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Title: MODEL ABSTRACTION TO ASSESS UNCERTAINTY IN FLOW AND TRANSPORT MODELING

Authors
item Pachepsky, Yakov
item Guber, A - ARS, VISITING SCIENTIST
item Nicholson, T - US NRC,WASHINGTON DC
item Simunek, J - U. OF CA, RIVERSIDE,CA
item Van Genuchten, Martinus
item Cady, R - US NRC,WASHINGTON DC
item Jacques, D - SCK-CEN,BELGIUM
item Schaap, M - USDA-ARS,RIVERSIDE,CA

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: September 13, 2004
Publication Date: December 14, 2004
Citation: Pachepsky, Y.A., Guber, A.K., Nicholson, T.J., Simunek, J., Van Genuchten, M.T., Cady, R.E., Jacques, D., Schaap, M.G. 2004. Model abstraction to assess uncertainty in flow and transport modeling[Abstract]. American Geophysical Union. p.H14A-06.

Technical Abstract: Model abstraction (MA) 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 MA is recognized in simulations of complex systems where increased level of detail does not necessarily increase accuracy, but increases computational complexity, data collection burden, and difficulty to interpret results. We present a systematic classification and compendium of MA techniques for flow and transport modeling in soils. MA techniques were applied to soil water flow through a densely-instrumented soil profile of an experimental trench using: (a) Richards equation- and water budget-based models; (b) inverse modeling, laboratory measurements and pedotransfer functions to estimate parameters; (c) layered vs. homogeneous soil conceptual model. MA showed different efficiency when applied to soil water contents than to water fluxes. The water budget model was comparable to the mechanistic model with respect to water fluxes at coarse time scales. Measured hydraulic properties provided no advantage compared to pedotransfer functions. A spectrum of pedotransfer functions characterized uncertainty in hydraulic properties. One MA technique used neural networks to mimic simulated soil water flow. The MA application was useful both in understanding the flow system, and in justifying simplifications of its conceptualization and characterization.

   
 
 
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