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ARS Home » Research » Publications at this Location » Publication #165468
Title: MODEL ABSTRACTION TECHNIQUES: AN OVERIVEW OF APPLICATIONS IN AGRICULTURAL CONTAMINANT

Author
item Pachepsky, Yakov
item Van Genuchten, Martinus
item GUBER, ANDREY - ARS,VISITING SCIENTIST
item SIMUNEK, JIRI - U.OF RIVERSIDE, CA
item SCHAAP, MARCEL - USDA,ARS,RIVERSIDE,CA

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/1/2004
Publication Date: 9/25/2004
Citation: Pachepsky, Y.A., Van Genuchten, M.T., Guber, A., Simunek, J., Schaap, M. 2004. Model abstraction techniques: an overivew of applications in agricultural contaminant. [Meeting Abstract]. European Society of Ecological Modeling Conference, September 27th- October 1, 2004, Bled, Slovenia. Paper No. 66, CD-ROM.

Interpretive Summary:

Technical Abstract: Model abstraction is a methodology for reducing the complexity of a simulation model while maintaining the validity of the simulation results with respect to the question that the simulation is being used to address. The need for model abstraction has been recognised in simulations of complex systems that show that increased level of detail does not necessarily imply increased accuracy of simulation results, but usually increases computational complexity and/or data collection burden, and may make simulation results more difficult to interpret. Model abstraction explicitly deals with uncertainties in model structure. We introduce and characterize main model abstraction techniques. An example of MA application will be presented that shows that (a) model abstractions enable risk assessments to be run and analyzed with much quicker turnaround, with the potential for allowing further analyses of problem sensitivity and uncertainty, and (b) model abstraction enhances communication as simplifications that result from appropriate model abstractions may make the description of the problem more easily relayed to and understandable by others, including decision-makers and the public. It is often imperative to explicitly acknowledge the abstraction strategy used and its inherent biases, so that the modeling process is transparent and tractable.