Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content

Abstract

Soil water content (SWC) has a vital role in a variety of hydrological processes such as infiltration, runoff and erosion. Mapping the spatial pattern of SWC is then essential for appropriate addressing of these processes. Geostatistics is often used to characterize the spatial variability of SWC. This information may be used for estimating SWC e.g., by ordinary kriging (OK) or modeling location-specific uncertainty (local uncertainty) of the estimates by indicator kriging (IK). Kriging-based algorithms however smooth out the details and are incapable of detecting multi-location uncertainty (spatial uncertainty) of SWC estimates. Sequential Gaussian simulation (sGs) can model the spatial uncertainty through generation of several equally probable stochastic realizations. In this study sGs is used to map SWC spatial distribution and to provide a quantitative measure of its spatial uncertainty in particular. The SWC measurements were performed on 157 soil samples taken from an 18 ha erosion experiment field in Lower Austria. The results show that the spatial pattern of SWC is well recognized using the sGs as the simulated models reproduce the sample statistics including histogram and semivariogram model reasonably well. The difference among realizations is used to provide a quantitative measure of spatial uncertainty of SWC estimates. Knowledge of spatial uncertainty is helpful to evaluate the delineation of vulnerable areas to erosion.