Organic matter processing and soil evolution in a braided river system

Traditionally, braided river research has considered flow, sediment transport processes and, recently, vegetation dynamics in relation to river morphodynamics. However, if considering the development of woody vegetated patches over a time scale of decades, we must consider the extent to which soil forming processes, particularly related to soil organic matter, impact the alluvial geomorphic-vegetation system. Here we quantify the soil organic matter processing (humification) that occurs on young alluvial landforms. We sampled different geomorphic units, ranging from the active river channel to established river terraces in a braided river system. For each geomorphic unit, soil pits were used to sample sediment/soil layers that were analysed in terms of grain size (<2mm) and organic matter quantity and quality (RockEval method). A principal components analysis was used to identify patterns in the dataset. Results suggest that during the succession from bare river gravels to a terrace soil, there is a transition from small amounts of external organic matter supply provided by sedimentation processes (e.g. organic matter transported in suspension and deposited on bars), to large amounts of autogenic in situ organic matter production due to plant colonisation. This appears to change the time scale and pathways of alluvial succession (bio-geomorphic succession). However, this process is complicated by: the ongoing possibility of local sedimentation, which can serve to isolate surface layers via aggradation from the exogenic supply; and erosion which tends to create fresh deposits upon which organic matter processing must re-start. The result is a complex pattern of organic matter states as well as a general lack of any clear chronosequence within the active river corridor. This state reflects the continual battle between deposition events that can isolate organic matter from the surface, erosion events that can destroy accumulating organic matter and the early ecosystem processes necessary to assist the co-evolution of soil and vegetation. A key question emerges over the extent to which the fresh organic matter deposited in the active zone is capable of significantly transforming the local geochemical environment sufficiently to accelerate soil development.

On the diurnal soil water content dynamics during evaporation using dielectric methods

The water content dynamics in the upper soil surface during evaporation is a key element in land-atmosphere exchanges. Previous experimental studies have suggested that the soil water content increases at the depth of 5 to 15 cm below the soil surface during evapo- ration, while the layer in the immediate vicinity of the soil surface is drying. In this study, the dynamics of water content profiles exposed to solar radiative forcing was monitored at a high temporal resolution using dielectric methods both in the presence and absence of evaporation. A 4-d comparison of reported moisture content in coarse sand in covered and uncovered buckets using a commercial dielectric-based probe (70 MHz ECH2O-5TE, Decagon Devices, Pullman, WA) and the standard 1-GHz time domain reflectometry method. Both sensors reported a positive correlation between temperature and water content in the 5- to 10-cm depth, most pronounced in the morning during heating and in the afternoon during cooling. Such positive correlation might have a physical origin induced by evaporation at the surface and redistribution due to liquid water fluxes resulting from the temperature- gradient dynamics within the sand profile at those depths. Our experimental data suggest that the combined effect of surface evaporation and temperature-gradient dynamics should be considered to analyze experimental soil water profiles. Additional effects related to the frequency of operation and to protocols for temperature compensation of the dielectric sensors may also affect the probes' response during large temperature changes.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Soil diffuse degassing and thermal energy fluxes from the southern Lakki plain, Nisyros (Greece)

Two diffuse soil CO2 flux surveys from the southern Lakki plain show that CO2 is mainly released from the hydrothermal explosion craters. The correspondence between high CO2 fluxes and elevated soil temperatures suggests that a flux of hot hydrothermal fluids ascends towards the surface. Steam mostly condenses near the surface and the heat given off is conductively transferred to the atmosphere through the soil, accompanied by a large CO2 flux. Tt was calculated, that 68 t d(-1) of hydrothermal CO2 are released through the total surveyed area of similar to1.3 km(2) Admitting that a steam flux of 2200 t d(-1) accompanies this CO2 flux, the thermal energy released through steam condensation amounts to 58 MW.

Controls on 13C and 14C variability in soil CO2

14C dating of groundwater depends on the isotopic composition of both the solid carbonate and the soil CO2 and requires the use of 14C age correction models. To better assess the variability of the 14C activity of soil CO2 (A14Csoil-CO2) and the δ13C of soil CO2 (δ13Csoil-CO2), which are two parameters used in 14C age correction models, we studied the different processes involving carbon isotopes in the soil. The approach used experimental data from two sites in France (Fontainebleau sands and Astian sands) and a steady-state transport model. In most cases, the 14C activity (A14C) of atmospheric CO2 is directly used in the 14C age correction models as the A14Csoil-CO2. However, we demonstrate that since 1950, the evolution of the A14Csoil-CO2 reflects the competition between the fluxes of root-derived CO2 and organic matter-derived CO2. Therefore, the A14Csoil-CO2 must be used to date groundwater that is younger than 60 years old. Moreover, the δ13C of soil CO2 (δ13Csoil-CO2) showed large seasonal variations that must be taken into account in selecting the δ13Csoil-CO2 for 14C age correction models.

Comparison of central macular thickness (CMT) measurement between time domain and spectral domain (SD) optical coherence tomography (OCT) and reproducibility of SD OCT in active neovascular AMD

Purpose: To evaluate the reproducibility of Cirrus-SD OCT measurements and to compare central macular thickness (CMT) measurements between TD-Stratus and SD-Cirrus OCT in patients with active exudative AMD. Methods: Consecutive case series of patients with active exudative AMD seen in the Medical Retina Department. Patients underwent 1 scan with Stratus (macular thickness map protocol) and 5 scans with Cirrus (Macular Cube protocol) at the same visit by the same experienced examiner. To be included, patients best-corrected visual acuity (BCVA) had to be >20/200 while all scans had to be of sufficient quality, well-centered and at least one Cirrus scan with CMT >300 microns. The repeatability of the SD Cirrus was estimated by using all 5 CMT measurements and the mean of the Cirrus measurements was compared with the CMT obtained by TD Stratus. Results: Cirrus OCT demonstrated high intraobserver repeatability at the central foveal region (ICC 96%). The mean of the CMT measurements was 321microns for Stratus and 387 microns for Cirrus. The average difference was 65m (SD=30). The coefficient of concordance between Stratus and Cirrus CMT measurements was rho=0,749 with a high precision and a moderate accuracy. The equation of the line of regression between Stratus and meanCirrus is given by the following: M_stratus = 0,848 x m_cirrus - 4,496 (1).Conclusions: The Cirrus macular cube protocol allows reproducible CMT measurements in patients with active exudative AMD. In cases of upgrading from TD to SD use and vice versa, there is the possibility to predict the measurements by using the equation (1). These real life data and conclusions can help in improving our clinical management of patients with neovascular AMD.

Testing the influence of topography and material properties on catchment-scale soil moisture patterns using remotely sensed vegetation patterns in a humid temperate catchment, northern Britain

In order to evaluate the relationship between the apparent complexity of hillslope soil moisture and the emergent patterns of catchment hydrological behaviour and water quality, we need fine-resolution catchment-wide data on soil moisture characteristics. This study proposes a methodology whereby vegetation patterns obtained from high-resolution orthorectified aerial photographs are used as an indicator of soil moisture characteristics. This enables us to examine a set of hypotheses regarding what drives the spatial patterns of soil moisture at the catchment scale (material properties or topography). We find that the pattern of Juncus effusus vegetation is controlled largely by topography and mediated by the catchment's material properties. Characterizing topography using the topographic index adds value to the soil moisture predictions relative to slope or upslope contributing area (UCA). However, these predictions depart from the observed soil moisture patterns at very steep slopes or low UCAs. Copyright (c) 2012 John Wiley & Sons, Ltd.

En face optical coherence tomography of foveal microstructure in full-thickness macular hole: a model to study perifoveal müller cells.

PURPOSE: To characterize perifoveal intraretinal cavities observed around full-thickness macular holes (MH) using en face optical coherence tomography and to establish correlations with histology of human and primate maculae. DESIGN: Retrospective nonconsecutive observational case series. METHODS: Macular en face scans of 8 patients with MH were analyzed to quantify the areas of hyporeflective spaces, and were compared with macular flat mounts and sections from 1 normal human donor eye and 2 normal primate eyes (Macaca fascicularis). Immunohistochemistry was used to study the distribution of glutamine synthetase, expressed by Müller cells, and zonula occludens-1, a tight-junction protein. RESULTS: The mean area of hyporeflective spaces was lower in the inner nuclear layer (INL) than in the complex formed by the outer plexiform (OPL) and the Henle fiber layers (HFL): 5.0 × 10(-3) mm(2) vs 15.9 × 10(-3) mm(2), respectively (P < .0001, Kruskal-Wallis test). In the OPL and HFL, cavities were elongated with a stellate pattern, whereas in the INL they were rounded and formed vertical cylinders. Immunohistochemistry confirmed that Müller cells followed a radial distribution around the fovea in the frontal plane and a "Z-shaped" course in the axial plane, running obliquely in the OPL and HFL and vertically in the inner layers. In addition, zonula occludens-1 co-localized with Müller cells within the complex of OPL and HFL, indicating junctions in between Müller cells and cone axons. CONCLUSION: The dual profile of cavities around MHs correlates with Müller cell morphology and is consistent with the hypothesis of intra- or extracellular fluid accumulation along these cells.

New insight in the relationship between regional patterns of knee cartilage thickness, osteoarthritis disease severity, and gait mechanics.

To test if the relationship between knee kinetics during walking and regional patterns of cartilage thickness is influenced by disease severity we tested the following hypotheses in a cross-sectional study of medial compartment osteoarthritis (OA) subjects: (1) the peak knee flexion (KFM) and adduction moments (KAM) during walking are associated with regional cartilage thickness and medial-to-lateral cartilage thickness ratios, and (2) the associations between knee moments and cartilage thickness data are dependent on disease severity. Seventy individuals with medial compartment knee OA were studied. Gait analysis was used to determine the knee moments and cartilage thickness was measured from magnetic resonance imaging. Multiple linear regression analyses tested for associations between cartilage thickness and knee kinetics. Medial cartilage thickness and medial-to-lateral cartilage thickness ratios were lower in subjects with greater KAM for specific regions of the femoral condyle and tibial plateau with no associations for KFM in patients of all disease severities. When separated by severity, the association between KAM and cartilage thickness was found only in patients with more severe OA, and KFM was significantly associated with cartilage thickness only for the less severe OA subjects for specific tibial plateau regions. The results support the idea that the KAM is larger in patients with more severe disease and the KFM has greater influence early in the disease process, which may lessen as pain increases with disease severity. Each component influences different regions of cartilage. Thus the relative contributions of both KAM and KFM should be considered when evaluating gait mechanics and the influence of any intervention for knee OA.

Effect of partial-thickness tear on loading capacities of the supraspinatus tendon: a finite element analysis.

Partial-thickness tears of the supraspinatus tendon frequently occur at its insertion on the greater tubercule of the humerus, causing pain and reduced strength and range of motion. The goal of this work was to quantify the loss of loading capacity due to tendon tears at the insertion area. A finite element model of the supraspinatus tendon was developed using in vivo magnetic resonance images data. The tendon was represented by an anisotropic hyperelastic constitutive law identified with experimental measurements. A failure criterion was proposed and calibrated with experimental data. A partial-thickness tear was gradually increased, starting from the deep articular-sided fibres. For different values of tendon tear thickness, the tendon was mechanically loaded up to failure. The numerical model predicted a loss in loading capacity of the tendon as the tear thickness progressed. Tendon failure was more likely when the tendon tear exceeded 20%. The predictions of the model were consistent with experimental studies. Partial-thickness tears below 40% tear are sufficiently stable to persist physiotherapeutic exercises. Above 60% tear surgery should be considered to restore shoulder strength.