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Title: MODEL ABSTRACTION APPROACH IN SOIL WATER BALANCE SIMULATIONS

Author
item Pachepsky, Yakov
item GUBER, ANDREY - ARS/BA VISITING SCIENTIST
item JACQUES, D. - SCK-CEN, BELGIUM
item CADY, R. - US NRC, WASHINGTON DC
item NICHOLSON, T. - US NRC, WASHINGTON DC
item SIMUNEK, J - U. OF CAL., RIVERSIDE,CA
item SCHAAP, M. - USDA/ARS,RIVERSIDE,CA
item Van Genuchten, Martinus

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/19/2004
Publication Date: 10/31/2004
Citation: Pachepsky, Y.A., Guber, A.K., Jacques, D., Cady, R.E., Nicholson, T.J., Simunek, J., Schaap, M.G., Van Genuchten, M.T. 2004. Model abstraction approach in soil water balance simulations. [Abstract]. ASA-CSSA-SSSA Annual Meeting,October 31 - November 4, 2004. Seattle, Washington. p.78.

Interpretive Summary:

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 of result interpretation. We present a compendium of MA techniques and an example of the MA application to soil water flow that was developed for a densely instrumented soil trench using (a) Richards equation- and water budget-based models, (b) inverse modeling, laboratory measurements and pedotransfer functions to estimate parameters, and (c) layered vs. homogeneous soil. MA showed different efficiency when applied to soil water contents and to water fluxes. The water budget model worked not worse than the mechanistic model with respect to water fluxes at coarse time scales. Measured hydraulic properties did not provide an advantage compared with pedotransfer functions. A spectrum of pedotransfer functions gave a good description of uncertainty in hydraulic properties. Using neural networks to mimic simulated water flow was a promising MA direction. MA was useful both in understanding the system and in justifying simplification of its description.