modeling nutrient and sediment losses from cropland - 2006 ... · management effects. univ. of...
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Univ. of Minnesota
Modeling Nutrient and Sediment Modeling Nutrient and Sediment Losses from CroplandLosses from Cropland
D. J. MullaDept. Soil, Water, & Climate
University of Minnesota
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Univ. of Minnesota
Watershed Management FrameworkWatershed Management Framework● Identify the problems and their extent● Monitor water quality● Evaluate pollution sources (modeling)● Set water quality goals (modeling)● Prioritize watersheds and
agroecoregions● Identify and implement BMPs to
improve water quality ● Evaluate progress towards goals
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Univ. of Minnesota
Watershed ModelingWatershed Modeling
● Used to represent transport and fate of pollutants from the landscape to mouth of watershed
● Accuracy depends on ability of model to represent actual transport and fate processes
● Ability to evaluate effect on flow and water quality of alternative scenarios
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Univ. of Minnesota
Model Selection CriteriaModel Selection Criteria
● Questions to be answered● Processes and pathways simulated● Spatial and temporal resolution needed● Complexity of model● Availability of input data● Time frame needed for results● Costs and staff expertise
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Univ. of Minnesota
Simulation ModelsSimulation Models
● Export coefficient models● Statistical models● Mechanistic watershed scale models
– HSPF - EPA– SWAT, AGNPS - USDA– ADAPT - Univ. of Minnesota, Ohio State
Univ. (DRAINMOD + GLEAMS + Routing)● Mechanistic field scale models
– EPIC, RZWQM, DRAINMOD, GLEAMS
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Univ. of Minnesota
Export Coefficient ModelsExport Coefficient Models
● Able to differentiate water quality impacts across broad land use classes
● Unable to account for variability caused by soil or climatic effects
● May not account for the diversity of agricultural management operations
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Univ. of Minnesota
Statistical ModelsStatistical Models● Linear or non-linear regression● Most useful at the field scale● Tendency to over- or under-
parameterize● Interpretation of causes and effects may
be problematic● Statistical relationships are not
necessarily consistent with underlying transport or fate mechanisms
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Univ. of Minnesota
The Universal Soil Loss The Universal Soil Loss EqtnEqtn (USLE)(USLE)
● A = R * K * L S * C * P– A is Estimated Soil Loss (tons/acre-yr) – Rainfall-Runoff Erosivity Factor (R) – Soil Erodibility Factor (K)– Slope Length and Steepness Factor (LS)– Cover Management Factor (C)– Supporting (Conservation) Practices Factor
(P)
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Univ. of Minnesota
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Univ. of Minnesota
Phosphorus Index Pathway Model Concept
Erosion(PP) c Soil P c
BMPsStructuresDelivery
RISK=
Rainfall Runoff(DP) c Soil P
Applied P c PracticeFactors RISK=
OverallRisk
Snowmelt Runoff(DP) c Biomass
Applied P c PracticeFactors RISK=
TransportMechanism
PhosphorusSource
ManagementEffects
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Univ. of Minnesota
Mechanistic ModelsMechanistic Models
● Attempt to describe underlying processes of transport and fate
● Designed for application at different scales
● Require more detailed input data than statistical models
● Differ in degree of empiricism used to describe underlying mechanisms
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Univ. of Minnesota
Mechanistic Model StrengthsMechanistic Model Strengths
● Can separate effects of point and non-point sources
● Can investigate impacts of changing climatic conditions
● Estimate both concentrations and loads (useful for setting TMDLs)
● Can identify impacts of alternative management strategies
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Univ. of Minnesota
Mechanistic Watershed Scale ModelsMechanistic Watershed Scale Models
● Hydrological Simulation Program Fortran (HSPF) – USGS and Stanford
● Soil and Water Assessment Tool (SWAT) - USDA-ARS
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Univ. of Minnesota
HSPFHSPF● Continuous rainfall hydrology, runoff and
water quality model linked to nationwide GIS databases
● Represents watershed as pervious and impervious areas, stream channels and reserviors
● Sediment loads based on rainfall detachment and wash off based on transport capacity and scour
● Phosphorus loads based on phosphate and organic forms using buildup and washoffcoefficients
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Univ. of Minnesota
HSPFHSPF● Strengths
– Widely used and accurate for daily and monthly flows
– Well suited for urban hydrology modeling– Accounts for overland transport as well as channel
and reservoir transport
● Weaknesses– Very difficult to calibrate– Does not represent agricultural management
practices explicitly– Doesn’t explicitly estimate gully or streambank
erosion
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Univ. of Minnesota
SWATSWAT● Continuous rainfall hydrology, runoff,
sediment, crop growth, nutrients, agricultural management model with channel and reservoir routing linked to nationwide GIS databases
● Sub-basins grouped based on climate, land use, soil, management, ponds, and channel
● Sediment loads based on Modified Universal Soil Loss Equation
● Phosphorus loads based on runoff partitioning and erosion loading functions
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Univ. of Minnesota
SWATSWAT● Strengths
– Ability to evaluate impacts of riparian, tillage, fertilizer and manure management practices on flow and water quality
– Widely used and accurate for monthly average flows
– Accounts for overland transport as well as channel and reservoir transport
– Accounts for groundwater and tile drain flow
● Weaknesses– Many calibration parameters– Doesn’t explicitly estimate gully or streambank
erosion
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Univ. of Minnesota
Model Calibration and ValidationModel Calibration and Validation
● Calibrate model using multiple years of monitoring data using measured data for input parameters wherever possible
● Need good match between model predictions and measured data for flow, sediment, phosphorus, nitrate, etc.
● Predicted contributions to flow from runoff, interflow, tile drainage must be reasonable
● Use independent data for validation
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Univ. of Minnesota
Modeling OutcomesModeling Outcomes
● Pollutant concentrations and loads at mouth of watershed
● Ability to identify sources of pollutant loads– Helps assess Waste Load Allocations (point sources) and
Load Allocations (non-point sources)● Ability to estimate load reductions with various
alternative interventions – Helps assess feasibility of attaining TMDL
● Ability to estimate changes in loads in response to climatic or landuse changes– Helps set reserve capacity
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Univ. of Minnesota
Modeling Time FrameModeling Time Frame● TMDL modeling involves several stages:
– Data collection– Modeling– Analysis– Outreach– Public participation– Administrative duties
● Time required increases with size of area● HSPF takes twice as long to run as SWAT
and requires more FTE than SWAT● Two years is probably the absolute minimum
needed to model portions of the upland areas in the Lake Pepin Basin
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Univ. of Minnesota
Model UncertaintyModel Uncertainty
● All models have uncertainty, these are characterized during calibration and validation using measures such as standard error, root mean square error, index of agreement, etc.
● Uncertainty is partially caused by climatic variability● The impacts of uncertainty on a TMDL can be
quantitatively estimated from model results● As uncertainty increases, the loads allowed for point
and non-point sources decrease● Uncertainty decreases as the complexity of the model
increases
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ConclusionsConclusions
● Modeling is an important component of integrated watershed assessment
● Ability to evaluate management alternatives depends on type and scale of model
● The type of model selected has a big impact on time required for TMDL evaluation and on model uncertainty