Abstract: Stimulated anaerobic dechlorination is generally considered a valuable step for the remediation of aquifers polluted with chlorinated ethenes (CEs). Correct simulation and prediction of this process in situ, however, require good knowledge of the associated biological reactions. The aim of this study was to evaluate the dechlorination reaction in an aquifer contaminated with trichloroethene (TCE) and its daughter products, discharging into the Zenne River. Different carbon sources were used in batch cultures and these were related to the dechlorination reaction, together with the monitored biomarkers. Appropriate kinetic formulations were assessed. Reductive dechlorination of TCE took place only when external carbon sources were added to microcosms, and occurred concomitant with a pronounced increase in the Dehalococcoides mccartyi cell count as determined by 16S rRNA gene-targeted qPCR. This indicates that native dechlorinating bacteria are present in the aquifer of the Zenne site and that the oligotrophic nature of the aquifer prevents a complete degradation to ethene. The type of carbon source, the cell number of D. mccartyi or the reductive dehalogenase genes, however, did not unequivocally explain the observed differences in degradation rates or the extent of dechlorination. Neither first-order, Michaelis-Menten nor Monod kinetics could perfectly simulate the dechlorination reactions in TCE spiked microcosms. A sensitivity analysis indicated that the inclusion of donor limitation would not significantly enhance the simulations without a clear process understanding. Results point to the role of the supporting microbial community but it remains to be verified how the complexity of the microbial (inter)actions should be represented in a model framework. (C) 2013 Elsevier B.V. All rights reserved.

Abstract: Finding a correspondence between soil hydraulic properties, such as saturated hydraulic conductivity (Ks) and apparent electrical conductivity (ECa) as an easily measurable parameter, may be a way forward to estimate the spatial distribution of hydraulic properties at the field scale. In this study, the spatial distributions of Ks, of soil ECa measured by a DUALEM-21S sensor and of soil physical properties were investigated in a sandy grassland. To predict field scale Ks, the statistical relationship between co-located soil Ks, and EMI-ECa was evaluated. Results demonstrated the large spatial variability of all studied properties with Ks being the most variable one (CV = 86.21%) followed by ECa (CV >= 53.77%). A significant negative correlation was found between In-transformed Ks and ECa (r = 0.83; P <= 0.01) at two depths of exploration (0-50 and 0-100 cm). This site specific relation between In Ks and ECa was used to predict saturated hydraulic conductivity over 0-50 cm depth for the whole field. The empirical relation was validated using an independent dataset of measured Ks. The statistical results demonstrate the robustness of this empirical relation with mean estimation error MEE = 0.46 (cm h(-1)), root-mean-square estimation errors RMSEE = 0.74 (cm h(-1)), coefficient of determination r(2) = 0.67 and coefficient of model efficiency Ce = 0.64. The relationship was then used to produce a detailed map of Ks for the whole field. The result will allow model predictions of spatially distributed water content in view of irrigation management. (C) 2016 Elsevier B.V. All rights reserved.

Abstract: A quasi 3D modelling approach was developed by integrating a crop growth (LINGRA-N) and a hydrological model (Hydrus-1D) to simulate and visualize water flow, soil-water storage, water stress and crop yield over a heterogeneous sandy field. We assessed computational efficiency and uncertainty with low to high-spatial resolution input factors (soil-hydraulic properties, soil-layer thickness and groundwater level) and evaluated four irrigation scenarios (no, current, optimized and triggered) to find the optimal and cost-effective irrigation scheduling. Numerical results showed that the simulation uncertainty was reduced when using the high-resolution information while a fast performance was maintained. The approach accurately determined the field scale irrigation requirements, taking into account spatial variations of input information. Optimal irrigation scheduling is obtained by triggered-irrigation resulting in saving up to similar to 300% water as compared to the current-irrigation, while yield increased similar to 1%. Overall, the approach can be useful to help decision makers and applicants in precision farming. (C) 2017 Published by Elsevier Ltd.

Abstract: Field water flow processes can be precisely delineated with proper sets of soil hydraulic properties derived from in situ and/or laboratory experiments. In this study we analyzed and compared soil hydraulic properties obtained by traditional laboratory experiments and inverse optimization tension infiltrometer data along the vertical direction within two typical Podzol profiles with sand texture in a potato field. The main goal was to identify proper sets of hydraulic parameters and to evaluate their relevance on hydrological model performance for irrigation management purposes. Tension disc infiltration experiments were carried out at four and five different depths for both profiles at consecutive negative pressure heads of 12, 6, 3 and 0.1 cm. At the same locations and depths undisturbed samples were taken to determine Mualem-van Genuchten (MVG) hydraulic parameters (theta(r), residual water content, theta(s), saturated water content, alpha and n, shape parameters and K-ls, saturated hydraulic conductivity) in the laboratory. Results demonstrated horizontal differences and vertical variability of hydraulic properties. The tension disc infiltration data fitted well in inverse modeling using Hydrus 2D/3D in combination with final water content at the end of the experiment, theta(f). Four MVG parameters (theta(s), alpha, n and field saturated hydraulic conductivity K-fs) were estimated (theta(r) set to zero), with estimated K-ls and alpha values being relatively similar to values from Wooding's solution which used as initial value and estimated theta(s) corresponded to (effective) field saturated water content, theta(f). The laboratory measurement of K-ls yielded 2-30 times higher values than the field method K-fs from top to subsoil layers, while there was a significant correlation between both K-s values (r = 0.75). We found significant differences of MVG parameters theta(s), n and alpha values between laboratory and field measurements, but again a significant correlation was observed between laboratory and field MVG parameters namely K-s, n, theta(s) (r >= 0.59). Assessment of the parameter relevance in 1-D model simulations, illustrated that the model over predicted and under predicted top soil-water content using laboratory and field experiments data sets respectively. The field MVG parameter data set resulted in better agreement to observed soil-water content as compared to the laboratory data set at nodes 10 and 20 cm. However, better simulation results were achieved using the laboratory data set at 30-60 cm depths. Results of our study do not confirm whether laboratory or field experiments data sets are most appropriate to predict soil water fluctuations in a complete soil profile, while field experiments are preferred in many studies. (C) 2016 Elsevier B.V. All rights reserved.