Stochastic correlated hydraulic conductivity tensor calibration using gradual deformation.

• Stochastic point conductivities are upscaled to define block K-tensor components. • A new bivariate Gaussian transformation preserves tensor components' correlations. • Linear coregionalisation model is used for simulation of equiprobable tensor fields. • Gradual deformation method (GDM) is used to calibrate fields to head observations. • Capture zones obtained with PEST and calibrated tensor fields are compared. Quasi-point hydraulic properties (K) measured locally under laboratory or field conditions need to be upscaled to block-scale K -tensors for use in flow simulators. The upscaled model also needs to be calibrated to hydraulic head observations. The calibration must preserve spatial covariance, cross-covariance and non-linear relations between tensor components. We apply a new upscaling method that allows to compute and model the covariance between block K -tensor components. We use a gradual deformation method for calibration of simulated K -tensor fields to measured head data. Our method incorporates a new bivariate transform that preserves the non-linear relations between K -tensor components. The ensemble of calibrated realizations allows quantification of uncertainty of groundwater flow models. A comparison with PEST on a test case defining capture zones for water supply wells shows that our method calibrates better to measured heads than PEST, provides more realistic K -tensors and results in larger capture zones. [ABSTRACT FROM AUTHOR]