Assessment of assimilating SMOS soil moisture information into a distributed hydrologic model

The role that soil moisture plays in terms of modulating hydrologic processes including infiltration and runoff generation makes it an essential component to capture for hydrologic modeling. This work aims to leverage information gained from SMOS to improve surface soil moisture simulations in the Russian River Basin (California, U.S.A). The basin's complex terrain offers a rigorous testing ground for SMOS soil moisture products. Data from seven in situ observation sites are used to assess model performance after assimilating SMOS-based soil saturation ratios. For a comparison of "best case" scenarios, the in situ observations themselves are assimilated. Results show that SMOS assimilated simulations shows modest improvement at most in situ locations. Despite the observed decrease in model performance at some locations, overall performance of simulations assimilated with SMOS-based saturation ratios remains high. Findings suggest that even in a complex environment, useful information may be extracted from SMOS estimates for hydrologic modeling

Overview on new psychoactive substances in Portugal

This working paper provides an overview of the phenomenon of new psychoactive substances (NPS) in Portugal, including suggested definitions of NPS, a review of drug policy in Portugal, NPS markets, NPS demand and supply, prevention strategies and insights from expert interviews. NPS emerged in Portugal in 2007, and despite the closure of NPS physical selling points in 2013 and decreasing rates of NPS consumption, the market seems to be continuing with new particularities: a rise in unintentional consumers and the increasing association with problematic drug use. The new trends in users and consumption patterns as well as new forms of communication, acquisition, and production of substances have challenged conventional mechanisms of drug control in Portugal

Effect of Particle Orientation on the Elastic Anisotropy of Al/SiCp Metal Matrix Composites

Metal matrix composites (MMCs) are promising new materials for structural applications because of their high specific stiffness and strength, and high temperature stability. Of particular interest are the discontinuous silicon carbide (SiC) reinforced aluminum metal matrix composites. The improved mechanical properties are governed by the properties of the constituent phases, as well as the SiC particle characteristics such as shape, aspect ratio and orientation. The particle characteristics have a major effect on the anisotropic properties of these composites. The overall properties also depend on the manufacturing process of these composites since it determines the orientation of the particles and may produce internal defects such as porosity and intermetallic compounds [l]. Thus it is important to experimentally characterize the effective elastic properties and to theoretically predict them from the knowledge of the constituent properties and the microstructures

Avalanche-Induced Current Enhancement in Semiconducting Carbon Nanotubes

Semiconducting carbon nanotubes under high electric field stress (~10 V/um) display a striking, exponential current increase due to avalanche generation of free electrons and holes. Unlike in other materials, the avalanche process in such 1D quantum wires involves access to the third sub-band, is insensitive to temperature, but strongly dependent on diameter ~exp(-1/d^2). Comparison with a theoretical model yields a novel approach to obtain the inelastic optical phonon emission length, L_OP,ems ~ 15d nm. The combined results underscore the importance of multi-band transport in 1D molecular wires

A review of Monte Carlo simulations of polymers with PERM

In this review, we describe applications of the pruned-enriched Rosenbluth method (PERM), a sequential Monte Carlo algorithm with resampling, to various problems in polymer physics. PERM produces samples according to any given prescribed weight distribution, by growing configurations step by step with controlled bias, and correcting "bad" configurations by "population control". The latter is implemented, in contrast to other population based algorithms like e.g. genetic algorithms, by depth-first recursion which avoids storing all members of the population at the same time in computer memory. The problems we discuss all concern single polymers (with one exception), but under various conditions: Homopolymers in good solvents and at the $\Theta$ point, semi-stiff polymers, polymers in confining geometries, stretched polymers undergoing a forced globule-linear transition, star polymers, bottle brushes, lattice animals as a model for randomly branched polymers, DNA melting, and finally -- as the only system at low temperatures, lattice heteropolymers as simple models for protein folding. PERM is for some of these problems the method of choice, but it can also fail. We discuss how to recognize when a result is reliable, and we discuss also some types of bias that can be crucial in guiding the growth into the right directions.Comment: 29 pages, 26 figures, to be published in J. Stat. Phys. (2011

Elastic Moduli of Silicon Carbide Particulate Reinforced Aluminum Metal Matrix Composites

The mechanical properties of metal matrix composites (MMCs) reinforced by discontinuous silicon carbides are governed by the properties of the reinforcing phase, as well as their morphology (whisker vs. particulate), orientation and volume fraction. The morphology of SiC particles and their orientation are major variables affecting the anisotropic properties of these composites. SiC whisker (SiCW) reinforced aluminum MMCs tend to have higher strengths and moduli in the extrusion direction due to the high degree of whisker alignment in that direction, and these values are higher than those for SiC particulate (SiCp) reinforced composites at a given reinforcement level [1]. SiCpreinforced MMCs are known to be more isotropic in the extrusion plane. In situations requiring multidirectional reinforcement, particulate reinforced composites can outperform whisker reinforced composites. Thus, it is important to characterize the mechanical properties of these composites in order to develop the criteria for selecting microstructural design variables

Universal scaling functions for bond percolation on planar random and square lattices with multiple percolating clusters

Percolation models with multiple percolating clusters have attracted much attention in recent years. Here we use Monte Carlo simulations to study bond percolation on $L_{1}\times L_{2}$ planar random lattices, duals of random lattices, and square lattices with free and periodic boundary conditions, in vertical and horizontal directions, respectively, and with various aspect ratio $L_{1}/L_{2}$. We calculate the probability for the appearance of $n$ percolating clusters, $W_{n},$ the percolating probabilities, $P$, the average fraction of lattice bonds (sites) in the percolating clusters, $_{n}$ ($_{n}$), and the probability distribution function for the fraction $c$ of lattice bonds (sites), in percolating clusters of subgraphs with $n$ percolating clusters, $f_{n}(c^{b})$ ($f_{n}(c^{s})$). Using a small number of nonuniversal metric factors, we find that $W_{n}$, $P$, $_{n}$ ($_{n}$), and $f_{n}(c^{b})$ ($f_{n}(c^{s})$) for random lattices, duals of random lattices, and square lattices have the same universal finite-size scaling functions. We also find that nonuniversal metric factors are independent of boundary conditions and aspect ratios.Comment: 15 pages, 11 figure

A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning