This thesis investigates changes in temperature extremes between 1950-2005, analysing gridded data sets of observations and climate model simulations. It focuses on changes in the frequency of extreme temperatures occurring in single days or over periods of six or more consecutive days. The study aims to quantify the significance of changes in extreme temperature events and answer the following questions. Are external or human-induced forcings together with natural forcings responsible for the observed change in temperature extremes or can these changes be explained due to natural climate variability alone? Are the observed changes consistent with those from climate model simulations? And are the changes in extremes linked only to changes in the mean climate, or only to those in climate variability or both? The analysis concentrates on changes from global to regional scale and from annual mean to seasonal scale. A detection method is applied to assess if changes are significantly different with respect to the internal climate variability. Results show that there has been a significant increase in warm daily extremes and a decrease in cold ones, both on large and small spatial scales. The increase in warm extremes has been found to be highly correlated with the increase in mean temperature. The changes in daily extremes are well represented in climate model simulations. Changes in the persistent extremes show a detectable increase in the frequency of warm and a decrease in cold events and are reproducible by models.

Thesis (M.Sc. (Statistics )) -- University of Limpopo, 2021 / Temperature extremes have a crucial impact on agricultural, economic, health and energy sectors due to the occurrence of climate extreme events such as heat waves and cold waves. Limpopo province is among the hottest provinces of South Africa and experiences little rainfall which affect the water availabil ity, food production and biodiversity. In the Limpopo province, temperature extremes are expected to become more frequent as a result of climate change. The aim of this study was to model temperature extremes in the Limpopo province of South Africa using extreme value theory (EVT). The stationarity of the data was tested using augmented Dickey-Fuller (ADF), Phillips-Peron (PP) and Kwiatkowski-Phillips-Schmit-Shin (KPSS). Four candidate parent distri butions: normal, log-normal, gamma and Weibull distributions, were fitted to the average monthly maximum and minimum daily temperatures. Prior to the selection of the parent distributions, the data set at each station was subjected to normality test using the Shapiro-Wilk (SW) and Jarque-Bera (JB) tests. The stationarity and normality tests revealed that the maximum and minimum temperature data series at all the stations are neither stationary nor normally distributed. Akaike information criterion (AIC) and Bayesian information cri terion (BIC) were used to select the best fitting distribution at a particular site. The findings revealed that both maximum and minimum temperatures series at all the stations belong to the Weibull domain of attraction. The findings from the Mann-Kendall (M-K) test and time series plots trend analyses showed that there is a monotonic downward and upward long-term trend in minimum and maximum temperature data, respectively. Two fundamental approaches of EVT, block maxima and peaks-over-threshold (POT), were used in this dissertation. The generalised extreme value (GEV), generalised Pareto (GP) and Poisson point process distributions were fitted to the data set for each station. In order to account for climate change impact, non-stationary models were considered with Seasonal Oscillation Index (SOI) as covariates of the parameters of the GEV distribution. The findings revealed that both the maximum and minimum temperature data can be modelled by the Weibull family of distribution. The EVT return level analysis findings of above 400C for maximum temperature suggests impending heat waves and droughts in the Limpopo province. The bivariate conditional extremes ap proach with a time-varying threshold was used. The findings revealed both significant positive and negative extremal dependence in some pairs of meteo rological stations. The findings of this study play an important role in revealing information useful to meteorologists, climatologists, agriculturalists and plan ners in the energy sector where temperature extremes play an important role. The scientific contribution of this study was to reduce the risk and impact of temperature extremes on agricultural, energy and health sectors in the Limpopo province. An understanding of temperature extremes will help gov ernment and other stakeholders to formulate mitigation strategies that will minimise the negative impact resulting from temperature extremes in the Limpopo province. Among the major contributions of the study was the use of a pe nalised cubic smoothing spline to perform a nonlinear detrending of the tem perature data, before fitting bivariate time-varying threshold excess models based on Laplace margins, to capture the climate change effects in the data. Future studies may consider exploring the use of extreme value copulas, as well as spatio-temporal dependence between temperature extremes using the conditional extremes model of Heffernan and Tawn (2004). / DST National Research Foundation (NRF)

[pt] Quando ocorre algum tipo de falta em uma Linha de Transmissão (LT), sua localização exata é essencial para uma rápida recomposição do Sistema Elétrico. Métodos que utilizam tensão e corrente de apenas um terminal contêm simplificações que podem acarretar erros. Esta dissertação investiga a aplicação de Redes Neurais Artificiais (RNA) na obtenção de uma nova forma de identificar o tipo do curto- circuito e determinar a sua localização, utilizando dados obtidos em somente um terminal. O trabalho consiste de 4 partes principais: estudo bibliográfico da área de Redes Neurais; simulações de faltas para a obtenção de padrões; definição e implementação dos modelos de Redes Neurais para identificação e localização da falta; e estudos de casos. Na fase do estudo bibliográfico sobre RNA, foi verificado que as topologias de redes mais usuais são as Feed Forward, que podem ter uma ou mais camadas de Elementos Processadores (EP), sendo as redes com múltiplas camadas (Multi-Layer) a configuração mais completa. Para treinamento da rede, o algoritmo de aprendizado mais utilizado é o Back Propagation. Como fruto deste estudo bibliográfico é apresentado neste trabalho um resumo sobre RNA. Nas simulações de faltas para obtenção dos padrões de treinamento e teste, foi utilizado um sistema automático que, através da combinação de vários parâmetros do sistema elétrico, gera situações diferentes de falta. Este sistema utiliza como base o programa Alternative Transient Program -ATP. Neste trabalho o sistema elétrico está representado por uma LT de 345 KV, com fontes equivalentes representando um sistema real de Furnas Centrais Elétricas. Todos o sinais de tensão e corrente utilizados são representados por fasores de 60 Hz, obtidos através da Transformada Discreta de Fourier (TDF). Os modelos de RNAs para identificação e localização de falta foram implementados com sub-rotinas de redes neurais do programa MATLAB ver. 6.0, representados por Redes Perceptron Multicamadas (Multi Layer Perceptron), treinadas com algoritmo Back Propagation com taxa de aprendizado adaptativa e o termo momentum fixo. Um modelo único de RNA identifica quais as fases (A, B, C e T) envolvidas, classificando o tipo de falta, que pode ser: Monofásica; Bifásica; Bifásica-Terra ou Trifásica. Para a localização da falta, foram definidas 4 arquiteturas de RNA, uma para cada tipo de falta. A ativação de cada topologia de RNA para localização é definida em função do tipo de falta classificada no modelo de identificação com RNA. Na etapa de estudo de casos testou-se o desempenho de cada modelo de RNA utilizando casos de testes em outras situações de falta, diferentes dos conjuntos de treinamento. A RNA de identificação de falta foi avaliada para situações de faltas envolvendo outras LTs, com diferentes níveis de tensão. Os resultados das 4 RNAs de localização da falta foram comparados com os resultados obtidos utilizando o método tradicional, tanto para os casos simulados quanto para algumas situações reais de falta. A utilização de RNAs para a identificação e a localização de falta mostrouse bastante eficiente para os casos analisados, comprovando a aplicabilidade das redes neurais nesse problema. / [en] When a kind of fault occurs in a Transmission Line, its exact location is essential for a fast reclosing of the Electric System. Methods that use voltages and currents from only one terminal contain simplifications that can to cause mistakes. This paper presents an investigation about application of Artificial Neural Network (ANN) obtaining a new way of identification for the type of the short circuit and its location, using data obtained only in one terminal. The work consists on the following 4 main parts: bibliographical study of Neural Network`s area; simulations of faults in order to obtain of patterns; definition and implementation of Neural Network`s models for identification and location of the fault; and studies of cases. In the bibliographical study step on ANN, it was verified that the topologies for the more usual nets are Feed-

In this project warm extreme temperature events in the Arctic region during the spring months March, April and May were identified and analysed. In the analysis daily average NCEP reanalysis data from NOAA/OAR/ESRL PSL format was used. The extreme events were retrieved as the highest positive temperature anomalies from the climatological mean, and the synoptic scale plots for the 50 most extreme events were created to identify what patterns caused the extreme warming over the Polar region. By contouring the areas of statistical significance, the regions with a reoccuring pattern were identified. The results conclude that cyclonic activity over the high Arctic extending down over Greenland and northern Canada combined with anomalously high geopotential height over the north Pacific ocean, over the Arctic, and towards Siberia cause the high temperatures over the pole. A weaker Polar Vortex causes perturbations in the jet stream, ridges in these Rossby waves can act as a pathway for warm and moist air from the oceanic regions which has a warming effect in the Arctic. Further analysis can be done to investigate what teleconnections these spring-time extreme events have on a global scale. / Under detta projekt har extremt varma temperaturevent i Arktisområdet under vårmånaderna Mars, April och Maj identifierats samt analyserats, genom att använda daglig medelvärdes NCEP reanalys data från NOAA/OAR/ESRL PSL i NetCDF format. De extrema händelserna identifierades genom att ta de största positiva temperaturavvikelserna från ett klimatologiskt medelvärde, storskaliga avvikelseplottar skapades för de 50 mest extrema händelserna för att kunna identifiera meteorologiska mönster som ovanligt varma Arktisdagar. De områden med mest återkommande mönsterna var statistiskt signifikanta  och markerades med svarta konturer. Resultaten visar att lågtrycksaktivitet i Arktis som sträcker sig ner över Grönland samt norra Kanada kombinerat med höga geopotentialhöjdavvikelser över Stilla havet och Sibirien som sträcker sig upp mot Nordpolen orsakar ovanligt höga temperaturer i Arktis. En svag polarvirvel orsakar störningar i jetströmmen, dessa ryggar i jetströmmen kan transportera varm fuktig luft från haven mot polen vilket kan ha en värmande effekt. Vidare forskning kan utföras för att identifiera de exakta kopplingarna och konsekvenserna som dessa varma extrema Arktishändelser har globalt.

Between July 2009 and October 2011, a new habitat was found for a rarely reported freshwater dinoflagellate species, Thompsodinium intermedium - Comal Springs (Comal County), Texas. In 2011, diel in-situ monitoring in monospecific blooms of this species revealed previously undetected negative impacts on endangered species habitat availability associated with conditions of low flow levels, recorded at the U.S. Geological Survey gage # 08169000 on Texas Commission on Environmental Quality river segment 1811 station 12655. During a period of low springflow in the summer of 2011, late afternoon and early morning measurements of dissolved oxygen and temperature and presence of dinoflagellate blooms were monitored at six sites. Significant differences in diel fluctuations were found in all of these parameters among sites with and without the planktonic blooms. These fluctuations increased risk of hypoxia and hyperthermia conditions at sites of planktonic bloom events. Arrays of in-situ continuous monitoring temperature/light probes were used inside and outside of blooms. Wildlife and human health implications are that hypoxia and hyperthermia are known to promote conditions favorable to harmful microbes which may be transported from springs to coastal bays. In-situ data demonstrated that T. intermedium blooms, hypoxia, and hyperthermia occurred in the upper Comal headwaters. These natural environmental stressors may be avoidable if adequate springflows are maintained to buffer against these impacts.