Improving kriging of groundwater level data using nonlinear normalizing transformations—a field applicationImproving kriging of groundwater level data using nonlinear normalizing transformations—a field applicationAmélioration du krigeage de données piézométriques à l'aide de transformations normalisatrices non-linéaires. Application sur le terrain Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-10-202012enfrThe present research study investigates the application of nonlinear normalizing data transformations in conjunction with ordinary kriging (OK) for the accurate prediction of groundwater level spatial variability in a sparsely-gauged basin. We investigate three established normalizing methods, Gaussian anamorphosis, trans-Gaussian kriging and the Box-Cox method to improve the estimation accuracy. The first two are applied for the first time to groundwater level data. All three methods improve the mean absolute prediction error compared to the application of OK to the non-transformed data. In addition, a modified Box-Cox transformation is proposed and applied to normalize the hydraulic heads. The modified Box-Cox transformation in conjunction with OK is found to be the optimal spatial model based on leave-one-out cross-validation. The recently established Spartan semivariogram family provides the optimal model fit to the transformed data. Finally, we present maps of the groundwater level and the kriging variance based on the optimal spatial model.Δημοσίευση σε επιστημονικό περιοδικό http://creativecommons.org/licenses/by/4.0/Hydrological Sciences Journal5771404-1419 Varouchakis Emmanouil Βαρουχακης Εμμανουηλ Christopoulos Dionysios Χριστοπουλος Διονυσιος Karatzas Giorgos Καρατζας Γιωργος Taylor & Francis Kriging Gaussian anamorphosis Trans-Gaussian kriging Box-Cox transformation Modified Box-Cox Spartan semivariogram Ungauged basin Groundwater level