|Van Genuchten, Martinus|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/9/2005
Publication Date: 8/16/2005
Citation: Pachepsky, Y.A., Guber, A.K., Van Genuchten, M.T., Nicholson, T.J., Cady, R.E., Simunek, J., Gish, T.J., Daughtry, C.S., Jackues, D. 2005. Model abstraction in hydrologic modeling [abstract]. Annual Public Meeting of the Interagency Steering Committee on Multimedia Environmental Models. p. 7. 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 MA explicitly deals with uncertainties in model structure and in model parameter sources. It has been researched in various knowledge fields that actively use modeling. We present (a) the taxonomy of model abstraction techniques being applied in subsurface hydrologic modeling, (b) the systematic and comprehensive procedure of the MA implementation including (1) defining the context of the modeling problem, (2) defining the need for the model abstraction, (3) selecting applicable MA techniques, (4) identifying MA directions that may give substantial gain, and (5) simplifying the base model in each direction. The need in MA may stem from (a) difficulties to obtain a reliable calibration of the base model, (b) the error propagation making the key outputs uncertain, (c) inexplicable results from the base model, (d) excessive resource requirements of the base model, (e) the intent to include the base model in a larger multimedia environmental model, (f) the request to make the modeling process more transparent and tractable, and (g) the need to justify the use a simple model when a complex model is available Two examples will illustrate the MA application in field-scale simulations of water flow in variably saturated soils and sediments. The MA (a) can result in the improved reliability of modeling results, (c) make the data use more efficient, (c) 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 (d) enhance 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.