We discuss two recent methods of characterizing the spatial variability of a random (natural) log transmissivity field on the basis of observed space-time variations in hydraulic head: a graphical stochastic type-curve method (Neuman et al., 2004, 2007) and a geostatistical method of inverting stochastic mean flow equations (Hernandez et al., 2003, 2006). While both methods allow estimating the unconditional variance and integral (correlation) scale of log transmissivities, geostatistical inversion is computationally more intensive but provides also tomographic images of how log transmissivity estimates and their variance vary in space. We apply the two approaches to synthetic scenarios and to measured late time (quasisteady state) drawdowns from a sequence of transient pumping tests in an unconfined aquifer near Tübingen, Germany.
Characterizing the spatial variability of transmissivity using stochastic type-curves and numerical inverse analyses of data from a sequence of pumping tests
RIVA, MONICA;GUADAGNINI, ALBERTO;
2008-01-01
Abstract
We discuss two recent methods of characterizing the spatial variability of a random (natural) log transmissivity field on the basis of observed space-time variations in hydraulic head: a graphical stochastic type-curve method (Neuman et al., 2004, 2007) and a geostatistical method of inverting stochastic mean flow equations (Hernandez et al., 2003, 2006). While both methods allow estimating the unconditional variance and integral (correlation) scale of log transmissivities, geostatistical inversion is computationally more intensive but provides also tomographic images of how log transmissivity estimates and their variance vary in space. We apply the two approaches to synthetic scenarios and to measured late time (quasisteady state) drawdowns from a sequence of transient pumping tests in an unconfined aquifer near Tübingen, Germany.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.