Modeling Nutrient and Sediment Losses from Cropland D. J. Mulla Dept. Soil, Water, & Climate University of Minnesota

Modeling Nutrient and Sediment Losses from Cropland D. J. Mulla Dept. Soil, Water, & Climate University of Minnesota

Watershed 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

Watershed 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

Model 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

Simulation Models Export coefficient models Statistical models Mechanistic watershed scale models HSPF - EPA SWAT, AGNPS - USDA ADAPT -, Ohio State Univ. (DRAINMOD + GLEAMS + Routing) Mechanistic field scale models EPIC, RZWQM, DRAINMOD, GLEAMS

Export 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

Statistical Models Linear or non-linear regression Most useful at the field scale Tendency to over- or underparameterize Interpretation of causes and effects may be problematic Statistical relationships are not necessarily consistent with underlying transport or fate mechanisms

The Universal Soil Loss Eqtn (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)

Transport Mechanism Erosion (PP) Phosphorus Source c Soil P c Management Effects BMPs Structures Delivery = RISK Rainfall Runoff (DP) c Soil P Applied P c Practice Factors = RISK Snowmelt Runoff c (DP) Biomass Applied P c Practice Factors = RISK Phosphorus Index Pathway Model Concept Overall Risk

Mechanistic 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

Mechanistic Model Strengths Can separate effects of point and nonpoint sources Can investigate impacts of changing climatic conditions Estimate both concentrations and loads (useful for setting TMDLs) Can identify impacts of alternative management strategies

Mechanistic Watershed Scale Models Hydrological Simulation Program Fortran (HSPF) USGS and Stanford Soil and Water Assessment Tool (SWAT) - USDA-ARS

HSPF 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 washoff coefficients

Strengths HSPF 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

SWAT 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

Strengths SWAT 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

Model 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

Modeling 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

Modeling 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

Model 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

Conclusions 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