Ecohydrology in Mediterranean areas: a numerical model to describe growing seasons out of phase with precipitations

International audienceThe probabilistic description of soil moisture dynamics is a relatively new topic in hydrology. The most common ecohydrological models start from a stochastic differential equation describing the soil water balance, where the unknown quantity, the soil moisture, depends both on spaces and time. Most of the solutions existing in literature are obtained in a probabilistic framework and under steady-state condition; even if this last condition allows the analytical handling of the problem, it has considerably simplified the same problem by subtracting generalities from it. The steady-state hypothesis, appears perfectly applicable in arid and semiarid climatic areas like those of African's or middle American's savannas, but it seems to be no more valid in areas with Mediterranean climate, where, notoriously, the wet season foregoes the growing season, recharging water into the soil. This moisture stored at the beginning of the growing season (known as soil moisture initial condition) has a great importance, especially for deep-rooted vegetation, by enabling survival in absence of rainfalls during the growing season and, however, keeping the water stress low during the first period of the same season. The aim of this paper is to analyze the soil moisture dynamics using a simple non-steady numerical ecohydrological model. The numerical model here proposed is able to reproduce soil moisture probability density function, obtained analytically in previous studies for different climates and soils in steady-state conditions; consequently it can be used to compute both the soil moisture time-profile and the vegetation static water stress time-profile in non-steady conditions. Here the differences between the steady-analytical and the non-steady numerical probability density functions are analyzed, showing how the proposed numerical model is able to capture the effects of winter recharge on the soil moisture. The dynamic water stress is also numerically evaluated, implicitly taking into account the soil moisture condition at the beginning of the growing season. It is also shown the role of different annual climatic parameterizations on the soil moisture probability density function and on the vegetation water stress evaluation. The proposed model is applied to a case study characteristic of Mediterranean climate: the watershed of Eleuterio in Sicily (Italy)

Modelling the shrub encroachment in a grassland with a Cellular Automata Model

Abstract. Arid and semi-arid grasslands of southwestern North America have changed dramatically over the last 150 years as a result of shrub encroachment, i.e. the increase in density, cover and biomass of indigenous shrubby plants in grasslands. Numerous studies have documented the expansion of shrublands in the southwestern American grasslands; in particular shrub encroachment has occurred strongly in part of the northern Chihuahuan desert since 1860. This encroachment has been simulated using an ecohydrological Cellular Automata model, CATGraSS. It is a spatially distributed model driven by spatially explicit irradiance and runs on a fine-resolution gridded domain. Plant competition is modelled by keeping track of mortality and establishment of plants; both are calculated probabilistically based on soil moisture stress. For this study CATGraSS has been improved with a stochastic fire module and a grazing function. The model has been implemented in a small area in Sevilleta National Wildlife Refuge (SNWR), characterized by two vegetation types (grass savanna and creosote bush shrub), considering as encroachment causes the fire return period increase, the grazing increase, the seed dispersal caused by animals, the role of wind direction and plant type competition. The model is able to reproduce the encroachment that has occurred in SNWR, simulating an increase of the shrub from 2% in 1860 to the current shrub percentage, 42%, and highlighting among the most influential factors the reduced fire frequency and the increased grazing intensity

Emerging biomarkers and metabolomics for assessing toxic nephropathy and acute kidney injury (AKI) in neonatology

Identification of novel drug-induced toxic nephropathy and acute kidney injury (AKI) biomarkers has been designated as a top priority by the American Society of Nephrology. Increasing knowledge in the science of biology and medicine is leading to the discovery of still more new biomarkers and of their roles in molecular pathways triggered by physiological and pathological conditions. Concomitantly, the development of the so-called “omics” allows the progressive clinical utilization of a multitude of information, from those related to the human genome (genomics) and proteome (proteomics), including the emerging epigenomics, to those related to metabolites (metabolomics). In preterm newborns, one of the most important factors causing the pathogenesis and the progression of AKI is the interaction between the individual genetic code, the environment, the gestational age, and the disease. By analyzing a small urine sample, metabolomics allows to identify instantly any change in phenotype, including changes due to genetic modifications. The role of liquid chromatography-mass spectrometry (LC-MS), proton nuclear magnetic resonance (1H NMR), and other emerging technologies is strategic, contributing basically to the sudden development of new biochemical and molecular tests. Urine neutrophil gelatinase-associated lipocalin (uNGAL) and kidney injury molecule-1 (KIM-1) are closely correlated with the severity of kidney injury, representing noninvasive sensitive surrogate biomarkers for diagnosing, monitoring, and quantifying kidney damage. To become routine tests, uNGAL and KIM-1 should be carefully tested in multicenter clinical trials and should be measured in biological fluids by robust, standardized analytical methods

Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico

This paper presents the development of a rainfall-triggered landslide module within an existing physically based spatially distributed ecohydrologic model. The model, tRIBS-VEGGIE (Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics are resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the study area of Luquillo Forest. The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards' equation (present in tRIBS-VEGGIE but not in tRIBS), which better represents the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the factor of safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the infinite slope model, creating a powerful tool for the assessment of rainfall-triggered landslide risk.United States. National Aeronautics and Space Administration (Project NNX07AD29G

Quasi-Particle Spectra, Charge-Density-Wave, Superconductivity and Electron-Phonon Coupling in 2H-NbSe2

High-resolution photoemission has been used to study the electronic structure of the charge density wave (CDW) and superconducting (SC) dichalcogenide, 2H- NbSe2. From the extracted self-energies, important components of the quasiparticle (QP) interactions have been identified. In contrast to previously studied TaSe2, the CDW transition does not affect the electronic properties significantly. The electron-phonon coupling is identified as a dominant contribution to the QP self-energy and is shown to be very anisotropic (k-dependent) and much stronger than in TaSe2.Comment: 4 pages, 3 figures, minor changes, to appear in PR

A Positron Implantation Profile Estimation Approach for the PALS Study of Battery Materials

Positron annihilation spectroscopy is a powerful probe to investigate the interfaces in materials relevant for energy storage such as Li-ion batteries. The key to the interpretation of the results is the positron implantation profile, which is a spatial function related to the characteristics of the materials forming the battery. We provide models for the positron implantation profile in a cathode of a Li-ion battery coin cell. These models are the basis for a reliable visualization of multilayer geometries and their interfaces in thin cathodes of lithium-ion batteries

Tracking system based on GEM chambers

GEM chambers are becoming one of the best technology for charge particle tracking fulfilling the challenging requirements of modern experiment at intermediate and high energy, including Parity Violation Electron Scattering experiments. GEM tracker combines high spatial resolution, large active area and pretty good tolerance to high particle flux, at reasonable cost. GEM technology is shortly presented and a specific application for the high luminosity experiments in Hall A at JLab is discussed. Some alternatives to the GEM are also addressed

Synthesis, studies and fuel cell performance of “core–shell” electrocatalysts for oxygen reduction reaction based on a PtNix carbon nitride “shell” and a pyrolyzed polyketone nanoball “core”

This report describes a new class of "core-shell" electrocatalysts for oxygen reduction reaction (ORR) processes for application in Proton Exchange Membrane Fuel Cells (PEMFCs). The electrocatalysts are obtained by supporting a "shell" consisting of PtNix alloy nanoparticles embedded into a carbon nitride matrix (indicated as PtNix-CN) on a "core" of pyrolyzed polyketone nanoballs, labeled 'STp'. ST(p)s are obtained by the sulfonation and pyrolysis of a precursor consisting of XC-72R carbon nanoparticles wrapped by polyketone (PK) fibers. The ST(p)s are extensively characterized in terms of the chemical composition, thermal stability, degree of graphitization and morphology. The "core-shell" ORR electrocatalysts are prepared by the pyrolysis of precursors obtained impregnating the STp "cores" with a zeolitic inorganic-organic polymer electrolyte (Z-IOPE) plastic material. The electrochemical performance of the electrocatalysts in the ORR is tested "in situ" by single fuel cell tests. The interplay between the chemical composition, the degree of graphitization of both PtNix-CN "shell" and STpS "cores", the morphology of the electrocatalysts and the fuel cell performance is elucidated. The most crucial preparation parameters for the optimization of the various features affecting the fuel cell performance of this promising class of ORR electrocatalysts are identified