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Abstract: Soil data acquisition and assessment are crucial phases in the evaluation of soil degradation scenarios. To overcome the lack of field data, flexible sampling approaches can be used to complement conventional soil sampling. For the assessment of soil quality, it is necessary to integrate different soil support data and to provide a coherent spatial characterization of soil properties. This study proposes a new model to combine soil data from two different supports: “point” data, which refers to the concentration measured in the topsoil layer, and “bulk” data, which refers to the concentration measured for the whole soil depth sampled. The method developed uses a geostatistical co-simulation algorithm based on the experimental bi-distribution between both types of soil supports to compute co-simulated values. This new approach was applied to assess Soil Organic Carbon (SOC) availability in the topsoil. The results were used to identify critical areas in the Left Margin of the Guadiana River; an area in the South of Portugal with a high susceptibility to desertification. [ABSTRACT FROM AUTHOR]

The practice of stochastic simulation for different environmental and earth sciences applications creates new theoretical problems that motivate the improvement of existing algorithms. In this context, we present the implementation of a new version of the direct sequential co-simulation (Co-DSS) algorithm. This new approach, titled Co-DSS with joint probability distributions, intends to solve the problem of mismatch between co-simulation results and experimental data, i.e. when the final biplot of simulated values does not respect the experimental relation known for the original data values. This situation occurs mostly in the beginning of the simulation process. To solve this issue, the new co-simulation algorithm, applied to a pair of covariates Z1( x) and Z2( x), proposes to resample Z2( x) from the joint distribution F( z1, z2) or, more precisely, from the conditional distribution of Z2( x0), at a location x0, given the previously simulated value $z_{1}^{(l)}(x_{0})$ ( $F(Z_{2}|Z_{1}=z_{1}^{(l)}(x_{0})$ ). The work developed demonstrates that Co-DSS with joint probability distributions reproduces the experimental bivariate cdf and, consequently, the conditional distributions, even when the correlation coefficient between the covariates is low. [ABSTRACT FROM AUTHOR]