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.

需要将在实验室或野外条件下本地测量的准点水力特性（K）放大到块比例K张量，以用于流量模拟器。升级后的模型还需要根据液压头的观测值进行校准。校准必须保留张量分量之间的空间协方差，互协方差和非线性关系。我们应用了一种新的放大方法，该方法可以计算和建模块K张量分量之间的协方差。我们使用渐进变形方法将模拟的K张量场校准为测得的头部数据。我们的方法结合了一个新的双变量变换，该变换保留了K之间的非线性关系。张量组件。校准实现的集合可以量化地下水流模型的不确定性。在定义供水井捕获区域的测试用例上与PEST的比较表明，与PEST相比，我们的方法对测得的压头进行了更好的校准，提供了更逼真的K张量，并导致了更大的捕获区域。