924 resultados para Geophysical techniques


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The research record on the quantification of sediment transport processes in periglacial mountain environments in Scandimvia dates back to the 1950s. A wide range of measurements is. available, especially from the Karkevagge region of northern Sweden. Within this paper satellite image analysis and tools provided by geographic information systems (GIS) are exploited in order to extend and improve this research and to complement geophysical methods. The processes of interest include mass movements such as solifluction, slope wash, dirty avalanches and rock-and boulder falls. Geomorphic process units have been derived in order to allow quantification via GIS techniques at a catchment scale. Mass movement rates based on existing Field measurements are employed in the budget calculation. In the Karkevagge catch ment. 80% of the area can be identified either as a source area for sediments or as a zone where sediments are deposited. The overall budget for the slopes beneath the rockwalls in the Karkevagge is approximately 680 t a(-1) whilst about 150 : a-1 are transported into the fluvial System.

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This review introduces the methods used to simulate the processes affecting dissolved oxygen (DO) in lowland rivers. The important processes are described and this provides a modelling framework to describe those processes in the context of a mass-balance model. The process equations that are introduced all require (reaction) rate parameters and a variety of common procedures for identifying those parameters are reviewed. This is important because there is a wide range of estimation techniques for many of the parameters. These different techniques elicit different estimates of the parameter value and so there is the potential for a significant uncertainty in the model's inputs and therefore in the output too. Finally, the data requirements for modelling DO in lowland rivers are summarised on the basis of modelling the processes described in this review using a mass-balance model. This is reviewed with regard to what data are available and from where they might be obtained. (C) 2003 Elsevier Science B.V. All rights reserved.

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Remote sensing can potentially provide information useful in improving pollution transport modelling in agricultural catchments. Realisation of this potential will depend on the availability of the raw data, development of information extraction techniques, and the impact of the assimilation of the derived information into models. High spatial resolution hyperspectral imagery of a farm near Hereford, UK is analysed. A technique is described to automatically identify the soil and vegetation endmembers within a field, enabling vegetation fractional cover estimation. The aerially-acquired laser altimetry is used to produce digital elevation models of the site. At the subfield scale the hypothesis that higher resolution topography will make a substantial difference to contaminant transport is tested using the AGricultural Non-Point Source (AGNPS) model. Slope aspect and direction information are extracted from the topography at different resolutions to study the effects on soil erosion, deposition, runoff and nutrient losses. Field-scale models are often used to model drainage water, nitrate and runoff/sediment loss, but the demanding input data requirements make scaling up to catchment level difficult. By determining the input range of spatial variables gathered from EO data, and comparing the response of models to the range of variation measured, the critical model inputs can be identified. Response surfaces to variation in these inputs constrain uncertainty in model predictions and are presented. Although optical earth observation analysis can provide fractional vegetation cover, cloud cover and semi-random weather patterns can hinder data acquisition in Northern Europe. A Spring and Autumn cloud cover analysis is carried out over seven UK sites close to agricultural districts, using historic satellite image metadata, climate modelling and historic ground weather observations. Results are assessed in terms of probability of acquisition probability and implications for future earth observation missions. (C) 2003 Elsevier Ltd. All rights reserved.

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Ochre samples excavated from the neolithic site at Qatalhoyuk, Turkey have been compared with "native" ochres from Clearwell Caves, UK using infrared spectroscopy backed up by Raman spectroscopy, scanning electron microscopy (with energy-dispersive X-rays (EDX) analysis), powder X-ray diffraction, diffuse reflection UV-Vis and atomic absorption spectroscopies. For the Clearwell Caves ochres, which range in colour from yellow-orange to red-brown, it is shown that the colour is related to the nature of the chromophore present and not to any differences in particle size. The darker red ochres contain predominantly haematite while the yellow ochre contains only goethite. The ochres from Qatalhoyuk contain only about one-twentieth of the levels of iron found in the Clearwell Caves ochres. The iron oxide pigment (haematite in all cases studied here) has been mixed with a soft lime plaster which also contains calcite and silicate (clay) minerals. (C) 2003 Elsevier B.V. All rights reserved.

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The time-of-flight technique is used on a small-angle neutron scattering instrument to separate the energies of the scattered neutrons, in order to determine the origin of the temperature-dependent scattering observed from silicon at Q > similar to 0.1 angstrom(-1). A quantitative analysis of the results in comparison with the phonon dispersion curves, determined by Dolling using a triple-axis neutron spectrometer, shows that the temperature-dependent scattering can be understood in terms of Umklapp processes whereby neutrons gain energy from phonons.

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Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot.

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The study of the morphology of tidal networks and their relation to salt marsh vegetation is currently an active area of research, and a number of theories have been developed which require validation using extensive observations. Conventional methods of measuring networks and associated vegetation can be cumbersome and subjective. Recent advances in remote sensing techniques mean that these can now often reduce measurement effort whilst at the same time increasing measurement scale. The status of remote sensing of tidal networks and their relation to vegetation is reviewed. The measurement of network planforms and their associated variables is possible to sufficient resolution using digital aerial photography and airborne scanning laser altimetry (LiDAR), with LiDAR also being able to measure channel depths. A multi-level knowledge-based technique is described to extract networks from LiDAR in a semi-automated fashion. This allows objective and detailed geomorphological information on networks to be obtained over large areas of the inter-tidal zone. It is illustrated using LIDAR data of the River Ems, Germany, the Venice lagoon, and Carnforth Marsh, Morecambe Bay, UK. Examples of geomorphological variables of networks extracted from LiDAR data are given. Associated marsh vegetation can be classified into its component species using airborne hyperspectral and satellite multispectral data. Other potential applications of remote sensing for network studies include determining spatial relationships between networks and vegetation, measuring marsh platform vegetation roughness, in-channel velocities and sediment processes, studying salt pans, and for marsh restoration schemes.

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At present, there is much anxiety regarding the security of energy supplies; for example, the UK and other European States are set to become increasingly dependant upon imports of natural gas from states with which political relations are often strained. These uncertainties are felt acutely by the electricity generating sector, which is facing major challenges regarding the choice of fuel mix in the years ahead. Nuclear energy may provide an alternative; however, in the UK, progress in replacing the first generation reactors is exceedingly slow. A number of operators are looking to coal as a means of plugging the energy gap. However, in the light of ever more stringent legal controls on emissions, this step cannot be taken without the adoption of sophisticated pollution abatement technology. This article examines the role which legal concepts such as Best Available Techniques (BAT) must play in bringing about these changes.

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The application of particle filters in geophysical systems is reviewed. Some background on Bayesian filtering is provided, and the existing methods are discussed. The emphasis is on the methodology, and not so much on the applications themselves. It is shown that direct application of the basic particle filter (i.e., importance sampling using the prior as the importance density) does not work in high-dimensional systems, but several variants are shown to have potential. Approximations to the full problem that try to keep some aspects of the particle filter beyond the Gaussian approximation are also presented and discussed.