939 resultados para COVARIANCE


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We investigated the seasonal patterns of Amazonian forest photosynthetic activity, and the effects thereon of variations in climate and land-use, by integrating data from a network of ground-based eddy flux towers in Brazil established as part of the ‘Large-Scale Biosphere Atmosphere Experiment in Amazonia’ project. We found that degree of water limitation, as indicated by the seasonality of the ratio of sensible to latent heat flux (Bowen ratio) predicts seasonal patterns of photosynthesis. In equatorial Amazonian forests (5◦ N–5◦ S), water limitation is absent, and photosynthetic fluxes (or gross ecosystem productivity, GEP) exhibit high or increasing levels of photosynthetic activity as the dry season progresses, likely a consequence of allocation to growth of new leaves. In contrast, forests along the southern flank of the Amazon, pastures converted from forest, and mixed forest-grass savanna, exhibit dry-season declines in GEP, consistent with increasing degrees of water limitation. Although previous work showed tropical ecosystem evapotranspiration (ET) is driven by incoming radiation, GEP observations reported here surprisingly show no or negative relationships with photosynthetically active radiation (PAR). Instead, GEP fluxes largely followed the phenology of canopy photosynthetic capacity (Pc), with only deviations from this primary pattern driven by variations in PAR. Estimates of leaf flush at three

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The quality of temperature and humidity retrievals from the infrared SEVIRI sensors on the geostationary Meteosat Second Generation (MSG) satellites is assessed by means of a one dimensional variational algorithm. The study is performed with the aim of improving the spatial and temporal resolution of available observations to feed analysis systems designed for high resolution regional scale numerical weather prediction (NWP) models. The non-hydrostatic forecast model COSMO (COnsortium for Small scale MOdelling) in the ARPA-SIM operational configuration is used to provide background fields. Only clear sky observations over sea are processed. An optimised 1D–VAR set-up comprising of the two water vapour and the three window channels is selected. It maximises the reduction of errors in the model backgrounds while ensuring ease of operational implementation through accurate bias correction procedures and correct radiative transfer simulations. The 1D–VAR retrieval quality is firstly quantified in relative terms employing statistics to estimate the reduction in the background model errors. Additionally the absolute retrieval accuracy is assessed comparing the analysis with independent radiosonde and satellite observations. The inclusion of satellite data brings a substantial reduction in the warm and dry biases present in the forecast model. Moreover it is shown that the retrieval profiles generated by the 1D–VAR are well correlated with the radiosonde measurements. Subsequently the 1D–VAR technique is applied to two three–dimensional case–studies: a false alarm case–study occurred in Friuli–Venezia–Giulia on the 8th of July 2004 and a heavy precipitation case occurred in Emilia–Romagna region between 9th and 12th of April 2005. The impact of satellite data for these two events is evaluated in terms of increments in the integrated water vapour and saturation water vapour over the column, in the 2 meters temperature and specific humidity and in the surface temperature. To improve the 1D–VAR technique a method to calculate flow–dependent model error covariance matrices is also assessed. The approach employs members from an ensemble forecast system generated by perturbing physical parameterisation schemes inside the model. The improved set–up applied to the case of 8th of July 2004 shows a substantial neutral impact.

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The Assimilation in the Unstable Subspace (AUS) was introduced by Trevisan and Uboldi in 2004, and developed by Trevisan, Uboldi and Carrassi, to minimize the analysis and forecast errors by exploiting the flow-dependent instabilities of the forecast-analysis cycle system, which may be thought of as a system forced by observations. In the AUS scheme the assimilation is obtained by confining the analysis increment in the unstable subspace of the forecast-analysis cycle system so that it will have the same structure of the dominant instabilities of the system. The unstable subspace is estimated by Breeding on the Data Assimilation System (BDAS). AUS- BDAS has already been tested in realistic models and observational configurations, including a Quasi-Geostrophicmodel and a high dimensional, primitive equation ocean model; the experiments include both fixed and“adaptive”observations. In these contexts, the AUS-BDAS approach greatly reduces the analysis error, with reasonable computational costs for data assimilation with respect, for example, to a prohibitive full Extended Kalman Filter. This is a follow-up study in which we revisit the AUS-BDAS approach in the more basic, highly nonlinear Lorenz 1963 convective model. We run observation system simulation experiments in a perfect model setting, and with two types of model error as well: random and systematic. In the different configurations examined, and in a perfect model setting, AUS once again shows better efficiency than other advanced data assimilation schemes. In the present study, we develop an iterative scheme that leads to a significant improvement of the overall assimilation performance with respect also to standard AUS. In particular, it boosts the efficiency of regime’s changes tracking, with a low computational cost. Other data assimilation schemes need estimates of ad hoc parameters, which have to be tuned for the specific model at hand. In Numerical Weather Prediction models, tuning of parameters — and in particular an estimate of the model error covariance matrix — may turn out to be quite difficult. Our proposed approach, instead, may be easier to implement in operational models.

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In this thesis work I analyze higher spin field theories from a first quantized perspective, finding in particular new equations describing complex higher spin fields on Kaehler manifolds. They are studied by means of worldline path integrals and canonical quantization, in the framework of supersymmetric spinning particle theories, in order to investigate their quantum properties both in flat and curved backgrounds. For instance, by quantizing a spinning particle with one complex extended supersymmetry, I describe quantum massless (p,0)-forms and find a worldline representation for their effective action on a Kaehler background, as well as exact duality relations. Interesting results are found also in the definition of the functional integral for the so called O(N) spinning particles, that will allow to study real higher spins on curved spaces. In the second part, I study Weyl invariant field theories by using a particular mathematical framework known as tractor calculus, that enable to maintain at each step manifest Weyl covariance.

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In this work we are concerned with the analysis and numerical solution of Black-Scholes type equations arising in the modeling of incomplete financial markets and an inverse problem of determining the local volatility function in a generalized Black-Scholes model from observed option prices. In the first chapter a fully nonlinear Black-Scholes equation which models transaction costs arising in option pricing is discretized by a new high order compact scheme. The compact scheme is proved to be unconditionally stable and non-oscillatory and is very efficient compared to classical schemes. Moreover, it is shown that the finite difference solution converges locally uniformly to the unique viscosity solution of the continuous equation. In the next chapter we turn to the calibration problem of computing local volatility functions from market data in a generalized Black-Scholes setting. We follow an optimal control approach in a Lagrangian framework. We show the existence of a global solution and study first- and second-order optimality conditions. Furthermore, we propose an algorithm that is based on a globalized sequential quadratic programming method and a primal-dual active set strategy, and present numerical results. In the last chapter we consider a quasilinear parabolic equation with quadratic gradient terms, which arises in the modeling of an optimal portfolio in incomplete markets. The existence of weak solutions is shown by considering a sequence of approximate solutions. The main difficulty of the proof is to infer the strong convergence of the sequence. Furthermore, we prove the uniqueness of weak solutions under a smallness condition on the derivatives of the covariance matrices with respect to the solution, but without additional regularity assumptions on the solution. The results are illustrated by a numerical example.

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The Spin-Statistics theorem states that the statistics of a system of identical particles is determined by their spin: Particles of integer spin are Bosons (i.e. obey Bose-Einstein statistics), whereas particles of half-integer spin are Fermions (i.e. obey Fermi-Dirac statistics). Since the original proof by Fierz and Pauli, it has been known that the connection between Spin and Statistics follows from the general principles of relativistic Quantum Field Theory. In spite of this, there are different approaches to Spin-Statistics and it is not clear whether the theorem holds under assumptions that are different, and even less restrictive, than the usual ones (e.g. Lorentz-covariance). Additionally, in Quantum Mechanics there is a deep relation between indistinguishabilty and the geometry of the configuration space. This is clearly illustrated by Gibbs' paradox. Therefore, for many years efforts have been made in order to find a geometric proof of the connection between Spin and Statistics. Recently, various proposals have been put forward, in which an attempt is made to derive the Spin-Statistics connection from assumptions different from the ones used in the relativistic, quantum field theoretic proofs. Among these, there is the one due to Berry and Robbins (BR), based on the postulation of a certain single-valuedness condition, that has caused a renewed interest in the problem. In the present thesis, we consider the problem of indistinguishability in Quantum Mechanics from a geometric-algebraic point of view. An approach is developed to study configuration spaces Q having a finite fundamental group, that allows us to describe different geometric structures of Q in terms of spaces of functions on the universal cover of Q. In particular, it is shown that the space of complex continuous functions over the universal cover of Q admits a decomposition into C(Q)-submodules, labelled by the irreducible representations of the fundamental group of Q, that can be interpreted as the spaces of sections of certain flat vector bundles over Q. With this technique, various results pertaining to the problem of quantum indistinguishability are reproduced in a clear and systematic way. Our method is also used in order to give a global formulation of the BR construction. As a result of this analysis, it is found that the single-valuedness condition of BR is inconsistent. Additionally, a proposal aiming at establishing the Fermi-Bose alternative, within our approach, is made.

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This thesis is mainly concerned with a model calculation for generalized parton distributions (GPDs). We calculate vectorial- and axial GPDs for the N N and N Delta transition in the framework of a light front quark model. This requires the elaboration of a connection between transition amplitudes and GPDs. We provide the first quark model calculations for N Delta GPDs. The examination of transition amplitudes leads to various model independent consistency relations. These relations are not exactly obeyed by our model calculation since the use of the impulse approximation in the light front quark model leads to a violation of Poincare covariance. We explore the impact of this covariance breaking on the GPDs and form factors which we determine in our model calculation and find large effects. The reference frame dependence of our results which originates from the breaking of Poincare covariance can be eliminated by introducing spurious covariants. We extend this formalism in order to obtain frame independent results from our transition amplitudes.

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Carbon fluxes and allocation pattern, and their relationship with the main environmental and physiological parameters, were studied in an apple orchard for one year (2010). I combined three widely used methods: eddy covariance, soil respiration and biometric measurements, and I applied a measurement protocol allowing a cross-check between C fluxes estimated using different methods. I attributed NPP components to standing biomass increment, detritus cycle and lateral export. The influence of environmental and physiological parameters on NEE, GPP and Reco was analyzed with a multiple regression model approach. I found that both NEP and GPP of the apple orchard were of similar magnitude to those of forests growing in similar climate conditions, while large differences occurred in the allocation pattern and in the fate of produced biomass. Apple production accounted for 49% of annual NPP, organic material (leaves, fine root litter, pruned wood and early fruit drop) contributing to detritus cycle was 46%, and only 5% went to standing biomass increment. The carbon use efficiency (CUE), with an annual average of 0.68 ± 0.10, was higher than the previously suggested constant values of 0.47-0.50. Light and leaf area index had the strongest influence on both NEE and GPP. On a diurnal basis, NEE and GPP reached their peak approximately at noon, while they appeared to be limited by high values of VPD and air temperature in the afternoon. The proposed models can be used to explain and simulate current relations between carbon fluxes and environmental parameters at daily and yearly time scale. On average, the annual NEP balanced the carbon annually exported with the harvested apples. These data support the hypothesis of a minimal or null impact of the apple orchard ecosystem on net C emission to the atmosphere.

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This thesis gives an overview of the history of gold per se, of gold as an investment good and offers some institutional details about gold and other precious metal markets. The goal of this study is to investigate the role of gold as a store of value and hedge against negative market movements in turbulent times. I investigate gold’s ability to act as a safe haven during periods of financial stress by employing instrumental variable techniques that allow for time varying conditional covariance. I find broad evidence supporting the view that gold acts as an anchor of stability during market downturns. During periods of high uncertainty and low stock market returns, gold tends to have higher than average excess returns. The effectiveness of gold as a safe haven is enhanced during periods of extreme crises: the largest peaks are observed during the global financial crises of 2007-2009 and, in particular, during the Lehman default (October 2008). A further goal of this thesis is to investigate whether gold provides protection from tail risk. I address the issue of asymmetric precious metal behavior conditioned to stock market performance and provide empirical evidence about the contribution of gold to a portfolio’s systematic skewness and kurtosis. I find that gold has positive coskewness with the market portfolio when the market is skewed to the left. Moreover, gold shows low cokurtosis with the market returns during volatile periods. I therefore show that gold is a desirable investment good to risk averse investors, since it tends to decrease the probability of experiencing extreme bad outcomes, and the magnitude of losses in case such events occur. Gold thus bears very important and under-researched characteristics as an asset class per se, which this thesis contributed to address and unveil.

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Dealing with latent constructs (loaded by reflective and congeneric measures) cross-culturally compared means studying how these unobserved variables vary, and/or covary each other, after controlling for possibly disturbing cultural forces. This yields to the so-called ‘measurement invariance’ matter that refers to the extent to which data collected by the same multi-item measurement instrument (i.e., self-reported questionnaire of items underlying common latent constructs) are comparable across different cultural environments. As a matter of fact, it would be unthinkable exploring latent variables heterogeneity (e.g., latent means; latent levels of deviations from the means (i.e., latent variances), latent levels of shared variation from the respective means (i.e., latent covariances), levels of magnitude of structural path coefficients with regard to causal relations among latent variables) across different populations without controlling for cultural bias in the underlying measures. Furthermore, it would be unrealistic to assess this latter correction without using a framework that is able to take into account all these potential cultural biases across populations simultaneously. Since the real world ‘acts’ in a simultaneous way as well. As a consequence, I, as researcher, may want to control for cultural forces hypothesizing they are all acting at the same time throughout groups of comparison and therefore examining if they are inflating or suppressing my new estimations with hierarchical nested constraints on the original estimated parameters. Multi Sample Structural Equation Modeling-based Confirmatory Factor Analysis (MS-SEM-based CFA) still represents a dominant and flexible statistical framework to work out this potential cultural bias in a simultaneous way. With this dissertation I wanted to make an attempt to introduce new viewpoints on measurement invariance handled under covariance-based SEM framework by means of a consumer behavior modeling application on functional food choices.

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Il progetto di ricerca che ho svolto in questi mesi si è focalizzato sull'integrazione dei risultati raggiunti grazie all'elaborazione di nuovi dati sperimentali. Questi sono stati prelevati dalla corteccia visiva di macachi, attraverso l'utilizzo di tecniche di registrazione elettro-fisiologiche mediante array di micro-elettrodi[25], durante la presentazionedi alcuni filmati (sequenze di immagini o frames). Attraverso la tecnica del clustering, dalle registrazioni degli esperimenti sono stati raggruppati gli spike appartenenti ad uno stesso neurone, sfruttando alcune caratteristiche come la forma del potenziale d'azione. Da questa elaborazione e stato possibile risalire a quali stimoli hanno prodotto una risposta neurale. I dati messi a disposizione da Ringach non potevano essere trattati direttamente con le tecniche della spike-triggered average e della spike-triggered covariance a causa di alcune loro caratteristiche. Utilizzando filtri di Gabor bidimensionali e l'energia di orientazione e stato pero possibile modellare la risposta di cellule complesse in corteccia visiva primaria. Applicare questi modelli su dati ad alta dimensionalita immagini molto grandi), sfruttando la tecnica di standardizzazione (Z-score), ha permesso di individuare la regione, la scala e l'orientazione all'interno del piano immagine dei profili recettivi delle cellule di cui era stata registrata l'attività neurale. Ritagliare tale regione e applicare la spike-triggered covariance su dati della giusta dimensionalita, permetterebbe di risalire ai profili recettivi delle cellule eccitate in un preciso momento, da una specifica immagine e ad una precisa scala e orientazione. Se queste ipotesi venissero confermate si potrebbe marcare e rafforzare la bontà del modello utilizzato per le cellule complesse in V1 e comprendere al meglio come avviene l'elaborazione delle immagini.

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In the first chapter, I develop a panel no-cointegration test which extends Pesaran, Shin and Smith (2001)'s bounds test to the panel framework by considering the individual regressions in a Seemingly Unrelated Regression (SUR) system. This allows to take into account unobserved common factors that contemporaneously affect all the units of the panel and provides, at the same time, unit-specific test statistics. Moreover, the approach is particularly suited when the number of individuals of the panel is small relatively to the number of time series observations. I develop the algorithm to implement the test and I use Monte Carlo simulation to analyze the properties of the test. The small sample properties of the test are remarkable, compared to its single equation counterpart. I illustrate the use of the test through a test of Purchasing Power Parity in a panel of EU15 countries. In the second chapter of my PhD thesis, I verify the Expectation Hypothesis of the Term Structure in the repurchasing agreements (repo) market with a new testing approach. I consider an "inexact" formulation of the EHTS, which models a time-varying component in the risk premia and I treat the interest rates as a non-stationary cointegrated system. The effect of the heteroskedasticity is controlled by means of testing procedures (bootstrap and heteroskedasticity correction) which are robust to variance and covariance shifts over time. I fi#nd that the long-run implications of EHTS are verified. A rolling window analysis clarifies that the EHTS is only rejected in periods of turbulence of #financial markets. The third chapter introduces the Stata command "bootrank" which implements the bootstrap likelihood ratio rank test algorithm developed by Cavaliere et al. (2012). The command is illustrated through an empirical application on the term structure of interest rates in the US.

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Forest models are tools for explaining and predicting the dynamics of forest ecosystems. They simulate forest behavior by integrating information on the underlying processes in trees, soil and atmosphere. Bayesian calibration is the application of probability theory to parameter estimation. It is a method, applicable to all models, that quantifies output uncertainty and identifies key parameters and variables. This study aims at testing the Bayesian procedure for calibration to different types of forest models, to evaluate their performances and the uncertainties associated with them. In particular,we aimed at 1) applying a Bayesian framework to calibrate forest models and test their performances in different biomes and different environmental conditions, 2) identifying and solve structure-related issues in simple models, and 3) identifying the advantages of additional information made available when calibrating forest models with a Bayesian approach. We applied the Bayesian framework to calibrate the Prelued model on eight Italian eddy-covariance sites in Chapter 2. The ability of Prelued to reproduce the estimated Gross Primary Productivity (GPP) was tested over contrasting natural vegetation types that represented a wide range of climatic and environmental conditions. The issues related to Prelued's multiplicative structure were the main topic of Chapter 3: several different MCMC-based procedures were applied within a Bayesian framework to calibrate the model, and their performances were compared. A more complex model was applied in Chapter 4, focusing on the application of the physiology-based model HYDRALL to the forest ecosystem of Lavarone (IT) to evaluate the importance of additional information in the calibration procedure and their impact on model performances, model uncertainties, and parameter estimation. Overall, the Bayesian technique proved to be an excellent and versatile tool to successfully calibrate forest models of different structure and complexity, on different kind and number of variables and with a different number of parameters involved.

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Ozon (O3) ist ein wichtiges Oxidierungs- und Treibhausgas in der Erdatmosphäre. Es hat Einfluss auf das Klima, die Luftqualität sowie auf die menschliche Gesundheit und die Vegetation. Ökosysteme, wie beispielsweise Wälder, sind Senken für troposphärisches Ozon und werden in Zukunft, bedingt durch Stürme, Pflanzenschädlinge und Änderungen in der Landnutzung, heterogener sein. Es ist anzunehmen, dass diese Heterogenitäten die Aufnahme von Treibhausgasen verringern und signifikante Rückkopplungen auf das Klimasystem bewirken werden. Beeinflusst wird der Atmosphären-Biosphären-Austausch von Ozon durch stomatäre Aufnahme, Deposition auf Pflanzenoberflächen und Böden sowie chemische Umwandlungen. Diese Prozesse zu verstehen und den Ozonaustausch für verschiedene Ökosysteme zu quantifizieren sind Voraussetzungen, um von lokalen Messungen auf regionale Ozonflüsse zu schließen.rnFür die Messung von vertikalen turbulenten Ozonflüssen wird die Eddy Kovarianz Methode genutzt. Die Verwendung von Eddy Kovarianz Systemen mit geschlossenem Pfad, basierend auf schnellen Chemilumineszenz-Ozonsensoren, kann zu Fehlern in der Flussmessung führen. Ein direkter Vergleich von nebeneinander angebrachten Ozonsensoren ermöglichte es einen Einblick in die Faktoren zu erhalten, die die Genauigkeit der Messungen beeinflussen. Systematische Unterschiede zwischen einzelnen Sensoren und der Einfluss von unterschiedlichen Längen des Einlassschlauches wurden untersucht, indem Frequenzspektren analysiert und Korrekturfaktoren für die Ozonflüsse bestimmt wurden. Die experimentell bestimmten Korrekturfaktoren zeigten keinen signifikanten Unterschied zu Korrekturfaktoren, die mithilfe von theoretischen Transferfunktionen bestimmt wurden, wodurch die Anwendbarkeit der theoretisch ermittelten Faktoren zur Korrektur von Ozonflüssen bestätigt wurde.rnIm Sommer 2011 wurden im Rahmen des EGER (ExchanGE processes in mountainous Regions) Projektes Messungen durchgeführt, um zu einem besseren Verständnis des Atmosphären-Biosphären Ozonaustauschs in gestörten Ökosystemen beizutragen. Ozonflüsse wurden auf beiden Seiten einer Waldkante gemessen, die einen Fichtenwald und einen Windwurf trennt. Auf der straßenähnlichen Freifläche, die durch den Sturm "Kyrill" (2007) entstand, entwickelte sich eine Sekundärvegetation, die sich in ihrer Phänologie und Blattphysiologie vom ursprünglich vorherrschenden Fichtenwald unterschied. Der mittlere nächtliche Fluss über dem Fichtenwald war -6 bis -7 nmol m2 s-1 und nahm auf -13 nmol m2 s-1 um die Mittagszeit ab. Die Ozonflüsse zeigten eine deutliche Beziehung zur Pflanzenverdunstung und CO2 Aufnahme, was darauf hinwies, dass während des Tages der Großteil des Ozons von den Pflanzenstomata aufgenommen wurde. Die relativ hohe nächtliche Deposition wurde durch nicht-stomatäre Prozesse verursacht. Die Deposition über dem Wald war im gesamten Tagesverlauf in etwa doppelt so hoch wie über der Freifläche. Dieses Verhältnis stimmte mit dem Verhältnis des Pflanzenflächenindex (PAI) überein. Die Störung des Ökosystems verringerte somit die Fähigkeit des Bewuchses, als Senke für troposphärisches Ozon zu fungieren. Der deutliche Unterschied der Ozonflüsse der beiden Bewuchsarten verdeutlichte die Herausforderung bei der Regionalisierung von Ozonflüssen in heterogen bewaldeten Gebieten.rnDie gemessenen Flüsse wurden darüber hinaus mit Simulationen verglichen, die mit dem Chemiemodell MLC-CHEM durchgeführt wurden. Um das Modell bezüglich der Berechnung von Ozonflüssen zu evaluieren, wurden gemessene und modellierte Flüsse von zwei Positionen im EGER-Gebiet verwendet. Obwohl die Größenordnung der Flüsse übereinstimmte, zeigten die Ergebnisse eine signifikante Differenz zwischen gemessenen und modellierten Flüssen. Zudem gab es eine klare Abhängigkeit der Differenz von der relativen Feuchte, mit abnehmender Differenz bei zunehmender Feuchte, was zeigte, dass das Modell vor einer Verwendung für umfangreiche Studien des Ozonflusses weiterer Verbesserungen bedarf.rn

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Background: fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG. Methodology/Principal Findings: In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band. Conclusions/Significance: Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.