HR: 0800h
AN: NG41A-0423    [Abstracts]
TI: Geostatistical Estimations of Regional Hydraulic Conductivity Fields
AU: * Patriarche, D
EM: delfpat@umich.edu
AF: University of Michigan, Department of Geological Sciences, 2534 C. C. Little Building 425 East University Avenue, Ann Arbor, MI 48109-1063 United States
AU: Castro, M C
EM: mccastro@umich.edu
AF: University of Michigan, Department of Geological Sciences, 2534 C. C. Little Building 425 East University Avenue, Ann Arbor, MI 48109-1063 United States
AU: Goovaerts, P
EM: goovaerts@biomedware.com
AF: Biomedware, 516 North State Street, Ann Arbor, MI 48104 United States
AB: Direct and indirect measurements of hydraulic conductivity ({\it K}) are commonly performed, providing information on the magnitude of this parameter at the local scale (tens of centimeters to hundreds of meters) and at shallow depths. By contrast, field information on hydraulic conductivities at regional scales of tens to hundreds of kilometers and at greater depths is relatively scarce. Geostatistical methods allow for sparsely sampled observations of a variable (primary information) to be complemented by a more densely sampled secondary attribute. Geostatistical estimations of the hydraulic conductivity field in the Carrizo aquifer, a major groundwater flow system extending along Texas, are performed using available primary (e.g., transmissivity, hydraulic conductivity) and secondary (specific capacity) information, for depths up to 2.2 km, and over three regional domains of increasing extent: 1) the domain corresponding to a three-dimensional groundwater flow model previously built (model domain); 2) the area corresponding to the ten counties encompassing the model domain (County domain), and; 3) the full extension of the Carrizo aquifer within Texas (Texas domain). Two different approaches are used: 1) an indirect approach are transmissivity ({\it T}) is estimated first and ({\it K}) is retrieved through division of the {\it T} estimate by the screening length of the wells, and; 2) a direct approach where {\it K} data are kriged directly. Prediction performances of the tested geostatistical procedures (kriging combined with linear regression, kriging with known local means, kriging of residuals, and cokriging) are evaluated through cross validation for both log-transformed variables and back-transformed ones. For the indirect approach, kriging of log {\it T} residuals yields the best estimates for both log-transformed and back-transformed variables in the model domain. For larger regional scales (County and Texas domains), cokriging performs generally better than univariate kriging procedures when estimating both (log {\it T})$^{*}$ and {\it T}$^{*}$. Among univariate procedures using the direct approach, the best prediction performances are obtained using simple kriging of log {\it K} with known local means. Cross validation also indicates that the indirect approach leads to smaller prediction errors than the direct approach, which is likely due to fewer available {\it K} primary data as well as a weaker correlation between primary and secondary attributes in the direct case. Although all procedures used log-transformed variables and incorporate secondary information derived from specific capacity data, none of the investigated techniques provides systematically better predictions for all scales, which stresses the importance of using cross validation to compare performances of alternative approaches and assess the unbiasedness of the back-transform procedure. Overall, estimation of the hydraulic conductivity field at such large regional scales through the tested geostatistical methods appears to be difficult due to both scarcity of sampling in the deeper portions of the formation ($>$ 1 km) and preferential emplacement of well screens in the most productive portions of the aquifer. For example, in the deepest portions of the aquifer in the model domain, the estimated hydraulic conductivity field is obtained by extrapolation and gives origin to unrealistically high hydraulic conductivity values.
DE: 3200 MATHEMATICAL GEOPHYSICS (New field)
DE: 3210 Modeling
DE: 3220 Nonlinear dynamics
DE: 3299 General or miscellaneous
DE: 1829 Groundwater hydrology
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting