Brazil has majority share of hydroelectric power in its generation matrix electricity, depending on the rainy seasons and settlement of multiannual reservoirs to ensure safety in the generation and distribution system power national power. In this context, time series analysis can provide information useful for planning and more efficient operation of the reservoirs, identifying possible trends in the flow of data or precipitation of the integrated stations hydroelectric plants. The trend analysis in hydrological series is held traditionally using non-parametric tests such as the Mann-Kendall test, and You can treat both monotonic trends or abrupt as in various ways. Such tests, although not require information about the distribution of data, require data to be independent, which rarely occurs in series hydrologic variables. Thus, new tests have arisen in the trend analysis, with objective of taking the autocorrelation of the series under consideration. This paper proposes trend analysis of historical streamflow series of major basins contribute to the National Interconnected System (SIN), obtained from data National Electric System Operator (ONS). The analysis will aim check importance of considering the autocorrelation of the data to detect trends, analyzing the first series without considering the autocorrelation - the nonparametric method Traditional Mann-Kendall with Sen estimator - and then applying filters Pre-Whitening and Trend-Free Pre-Whitening. The tests were implemented through the MatLab program to analyze the average natural flow monthly provided by the ONS. The spatial distribution of trends found It was presented through maps created the Free Software QGIS. The results found for the ONS posts corroborated the theory that the autocorrelation influences the detection of trends. Keywords: autocorrelation, trend analysis, hydropower se / O Brasil possui participaÃÃo majoritÃria de energia hidroelÃtrica em sua matriz de geraÃÃo de energia elÃtrica, dependendo das estaÃÃes chuvosas e da regularizaÃÃo plurianual dos reservatÃrios para garantir a seguranÃa na geraÃÃo e distribuiÃÃo de energia do sistema elÃtrico nacional. Nesse contexto, a anÃlise de sÃries temporais pode oferecer informaÃÃes Ãteis para o planejamento e a operaÃÃo mais eficientes dos reservatÃrios, identificando possÃveis tendÃncias nos dados de vazÃo ou de precipitaÃÃo dos postos integrados Ãs usinas hidrelÃtricas. A anÃlise de tendÃncias em sÃries hidrolÃgicas à realizada tradicionalmente atravÃs de testes nÃo-paramÃtricos, como o teste de Mann-Kendall, e pode tratar tanto de tendÃncias monotÃnicas, quanto de abruptas ou de formas diversas. Esses testes, apesar de nÃo requererem informaÃÃes sobre a distribuiÃÃo dos dados, requerem que os dados sejam independentes, o que raramente ocorre nas sÃries de variÃveis hidrolÃgicas. Assim, novos testes surgiram na anÃlise de tendÃncias, com o objetivo de levar a autocorrelaÃÃo das sÃries em consideraÃÃo. O presente trabalho propÃe a anÃlise de tendÃncias das sÃries histÃricas de vazÃes das principais bacias que contribuem para o Sistema Interligado Nacional (SIN), obtidas a partir de dados do Operador Nacional do Sistema ElÃtrico (ONS). A anÃlise terà por objetivo verificar a importÃncia da consideraÃÃo da autocorrelaÃÃo dos dados na detecÃÃo de tendÃncias, analisando as sÃries primeiramente sem considerar a autocorrelaÃÃo - pelo mÃtodo nÃoparamÃtrico tradicional de Mann-Kendall com estimador de Sen - e, em seguida, aplicando os filtros do Pre-Whitening e do Trend-Free Pre-Whitening. Os testes foram implementados atravÃs do programa MatLab, para anÃlise das vazÃes naturais mÃdias mensais disponibilizadas pelo ONS. A distribuiÃÃo espacial das tendÃncias encontradas foi apresentada atravÃs de mapas criados no software livre QGIS. Os resultados encontrados para os postos do ONS corroboraram a teoria de que a autocorrelaÃÃo influencia na detecÃÃo de tendÃncias.

Conforme o volume e a diversidade de informações científicas aumentam, se torna necessário entender o que, porque e como esse aumento acontece. Estratégias e políticas públicas podem se desenvolver a partir dessas informações potencializando os serviços de educação e inovação oferecidos à sociedade. A análise de tendências é um dos passos nessa direção. Este trabalho no entanto vai além de considerar apenas o conteúdo das informações analisadas incluindo a estrutura das fontes geradoras das informações, ou seja, as redes sociais, como uma dimensão adicional para modelar e predizer tendências ao longo do tempo. Os experimentos foram realizados com os títulos das publicações de todos os doutores brasileiros da área de Ciência da Computação. Os resultados mostraram que a incorporação das medidas oriundas da análise de redes sociais reduziram os erros de predição, na média, para cerca de 18% daqueles produzidos sem a utilização destas medidas. Adicionalmente, esta incorporação permitiu que previsões mais futuras fossem realizadas sem grandes aumentos no erro destas previsões / As scientific information volume and diversity grow, the understanding of what, why and how it happens become necessary. Strategies and public politcs can be developed from these informations to power innovation and education services offered to society. Trend analysis is one of the steps in this direction. This work however goes beyond of just anlyse information content, it includes the information about the information sources structures, in other words, the social networks, as another dimension to model and predict trends through time. The experiments were made with the publication titles of all Brazilian Computer Science`s PHds. The results indicate the use o social network analysis metrics reduced the precision errors to about 18% of the errors produced without considering these metrics

abstract: The Western Continental United States has a rapidly changing and complex ecosystem that provides valuable resources to a large portion of the nation. Changes in social and environmental factors have been observed to be significantly correlated to usable ground and surface water levels. The assessment of water level changes and their influences on a semi-national level is needed to support planning and decision making for water resource management at local levels. Although many studies have been done in Ground and Surface Water (GSW) trend analysis, very few have attempted determine correlations with other factors. The number of studies done on correlation factors at a semi-national scale and near decadal temporal scale is even fewer. In this study, freshwater resources in GSW changes from 2004 to 2017 were quantified and used to determine if and how environmental and social variables are related to GSW changes using publicly available remotely sensed and census data. Results indicate that mean annual changes of GSW of the study period are significantly correlated with LULC changes related to deforestation, urbanization, environmental trends, as well as social variables. Further analysis indicates a strong correlation in the rate of change of GSW to LULC changes related to deforestation, environmental trends, as well as social variables. GSW slope trend analysis also reveals a negative trend in California, New Mexico, Arizona, and Nevada. Whereas a positive GSW trend is evident in the northeast part of the study area. GSW trends were found to be somewhat consistent in the states of Utah, Idaho, and Colorado, implying that there was no GSW changes over time in these states. / Dissertation/Thesis / Masters Thesis Geography 2018

Conforme o volume e a diversidade de informações científicas aumentam, se torna necessário entender o que, porque e como esse aumento acontece. Estratégias e políticas públicas podem se desenvolver a partir dessas informações potencializando os serviços de educação e inovação oferecidos à sociedade. A análise de tendências é um dos passos nessa direção. Este trabalho no entanto vai além de considerar apenas o conteúdo das informações analisadas incluindo a estrutura das fontes geradoras das informações, ou seja, as redes sociais, como uma dimensão adicional para modelar e predizer tendências ao longo do tempo. Os experimentos foram realizados com os títulos das publicações de todos os doutores brasileiros da área de Ciência da Computação. Os resultados mostraram que a incorporação das medidas oriundas da análise de redes sociais reduziram os erros de predição, na média, para cerca de 18% daqueles produzidos sem a utilização destas medidas. Adicionalmente, esta incorporação permitiu que previsões mais futuras fossem realizadas sem grandes aumentos no erro destas previsões / As scientific information volume and diversity grow, the understanding of what, why and how it happens become necessary. Strategies and public politcs can be developed from these informations to power innovation and education services offered to society. Trend analysis is one of the steps in this direction. This work however goes beyond of just anlyse information content, it includes the information about the information sources structures, in other words, the social networks, as another dimension to model and predict trends through time. The experiments were made with the publication titles of all Brazilian Computer Science`s PHds. The results indicate the use o social network analysis metrics reduced the precision errors to about 18% of the errors produced without considering these metrics

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This study is the first in a series of planned studies on the application of multidimensional visualization of business information and data within the context of accounting. The study's research question is: When is multidimensional visualization of information a better problem representation, improving both the effectiveness and efficiency of a spatial accounting judgment? To examine when multidimensional visualization can assist auditors in configural cue pattern recognition, the study employs the traditional DuPont analysis as the three pieces of key information to be represented on the X, Y, and Z axes of a single 3-D perspective display. To help determine when use of 3-D perspective display is beneficial in combining pieces of information, I rely on Vessey's (1991) Cognitive Fit Theory, and the Proximity Compatibility Principle (PCP) proposed by Wickens and Carswell (1995). The study has two hypotheses. Hypothesis H1 predicted that participants viewing a set of 2-D displays will be the most effective or most efficient in generating hypotheses for what caused the changes in the trend of accounting data or in estimating values. Hypothesis H2 predicted that participants viewing a single 3-D perspective display will be the most effective or most efficient in recognizing patterns of accounting data or in generating hypotheses for what caused the emerged pattern. To test the hypotheses of the study a 3 x 2 between-subjects design (display format x task) is used. The independent variables are display types and task types. Graphical display was manipulated at three levels: no graphical display (table only), 2-D display, and 3-D perspective display. Task was manipulated at two levels: trend analysis and pattern recognition task. The need for a fit between different types of spatial tasks and display formats is demonstrated by the findings of this study: 1) that 2-D displays appear to be more suitable for spatial tasks involving the generation of hypotheses for causes of trends in accounting data, while 2) 3-D perspective displays appear to be more suitable for spatial tasks involving pattern recognition in accounting data.

High resolution solar spectra are routinely recorded since more than two decades by the University of Liège at the International Scientific Station of the Jungfraujoch (Swiss Alps, 46.5°N, 8.0°E, 3580 m asl) with Fourier Transform Infrared (FTIR) spectrometers. Over the last years, major improvements have been implemented in the algorithms used to retrieve the abundances of atmospheric constituents accessible to the FTIR technique. Now, in addition to total column, information on the vertical distribution of the target gas is often available, allowing producing partial column data sets. We take advantage of these improvements to generate and characterize long-term total and partial columns time series of some important inorganic fluorinated trace gases deduced from FTIR measurements performed at Jungfraujoch. First, our investigations on hydrogen fluoride (HF) indicate that the adoption of a Galatry line shape model for this species significantly improves the fitting quality of the retrievals. The sensitivity of our FTIR measurements to HF inversions in three distinct regions that range from the low to the high stratosphere is confirmed thanks to products derived from two satellites and from two numerical models. We further demonstrate that the HF seasonal variations that occur in the low stratosphere are mainly responsible for the seasonal cycle observed in our HF total column time series. We have also developed an original multi-spectrum multi-window retrieval strategy allowing to derive, for the first time, some information on the vertical distribution of carbonyl fluoride (COF2) from ground-based FTIR measurements. After comparison with model data, our COF2 and HF FTIR datasets are combined to assess the atmospheric inorganic fluorine burden Fy. A trend analysis of our HF, COF2 and Fy time series is then performed for four different time periods spanning the 1985-2010 time interval. While we observe a recent stabilization for HF, corresponding COF2 data show a significant rise, after a period of significant reduction in its accumulation rate. This is probably ascribable to the combination of the decrease of its main source gas CFC-12 with the increase of the substitute product HCFC-22. However, this increase in the COF2 rate of change does not significantly impact the Fy trend, which is essentially driven by the change in HF. In addition, we show that the partitioning between the two major fluorine reservoirs HF and COF2 has not changed since the beginning of this century. Together, they account for around 95% of total inorganic fluorine in the atmosphere. Finally, we study the long-term evolution of carbon tetrafluoride (CF4), for the first time from ground-based FTIR measurements. The trend analysis of our time series indicates a slowing, initiated during the nineties, in the CF4 growth rate despite the fact that the absolute loading of this compound is still increasing. Our linear accumulation rates are consistent with those deduced from space or surface measurements.

This study analyzes the trends in temperature, and their extremes, in the Greater Toronto Area (GTA) in the context of urban heat island. The trends in annual and seasonal temperature changes were investigated in the GTA over the past century and a half with special focus on 1970-2000. The Mann-Kendall test is used to assess the significance of the trends and the Theil-Sen slope estimator is used to identify their magnitude. Statistically significant increasing trends for mean and minimum temperatures are observed mainly at the urban and suburban stations. The sequential Mann-Kendall test is used to identify any abrupt change in the time series of temperature (31 -161 years), and the results indicate that increasing trend for annual mean temperature has started after 1920 at Toronto downtown, after the 1960s at the suburban stations, and has increased significantly during the 1980s at all stations, which is consistent with the pace of urbanization during these periods in the GTA. The observed urban heat island (UHI) in Toronto is quantified and characterized by considering three different rural stations. The UHI intensity (∆Tu-r) in Toronto is categorized as winter dominating or summer dominating depending on the choice of a rural station. The results from the trend analysis of annual and seasonal ∆Tu-r suggest that the choice of the rural station is crucial in the estimation of ∆Tu-r, and thus can overestimate or underestimate its prediction depending on the location and topographical characteristics of a rural station relative to the urban station. The trends in extreme temperature indices are also investigated and the results indicate that indices based on daily maximum temperature are more pronounced at the urban and suburban stations compared to that at the rural stations. The changes in the trends for extreme indices based on daily minimum temperature are consistent at all stations for the period of 1971-2000. With the decrease in the percentage of cold nights and the increase in the percentage of warm nights, the diurnal temperature range has decreased throughout the GTA region. The analysis of heating degree days and cooling degree days revealed that the former is associated with decreasing trends and the latter exhibited increasing trends at almost all stations in the GTA. Finally, it is evident from the results that urban heat island phenomenon exerts warmer influence on the climate in cities, and with the current pace of urbanization in the GTA, it is imperative to understand the potential impact of the emerging UHI on humans and society.

In this thesis, 41 years of bright sunshine duration (SD) data of 192 meteorological stations in Turkey were analyzed. The main objective is to determine the trends of SD data and the importance of such analyses is the high correlation between SD data and solar irradiation reaching the surface of the earth. Because of the missing value problems, only the data set for 72 stations were examined. After imputing missing values of these stations by expectation maximization algorithm, to test the homogeneity Kruskal Wallis test (K-W) and Wald-Wolfowitz runs test for randomness were applied. Only 36 of the stations passed from these homogeneity tests therefore, trend analysis was carried out for these locations. To exclude the data sets which did not pass from the tests was important to reach more accurate trend analysis of the data in hand. Results of the trend analysis showed that the change of SD over the 41 years are in agreement with the globally identified surface solar radiation dimming and brightening time periods all over the world. The dimming period is mainly between the years 1970 and about 1990 while the brightening period is from about 1990 to 2010. The yearly averages of SD data sets of 27 locations out of 36, for the years in the dimming period, were in a good agreement with the global dimming trends. However, for the brightening period the agreement was not as clear as it was in the dimming period. Nevertheless, during the brightening period, the data set of most of the locations had zero trends or noticeably reduced rates of decrease of SD. The dimming might be attributed to the increase in air pollution and this might be an indication of human induced climate change. Larger amounts of negative trends during winter months supported this conclusion. However, to reach a concrete conclusion more accurate of different climatic parameters should be analyzed. Satellites images may be helpful for further clarifications of such conclusions on climate change issues.

This study analyzes the trends in temperature, and their extremes, in the Greater Toronto Area (GTA) in the context of urban heat island. The trends in annual and seasonal temperature changes were investigated in the GTA over the past century and a half with special focus on 1970-2000. The Mann-Kendall test is used to assess the significance of the trends and the Theil-Sen slope estimator is used to identify their magnitude. Statistically significant increasing trends for mean and minimum temperatures are observed mainly at the urban and suburban stations. The sequential Mann-Kendall test is used to identify any abrupt change in the time series of temperature (31 -161 years), and the results indicate that increasing trend for annual mean temperature has started after 1920 at Toronto downtown, after the 1960s at the suburban stations, and has increased significantly during the 1980s at all stations, which is consistent with the pace of urbanization during these periods in the GTA. The observed urban heat island (UHI) in Toronto is quantified and characterized by considering three different rural stations. The UHI intensity (∆Tu-r) in Toronto is categorized as winter dominating or summer dominating depending on the choice of a rural station. The results from the trend analysis of annual and seasonal ∆Tu-r suggest that the choice of the rural station is crucial in the estimation of ∆Tu-r, and thus can overestimate or underestimate its prediction depending on the location and topographical characteristics of a rural station relative to the urban station. The trends in extreme temperature indices are also investigated and the results indicate that indices based on daily maximum temperature are more pronounced at the urban and suburban stations compared to that at the rural stations. The changes in the trends for extreme indices based on daily minimum temperature are consistent at all stations for the period of 1971-2000. With the decrease in the percentage of cold nights and the increase in the percentage of warm nights, the diurnal temperature range has decreased throughout the GTA region. The analysis of heating degree days and cooling degree days revealed that the former is associated with decreasing trends and the latter exhibited increasing trends at almost all stations in the GTA. Finally, it is evident from the results that urban heat island phenomenon exerts warmer influence on the climate in cities, and with the current pace of urbanization in the GTA, it is imperative to understand the potential impact of the emerging UHI on humans and society.