The incorporation of auxiliary data into ground-water flow parameter e
stimation is a challenging task which can ultimately result in a bette
r site characterization. In this study a maximum likelihood estimation
procedure has been applied to the joint identification of the paramet
ers of the aquifer transmissivity random field, and the parameters of
the linear regression between the logarithm of transmissivity and the
logarithm of the electrical transverse formation factor (TF), determin
ed from surface geoelectrical methods (Vertical Electrical Sounding or
V.E.S.). This approach is basically a co-kriging technique applied to
the transmissivity and transverse formation factor random fields, but
it avoids the independent estimation of the cross-covariances and the
secondary variable covariance. The procedure needs some direct well d
ata for transmissivity and a (usually larger) number of V.E.S. measure
ments which have to be in part at a distance from the well locations i
n order to provide useful information. The algorithm determines the ch
aracteristics of the local (site dependent) transmissivity-transverse
formation factor relationship and utilizes this auxiliary information
for a geostatistical transmissivity field estimation. The methodology
is tested on a real field scenario: a fractured aquifer impacted by la
ndfill leachate contamination. The use of the formation factor in plac
e of the raw resistivity of the subsoil layers accounts for possible e
ffects of clay and contaminant concentration on pore-water resistivity
. The information provided by the V.E.S. can add, to some extent, to t
he understanding of the aquifer characteristics and vulnerability. How
ever, tbe specificity of each site has to be fully understood for an e
ffective application of the present procedure. It seems unlikely that
geoelectric data can differentiate between transmissivity values diffe
ring by less than two or three orders of magnitude.