Soil Variability and Biogeochemical Fluxes: Toward a Better Understanding of Soil Processes at the Land Surface

Core Ideas Pattern recognition techniques can help explain biogeochemical flux variability. Dynamic factors and their impact on biogeochemical flux variability need better identification. Controls on biogeochemical fluxes are time and space scale dependent. Soil biogeochemical fluxes in the vadose zone are characterized by a large degree of variability in space and time. This fact leads to the need for the development and application of appropriate methodologies to better understand the high nonlinearity and complex feedback mechanisms responsible for such fluxes. In this sense, there still exists a lack of knowledge in topics such as the scale dependence of the spatial and temporal variability of the controls on soil moisture and biodegradation rates and the dynamic behavior of flow and transport model parameter, and its association with the presence of roots. Knowledge of the variability of biogeochemical fluxes is needed for assorted applications ranging from natural hazards and environmental pollution risk assessment to agricultural production and water resources management. The contributions to this special section epitomize the ongoing effort toward the characterization, quantification, modeling, and understanding of biogeochemical fluxes in the vadose zone at several spatial and temporal scales. The main progress has been the identification of different controls on soil moisture and biodegradation rates depending on the scale of the study as well as the important dependence of the spatial and temporal variability of biogeochemical fluxes on dynamic properties such as vegetation and weather variables

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data