Morning Session: Principles of Modeling
This session will discuss the differences and similarities of empirical versus explanatory modeling. Basic modeling terminology and concepts will be covered, including how mathematical models are based on conceptual models and evolve over time as additional knowledge is obtained and testing of the model occurs.

• What is a model?
• Kinds of models, including deterministic vs. stochastic, and deterministic/explanatory vs. empirical
• Model formats
  • Old fashioned FORTRAN card format input files along with their pitfalls (e.g. PRZM and EXAMS without shell)
  • Graphical User Interface models and their pitfalls (e.g. PE5)
• Domain of applicability along with its analogy with statistical regression; interpolation vs. extrapolation
• Basic discussion of PRZM and EXAMS models

Afternoon Session: How to Identify Appropriate Environmental Modeling Parameters
This session will review environmental fate processes relevant to surface water modeling. These concepts will be reinforced with an exercise in which the students examine a set of field data to identify the appropriate input parameters (e.g. soil and weather). The identified parameters will be entered into a PRZM exercise, and discussions will include the hazards of over-extrapolating results from developed scenarios to other geographical areas where the environmental conditions are different.

• Weather
• Hydrology
  • Stream flow velocity
  • Sedimentation
  • Rainfall
  • Surface water runoff and infiltration
  • Runoff amounts and intensity
  • Hydrologic Soil Groups (HSG)
  • SCS Runoff curve numbers
  • Antecedent moisture
• Sediment transport - Universal Soil Loss Equation
• Drift

Morning Session: How to Identify Appropriate Pesticide Modeling Parameters
This session will discuss pesticide environmental fate processes and application methods. These concepts will be reinforced with an exercise in which the students examine a set of data to identify the appropriate environmental fate and application method input parameters using PE5.

• Chemical fate
• Adsorption
• Degradation
  • First order degradation
  • Kinds of degradation (e.g. photodegradation, anaerobic/aerobic degradation, hydrolysis, volatilization)
• Pesticide application methodology relevant to aquatic systems
  • Terrestrial applications: incorporation, dormant orchard applications, commercial structural applications
  • Water applications: drift issues and aquatic pesticides

Afternoon Session: How to Work with Modeling Results
This session will discuss the concept of uncertainty associated with model input parameters (and thus model output results). This session will also address sensitivity analysis, model calibration, and model validation, as well as a discussion on Mote Carlo techniques and probabilistic model output methods to assess model uncertainty.

• Uncertainty in modeling: Biological variability, sampling variability, input parameter selection effect on model results
• Model Input Parameter Sensitivity
• Calibration to field data
• Model validation
  • What is validation?
  • Statistics relevant to validation
    • Regression and other techniques
    • Statistical concepts relevant to stochastic output
    • Cumulative sampling distributions
    • Parametric vs. empirical
    • Examples of stochastic output
    • Concept of lumped parameters

Morning Session: How to Review a Modeling Report
This session will focus on reviewing a modeling report for submission purposes. The class will be guided through what to think about when reviewing a model report.

• Is the employed model appropriate for the current problem?
• Is the provided model input sufficient for reproduction of the modeling study?
• Is the currently accepted version of the employed model used in the modeling study? If a previous version is used, what implications (if any) are likely to result from use of an older version?
• Is the report internally consistent?
• Is the model validated? Is the employed version of the model validated?
• Was the model calibrated to this situation?
• Is the author seeming to hide something?
• Is author biased or ego involved?
• Does the author have reason to shade results? If so, is there any indication they have done so?
• Is modeling within the appropriate domain for the model? (e.g. time / space)
• Are results consistent with other known empirical data and physical properties?

Afternoon Session - Scale Considerations
This session will expand the modeling to include issues about scale, both spatial and temporal. The PRZM/EXAMS modeling exercise will be expanded to include simulating 30 weather years to assess the effect of changes in results over time. Discussion of daily time step versus smaller increments will be conducted. Other surface water models will be discussed/demonstrated in this session including GLEAMS, RZWQM, SWAT, RICEWQ, and SWMM.

• Spatial scale (e.g. edge of field vs. watershed)
• Temporal scale
  • Episodic vs. daily mean
  • Dictated in part by model's minimum time step
    

Morning Session: Model Comparisons and Wrap Up
This session will include comparison of the models discussed from the perspective of:

• Input requirements
• Internal algorithms
• Computational requirements
• Published validation reports
• Individual model strengths and weaknesses
• Model assumptions
• Intended usage
• Using multiple models in a similar manner to help address model uncertainty

The final exercises will include actively running some of the models and then comparing the results. The students will be encouraged to examine whether the differences are due too differences in the internal algorithms of the model, differences in model assumptions, availability of necessary model information, or differences in sensitivity of input parameters between the models.