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
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
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
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
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
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
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
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)
Univ. of Minnesota
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
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
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
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
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
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
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
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
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
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
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
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
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