A method for modeling the effects of climate and land use changes on erosion and sustainability of soil in a Mediterranean watershed (Languedoc, France).

Global climate and land use changes could strongly affect soil erosion and the capability of soils to sustain agriculture and in turn impact regional or global food security. The objective of our study was to develop a method to assess soil sustainability to erosion under changes in land use and climate. The method was applied in a typical mixed Mediterranean landscape in a wine-growing watershed (75 km(2)) within the Languedoc region (La Peyne, France) for two periods: a first period with the current climate and land use and a second period with the climate and land use scenarios at the end of the twenty-first century. The Intergovernmental Panel on Climate Change A1B future rainfall scenarios from the Météo France General circulation model was coupled with four contrasting land use change scenarios that were designed using a spatially-explicit land use change model. Mean annual erosion rate was estimated with an expert-based soil erosion model. Soil life expectancy was assessed using soil depth. Soil erosion rate and soil life expectancy were combined into a sustainability index. The median simulated soil erosion rate for the current period was 3.5 t/ha/year and the soil life expectancy was 273 years, showing a low sustainability of soils. For the future period with the same land use distribution, the median simulated soil erosion rate was 4.2 t/ha/year and the soil life expectancy was 249 years. The results show that soil erosion rate and soil life expectancy are more sensitive to changes in land use than to changes in precipitation. Among the scenarios tested, institution of a mandatory grass cover in vineyards seems to be an efficient means of significantly improving soil sustainability, both in terms of decreased soil erosion rates and increased soil life expectancies.

Analyzing the spatial distribution of PCB concentrations in soils using below-quantification limit data.

Polychlorinated biphenyls (PCBs) are highly toxic environmental pollutants that can accumulate in soils. We consider the problem of explaining and mapping the spatial distribution of PCBs using a spatial data set of 105 PCB-187 measurements from a region in the north of France. A large proportion of our data (35%) fell below a quantification limit (QL), meaning that their concentrations could not be determined to a sufficient degree of precision. Where a measurement fell below this QL, the inequality information was all that we were presented with. In this work, we demonstrate a full geostatistical analysis-bringing together the various components, including model selection, cross-validation, and mapping-using censored data to represent the uncertainty that results from below-QL observations. We implement a Monte Carlo maximum likelihood approach to estimate the geostatistical model parameters. To select the best set of explanatory variables for explaining and mapping the spatial distribution of PCB-187 concentrations, we apply the Akaike Information Criterion (AIC). The AIC provides a trade-off between the goodness-of-fit of a model and its complexity (i.e., the number of covariates). We then use the best set of explanatory variables to help interpolate the measurements via a Bayesian approach, and produce maps of the predictions. We calculate predictions of the probability of exceeding a concentration threshold, above which the land could be considered as contaminated. The work demonstrates some differences between approaches based on censored data and on imputed data (in which the below-QL data are replaced by a value of half of the QL). Cross-validation results demonstrate better predictions based on the censored data approach, and we should therefore have confidence in the information provided by predictions from this method.