1,720,975 research outputs found
Global assessment of trends in wetting and drying over land
No full textChanges in the hydrological conditions of the land surface have substantial impacts on society1, 2. Yet assessments of observed continental dryness trends yield contradicting results3, 4, 5, 6, 7. The concept that dry regions dry out further, whereas wet regions become wetter as the climate warms has been proposed as a simplified summary of expected8, 9, 10 as well as observed10, 11, 12, 13, 14 changes over land, although this concept is mostly based on oceanic data8, 10. Here we present an analysis of more than 300 combinations of various hydrological data sets of historical land dryness changes covering the period from 1948 to 2005. Each combination of data sets is benchmarked against an empirical relationship between evaporation, precipitation and aridity. Those combinations that perform well are used for trend analysis. We find that over about three-quarters of the global land area, robust dryness changes cannot be detected. Only 10.8% of the global land area shows a robust ‘dry gets drier, wet gets wetter’ pattern, compared to 9.5% of global land area with the opposite pattern, that is, dry gets wetter, and wet gets drier. We conclude that aridity changes over land, where the potential for direct socio-economic consequences is highest, have not followed a simple intensification of existing patterns
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Past as a type case - a combinatorial approach for regional climate simulations
No full textGrundlage aller Abschätzungen der Folgen eines sich ändernden Klimas sind Untersuchungen der klimatischen Entwicklung selbst, gestützt entweder auf die Messdaten der Beobachtungsperiode oder auf Simulationen, etwa wenn zukünftige Entwicklungen interessieren. Diese können von Region zu Region sehr unterschiedlich ausfallen, so dass
räumlich hoch aufgelöste Simulationen benötigt werden, möchte man den lokalen Eigenheiten des Klimawandels gerecht werden. Üblicherweise werden dazu die nur grob aufgelösten Simulationen eines Allgemeinen Zirkulationsmodells Zeitschritt für Zeitschritt auf eine feinere Auflösung übertragen, sei es dynamisch durch eine räumlich und zeitlich feiner aufgelöste Abbildung der physikalischen Prozesse, oder statistisch, indem während der Beobachtungsperiode festgestellte Zusammenhänge zwischen einzelnen Zustandsvariablen des Wettergeschehens verwendet werden.
Gegenstand dieser Arbeit ist ein solches statistisches Verfahren, das auf täglichen Stationsdaten der untersuchten Region basiert. Es liefert Simulationsreihen für diese Stationen, wiederum mit täglicher Auflösung. Eine wichtige Besonderheit des Verfahrens ist, dass die Simulationsreihen lediglich durch die Parameter einer linearen Regressionsgeraden beschränkt werden, die die lineare zeitliche Entwicklung einer ausgewählten Klimavariable (üblicherweise die Temperatur) beschreiben. Die Simulationsreihen werden dabei aus Beobachtungen der Beobachtungsperiode zusammengesetzt, so dass die
Neuaneinanderreihung der Beobachtungen eine Entwicklung der
ausgewählten Variable ergibt, die der vorgegebenen Regressionsgeraden entspricht. Da die Beobachtungen neben der ausgewählten Variable auch alle anderen erfassten Messgrößen enthalten und diese bei der Neuaneinanderreihung mitgeführt werden, bestehen die Reihen aus einzelnen Elementen, die physikalisch konsistent sind. Das gleiche
gilt für die Konsistenz der räumlichen Verteilungen, da die
Reihenfolge der Neuaneinanderreihung an allen Stationen die selbe ist. Heuristische Kriterien stellen außerdem sicher, dass die Simulationsreihen realistische Jahresgänge und Erhaltungsneigung aufweisen. Stochastische Elemente des Verfahrens ermöglichen die Erzeugung ganzer Simulationsensembles.
Ein Kreuzvalidierungsexperiment mit Daten des Elbeeinzugsgebiets zeigt, dass das Verfahren in der Lage ist, eine beobachtete Klimatologie realistisch zu reproduzieren. Dazu wird ein Datensatz mit täglichen Beobachtungen in zwei unabhängige Teilperioden zerlegt. Mit
den Beobachtungen der ersten wird die Klimatologie der zweiten simuliert, wobei die lineare Entwicklung der Temperatur als ausgewählter Klimavariable in der zweiten Periode vorgegeben wird. Simulation und Beobachtung können so für die zweite Periode direkt verglichen werden. Neben klassischen Statistiken wie Mittelwert und Streuung werden Erhaltungsneigung und Extremereignisse in befriedigender Übereinstimmung wiedergegeben, auch für die häufig kritische Variable des Niederschlags. Der Vergleich mit einem ähnlichen Experiment mit einem dynamischen Modell zeigt, dass der statistische Ansatz eine erheblich bessere Übereinstimmung mit den Beobachtungen erbringt.
Die Anwendung des Verfahrens auf die klimatische Zukunft des
Elbegebiets zeigt mit der vorgegebenen Erwärmung einhergehend eine leichte Abnahme der Niederschläge, die vor allem im Sommer stattfindet. Für die Winter hingegen lassen sich unveränderte bis leicht zunehmende Niederschläge feststellen. Durch die mit der Erwärmung verbundene zunehmende Verdunstung ist allerdings mit einer
deutlich stärkeren Austrocknung zu rechnen, als es die Entwicklung der Niederschläge allein nahelegt.
Insgesamt erlaubt dieses Verfahren, schnell zu akkuraten Abschätzungen regionaler Klimaentwicklungen zu gelangen, die als fundierter Ausgangspunkt für Klimafolgeuntersuchungen dienen können.Every impact analysis of a changing climate first requires an understanding of the climate development, based on observations or, if future developments are considered, simulations. Climate change varies enormously among different regions. In order to take its local
features into account properly, simulations at high resolution are necessary. Typically, to this end coarse resolution simulations from a General Circulation Model are translated to finer spatial scales. This can be achieved by a finer (both in space and in time) representation of physical processes - the dynamical approach -, or by making use of statistical relationships detected during an observation period between different climatological variables.
Subject of this thesis is such a statistical approach, which is based on daily meteorological station data from a region of interest. It generates simulated series of daily observations for each of the concerned stations. As an important feature, the simulated series are only constrained by the parameters of a linear regression line, which
describes the linear development of a chosen characteristic climate variable (usually temperature). Simulated series are assembled from segments of the observed series, such that the rearrangement of the observations yields series corresponding to the prescribed regression line. As the observations consist not only of the chosen variable but
comprise all of the measured variables, which are rearranged as well, the simulated series consist of elements which are physically consistent. The same holds for the spatial distributions, as the order of the rearrangement is identical at all stations. A set of heuristic rules makes sure that the simulated series exhibit realistic annual
cycles and persistence. Stochastic elements enable the generation of ensembles.
A cross validation experiment using data from the Elbe river basin proves the approach to be able to reproduce an observed climatology. To this end, a dataset of daily observations is split into two independent sub periods. Based on the data from the first, the climatology of the second is simulated, prescribing the linear temperature development from the second. Thereby, simulations and
observations from the second period can be compared. Besides classic statistics such as mean or variance, persistence features and extreme events are well reproduced, even for precipitation. The comparison with a similar experiment, for which a dynamical model is used, shows that the statistical approach yields a much closer match between simulated and observed data.
Using this approach to simulate the climatological future of the Elbe river basin gives slightly decreasing precipitation, corresponding to a prescribed warming. This decrease mainly happens during the summer, while winter experiences unchanged or even increased precipitation. However, as with temperature also evaporation increases, a drier climate is to be expected.
To conclude, this approach allows for quick and accurate simulations of regional climate simulations, which may serve as a sound starting point for climate impact investigations
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