Identification of heterogeneous aquifer transmissivity using an AE-based method.
Determination of hydraulic head, H, as a function of spatial coordinates and time, in ground water flow is the basis for aquifer management and for prediction of contaminant transport. Several computer codes are available for this purpose. Spatial distribution of the transmissivity, T(x,y), is a required input to these codes. In most aquifers, T varies in an erratic manner, and it can be characterized statistically in terms of a few moments: the expected value, the variance, and the variogram. Knowledge of these moments, combined with a few measurements, permits one to estimate T at any point using geostatistical methods. In a review of transmissivity data from 19 unconsolidated aquifers, Hoeksema and Kitanidis (1985) identified two types of the logtransmissivity Y= ln(T) variations: correlated variations with variance sigma2Yc and correlation scale, I(Y), on the order of kilometers, and uncorrelated variations with variance sigma2Yn. Direct identification of the logtransmissivity variogram, Gamma(Y), from measurements is difficult because T data are generally scarce. However, many head measurements are commonly available. The aim of the paper is to introduce a methodology to identify the transmissivity variogram parameters (sigma2Yc, I(Y), and sigma2Yn) using head data in formations characterized by large logtransmissivity variance. The identification methodology uses a combination of precise numerical simulations (carried out using analytic element method) and a theoretical model. The main objective is to demonstrate the application of the methodology to a regional ground water flow in Eagle Valley basin in west-central Nevada for which abundant transmissivity and head measurements are available.
Janković
Igor
I
Department of Civil, Structural and Environmental Engineering, University of Buffalo, NY 14260-4400, USA. ijankovi@eng.buffalo.edu
Fiori
Aldo
A
Suribhatla
Raghavendra
R
Dagan
Gedeon
G
D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D005544:Forecasting; D055593:Geological Phenomena; D005844:Geology; D008956:Models, Chemical; D009505:Nevada; D014872:Water Movements; D014881:Water Supply