Assessing the spatial variability of saturated soil hydraulic conductivity at the watershed scale using the sequential Gaussian co-simulation method

Abstract

Accurate spatial characterization of saturated soil hydraulic conductivity (KSat) is vital for modeling hydrological processes. An effective representation of KSat by field sampling is very difficult on a watershed scale, due the heterogeneity of landscape characteristics for wide land surfaces. To improve the estimation of spatial distributions of KSat at this scale, Sequential Gaussian Simulation (SGS) and Sequential Gaussian Co-Simulation (SGCS) algorithms were applied. The study was conducted in a 70 ha watershed, with 178 sampling points. In addition to KSat the database is composed of Bulk Density (BD), Total Porosity (TP), Macroporosity (Mac), Microporosity (Mic) and Organic Carbon (OC). The Land Use (LU) was also quantified as a regionalized variable. A principal component analysis generated additional two regionalized variables (PC1 and PC2). The simulations were evaluated by faithfully reproducing the stochastic and spatial characteristics of the KSat original data. To estimate the KSat, a univariate SGS was run first. The reproduction of the KSat histograms was satisfactory, while the semivariograms of the simulated fields underestimated the sill. The SGCS was run using five different secondary variables: BD; Mac; LU; PC1 and PC2. All SGCS satisfactorily represented the original KSat histogram, with a slight overestimation for some simulated fields. The semivariogram was very well reproduced for SGCS, being an indication that the process was quite efficient for all secondary variables. The smallest uncertainties were identified for the SGCS that used BD and Mac as secondary variables while the greatest uncertainties were associated with the SGS. The results of this study demonstrate that soil hydrological attributes as BD and Mac are a reliable auxiliary dataset to improve the spatial assessment of KSat by SGCS at watershed scale.