HR: 0800h
AN: H11C-0309    [Abstracts]
TI: Concurrent use of multiple observation types: impact on ground-water model parameter estimates
AU: * Voss, C I
EM: cvoss@usgs.gov
AF: U. S. Geological Survey, 431 National Center, Reston, VA 20192 United States
AU: Sanz, E
EM: esanz@ija.csic.es
AF: CSIC, Lluis Sole i Sabaris, Barcelona, 08028 Spain
AU: Nordqvist, R
EM: Rune.Nordqvist@geosigma.se
AF: Geosigma, Box 894, Uppsala, 75108 Sweden
AB: For inverse modeling, simultaneous use of different types of field observation data can improve ground-water model structure, features, and parameter values. However, such simultaneous use is neither always successful nor always desirable. With regard to coupled flow and transport modeling, there can be significant advantages to concurrent use of both hydraulic head (or pressure) and concentration (or temperature) data for model refinement and parameter estimation. Transport always depends on flow, thus, for both constant- and variable-density flow, measurements of concentration can be used to help estimate classical flow-model parameters such as hydraulic conductivity. When flow processes depend on transport processes, such as in variable-density flow, measurements of pressure can provide information to help estimate transport parameters such as dispersivity that are normally assumed to be estimatable only from measurements of concentration. In some cases, however, use of atypical observation types can give unexpected results. For example, in heterogeneous aquifers, drawdown propagates diffusively through all heterogeneities in the fabric, whereas plumes eventually select the most conductive-connected propagation paths, in effect sub-sampling the fabric. Parameter estimation separately using head and concentration observations thus gives two different sets of estimates for a homogeneous effective model of the same system. Because models never completely represent aquifer heterogeneity in a field area, this dilemma must be recognized in order to employ inverse modeling in a meaningful manner when concurrently using head and concentration observations. Indeed, different relative weightings of observation data types in an inverse model can result in disturbingly different estimates of parameters for the same model. There is no "correct" weighting to balance the influence of different observation types on the parameter estimation. Selection of such weights should depend on the practitioner's needs and insights. Further, selection of the error model for observations (e.g. normally- or log-normally-distributed errors) can imply significantly different parameter estimates when using the same observations. The differences implied by these error models can equivalently be achieved via appropriate selection of weights on the observations in this case. Moreover, the most effective observation networks for parameter estimation can be very different for the different error models, suggesting that the error model must be known a priori for network design.
DE: 1829 Groundwater hydrology
DE: 1832 Groundwater transport
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting