Iron isotope systematics in Arctic rivers

The input of iron to the Arctic Ocean plays a critical role in the productivity of aquatic ecosystems and is potentially impacted by climate change. We examine Fe isotope systematics of dissolved and colloidal Fe from several Arctic and sub-Arctic rivers in northern Eurasia and Alaska. We demonstrate that the Fe isotopic (δ56Fe) composition of large rivers, such as the Ob’ and Lena, has a restricted range of δ56Fe values ca.–0.11 ± 0.13‰, with minimal seasonal variability, in stark contrast to smaller organic-rich rivers with an overall δ56Fe range from–1.7 to + 1.6‰. The preferential enrichment with heavy Fe isotopes observed in low molecular weight colloidal fraction and during the high-flow period is consistent with the role of organic complexation of Fe. The light Fe isotope signatures of smaller rivers and meltwater reflect active redox cycling. Data synthesis reveals that small organic-rich rivers and meltwater in Arctic environments may contribute disproportionately to the input of labile Fe in the Arctic Ocean, while bearing contrasting Fe isotope compositions compared to larger rivers.

Radio-Frequency Interference Identification Over Oceans for C- and X-Band AMSR2 Channels

A new method for radio-frequency interference (RFI) contamination identification over open oceans for the two C-subbands and X-band of Advanced Microwave Scanning Radiometer 2 (AMSR2) channel measurements is suggested. The method is based both on the AMSR2 brightness temperature (T-B) modeling and on the analysis of AMSR2 measurements over oceans. The joint analysis of T-B spectral differences allowed to identify the relations between them and the limits of their variability, which are ensured by the changes in the environmental conditions. It was found that the constraints, based on the ratio of spectral differences, are more regionally and seasonally independent than the spectral differences themselves. Although not all possible RFI combinations are considered, the developed simple criteria can be used to detect most RFI-contaminated pixels over the World Ocean for AMSR2 measurements in two C-subbands and the X-band.

Nd-Sr isotopes, diatom and biogenic opal content of Pliocene sediments from IODP Site 318-U1361

Warm intervals within the Pliocene epoch (5.33-2.58 million years ago) were characterized by global temperatures comparable to those predicted for the end of this century (Haywood and Valdes, doi:10.1016/S0012-821X(03)00685-X) and atmospheric CO2 concentrations similar to today (Seki et al., 2010, doi:10.1016/j.epsl.2010.01.037; Bartoli et al., 2011, doi:10.1029/2010PA002055; Pagani et al., 2010, doi:10.1038/ngeo724). Estimates for global sea level highstands during these times (Miller et al., 2012, doi:10.1130/G32869.1) imply possible retreat of the East Antarctic ice sheet, but ice-proximal evidence from the Antarctic margin is scarce. Here we present new data from Pliocene marine sediments recovered offshore of Adélie Land, East Antarctica, that reveal dynamic behaviour of the East Antarctic ice sheet in the vicinity of the low-lying Wilkes Subglacial Basin during times of past climatic warmth. Sedimentary sequences deposited between 5.3 and 3.3 million years ago indicate increases in Southern Ocean surface water productivity, associated with elevated circum-Antarctic temperatures. The geochemical provenance of detrital material deposited during these warm intervals suggests active erosion of continental bedrock from within the Wilkes Subglacial Basin, an area today buried beneath the East Antarctic ice sheet. We interpret this erosion to be associated with retreat of the ice sheet margin several hundreds of kilometres inland and conclude that the East Antarctic ice sheet was sensitive to climatic warmth during the Pliocene.

Lattice Boltzmann Modeling and Specialized Laboratory Techniques to Determine the Permeability of Megaporous Karst Rock

The Pleistocene carbonate rock Biscayne Aquifer of south Florida contains laterally-extensive bioturbated ooltic zones characterized by interconnected touching-vug megapores that channelize most flow and make the aquifer extremely permeable. Standard petrophysical laboratory techniques may not be capable of accurately measuring such high permeabilities. Instead, innovative procedures that can measure high permeabilities were applied. These fragile rocks cannot easily be cored or cut to shapes convenient for conducting permeability measurements. For the laboratory measurement, a 3D epoxy-resin printed rock core was produced from computed tomography data obtained from an outcrop sample. Permeability measurements were conducted using a viscous fluid to permit easily observable head gradients (~2 cm over 1 m) simultaneously with low Reynolds number flow. For a second permeability measurement, Lattice Boltzmann Method flow simulations were computed on the 3D core renderings. Agreement between the two estimates indicates an accurate permeability was obtained that can be applied to future studies.

Challenges in imaging and predictive modeling of rhizosphere processes

Background Plant-soil interaction is central to human food production and ecosystem function. Thus, it is essential to not only understand, but also to develop predictive mathematical models which can be used to assess how climate and soil management practices will affect these interactions. Scope In this paper we review the current developments in structural and chemical imaging of rhizosphere processes within the context of multiscale mathematical image based modeling. We outline areas that need more research and areas which would benefit from more detailed understanding. Conclusions We conclude that the combination of structural and chemical imaging with modeling is an incredibly powerful tool which is fundamental for understanding how plant roots interact with soil. We emphasize the need for more researchers to be attracted to this area that is so fertile for future discoveries. Finally, model building must go hand in hand with experiments. In particular, there is a real need to integrate rhizosphere structural and chemical imaging with modeling for better understanding of the rhizosphere processes leading to models which explicitly account for pore scale processes.

La implementación de la trayectoria de desarrollo sostenible por parte de los pequeños estados insulares en desarrollo del Pacífico frente a su condición de vulnerabilidad

La investigación busca determinar el propósito que persiguen los pequeños Estados insulares en desarrollo del Pacífico al adoptar una trayectoria de desarrollo sostenible. Se plantea que la adopción de una trayectoria de desarrollo sostenible es la estrategia por medio de la cual estos Estados buscan hacer frente a su condición de vulnerabilidad; lo cual logran a través del uso de la diplomacia en distintos escenarios multilaterales, con el fin de modificar tanto sus prácticas como las de otros actores.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.