Modeling the impact of microbial activity on redox dynamics in porous media

The present study investigates the interaction between microbial growth and activity and the redox dynamics in natural porous media. The impact the transport regime has on this interaction is also addressed. Expressions for microbial growth are incorporated into a geochemical reaction network linking redox reaction rates to the activity of the microorganisms. A flexible simulation environment, the Biogeochemical Reaction Network Simulator (BRNS) is used for this purpose. Two reactive transport applications relevant to fields of contaminant hydrology and early diagenesis are simulated with the BRNS. Model results are evaluated based on a comparison with comprehensive datasets on the biodegradation of lactate in a sand column experiment and on the distribution of redox-sensitive chemical species in marine sediments of the Skagerrak, Denmark. It is shown that, despite quite different transport regimes, the geomicrobiological model performs equally well in the reproduction of measured chemical species distribution for both applications. This result emphasizes the broad applicability of the proposed approach. Our simulations support that the competitive behavior between various microbial groups is a process controlling the development of redox stratified environments. Furthermore, it is also shown that the transport regime is a key controlling factor for the degree of spatial correlation between microbial biomass distributions and redox reaction rates. Although all our simulations yield a pronounced stratification of the redox processes in the system, the biomass distribution is related to the associated reaction rates only in case of the advection controlled column experiment. In the early diagenetic application, mixing due to bioturbation is the dominant transport process for particulate matter, hence leading to fairly homogeneous distribution of bacterial biomasses which are unrelated to the spatial distribution of redox reaction rates. This homogeneous biomass distribution combined with the 1G carbon degradation model approach might explain why the steady state concentration profiles in such systems can be reproduced by diagenetic models without explicit representation of microbial growth.     

Correction to: Future changes in Atlantic hurricanes with the rotated-stretched ARPEGE-Climat at very high resolution

Climate Dynamics - The original article can be found online.     

An adaptive finite element model for wellbore breakout simulation

A finite element formulation is proposed and implemented for analysing the stability of excavated wells using the DiMaggio-Sandler constitutive elastoplastic model with a typical carbonate reservoir configuration. The quality of the finite element approximation is ensured by applying smooth curved elements adapted to the wellbore geometry, and h − p adaptive finite element meshes in the plastic zone. General purpose procedures are defined to transfer the elastoplastic deformation history to newly created integration points. A breakout damage criterion is proposed based on the second invariant of the deviatoric plastic deformation tensor. This damage criterion is used to apply a mesh movement algorithm to represent material collapse. The automatic successive application of the breakout damage criterion results in elliptical realistically looking geometries obtained in experiments reported in the literature.     

The supergene origin of ruthenian hexaferrum in Ni‐laterites

For two decades, the nature of Fe‐rich, oxygen‐bearing, Ru–Os compounds found in the supergene environment has been debated. Ru–Os–Fe‐oxides and nano‐intergrowths of ruthenium with magnetite have been proposed. We applied FE‐SEM, EMPA, μ‐Raman spectroscopy and synchrotron tts‐μXRD to Ru–Os–Fe compounds recovered from Ni‐laterites from the Dominican Republic. The results demonstrate that a significant portion of Fe exists in a common structure with the Ru–Os alloy, that is, ruthenian hexaferrum. This mineral occurs both as nanoparticles and as micrometric patches within a matrix of Fe‐oxide(s). Our data suggest that supergene ruthenian hexaferrum with a (Ru_0.4(Os,Ir)_0.1Fe_0.5)_?1.0 stoichiometry represents the most advanced weathering product of primary laurite within Ni‐laterites from the Dominican Republic.     

A gridded sea surface salinity data set for the tropical Pacific with sample applications (1950–2008)

We present a gridded data set of Sea Surface Salinity (SSS) for the tropical Pacific (120°E–70°W; 30°N–30°S), with a grid resolution of 1° longitude, 1° latitude and 1 month, from 1950 to 2008. The product, together with its associated error field, is derived from an objective analysis of about 10 million validated SSS records, with most of the data originating from Voluntary Observing Ships, TAO/TRITON moorings and Argo profilers (during the most recent period). We expect this product to benefit studies in oceanography, meteorology and paleoceanography. As examples of applications, we analyse: (a) the seasonal and ENSO (El Niño Southern Oscillation) modes of observed SSS variability, (b) the ability of 23 coupled models used in the Intergovernmental Panel for Climate Change 4th Assessment Report (IPCC AR4) to simulate the mean SSS and these two time varying modes, and (c) the usefulness of the SSS product and of its associated error field in calibrating and validating the paleo-salinity time series. We anticipate improvements and regular updates to our product, as more SSS data become available from in situ networks and from the ongoing and near-future satellite-derived observations by SMOS (Soil Moisture and Ocean Salinity) and Aquarius.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.