software.
A Model for Evaluating the Impacts of Spatial and Temporal Land Use Changes on Water Quality at Watershed Scale Jae-Pil Cho and Saied Mostaghimi 07/29/2003.
Soil Conservation. Erosion Two billion tons of U.S. soil lost annually Improved from Five billion tons in 1982 Conservation programs and voluntary conservation.
“AerWay No Till on Highly Erodible Lands” July 1, 2011 – June 30, 2012 Capital RC&D Council Hammond, La.
Conservation Effects Assessment Project (CEAP) Measuring the Environmental Benefits of Conservation Managing the Agricultural Landscape for Environmental.
PREDICTION OF SOIL LOSSES. EMPIRICAL WATER EROSION FORMULAS A= k s 0,75 L 1,5 I 1,5 (Kornev,1937) A= k s 1,49 L 1,6 (Zingg,1940) A= k s 0,8 p I 1,2 (Neal,1938)
Additional Questions, Resources, and Moving Forward Science questions raised in the development of a science assessment Effect of Conservation Tillage.
SOIL EROSION ASSESSMENT Measurement of Water Erosion Universal Soil Loss Equation (USLE) - predict annual soil loss by water – Wischmeier and Mannering,
Runoff Processes Reading: Applied Hydrology Sections 5.6 to 5.8 and Chapter 6 for Tuesday of next week.
Project collaborators: Laura Ward Good, Katie Songer, Matt Diebel, John Panuska, Jeff Maxted, Pete Nowak, John Norman, K.G. Karthikeyan, Tom Cox, Water.
P Index Development and Implementation The Iowa Experience Antonio Mallarino Iowa State University.
Developing Modeling Tools in Support of Nutrient Reduction Policies Randy Mentz Adam Freihoefer, Trip Hook, & Theresa Nelson Water Quality Modeling Technical.
Iowa Nutrient Reduction Strategy: Background Information Reid Christianson, P.E., Ph.D. Center for Watershed Protection Ellicott City, Maryland.
Precision Management beyond Fertilizer Application Hailin Zhang.
Watershed Management Assessment Through Modeling: SALT and CEAP Dr. Claire Baffaut Water Quality Short Course Boone County Extension Office April 12, 2007.
Planning Process for CNMPs Vicki S. Anderson Resource Conservationist Natural Resources Conservation Service.
LOGO Soil Erosion Assessment using GIS and RUSLE model By Yongsik Kim CE 394K GIS in Water Resources.
What makes the The Universal Soil Loss Equation Go ?
David Rounce. Outline Why Erosion Potential RUSLE Model Process of Project Relevance.
1 Erosion and Sedimentation Processes, Factors and Impacts on the Environment Level IB: Advanced Fundamentals Seminar Education and Training Requirements.
Evaluating Bids in the U.S. Conservation Reserve Program Ralph E. Heimlich Deputy Director for Analysis, Resource Economics Division, Economic Research.
Brad Barber Project Manager for SCFA Texas Forest Service Brad Barber Project Manager for SCFA Texas Forest Service.
Basin-scale assessment of transport of water and agricultural chemicals through the unsaturated zone Rick Webb, Randy Bayless, Tracy Hancock, Chuck Fisher,
1 Conservation Innovation Grant Introductory Meeting November 9, 2006 Institute of Water Research Michigan State University East Lansing, MI.
Iowa P-Index Relationship to Feedlots Steve Brinkman CCA Nutrient Management Specialist USDA / NRCS
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