Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-12-13T13:19:19Z No. of bitstreams: 1 cleberribeirojunior.pdf: 608218 bytes, checksum: c34a3b5f0b5767d1f66c9a1cc5ee7ca5 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-12-15T14:10:28Z (GMT) No. of bitstreams: 1 cleberribeirojunior.pdf: 608218 bytes, checksum: c34a3b5f0b5767d1f66c9a1cc5ee7ca5 (MD5) / Made available in DSpace on 2016-12-15T14:10:28Z (GMT). No. of bitstreams: 1 cleberribeirojunior.pdf: 608218 bytes, checksum: c34a3b5f0b5767d1f66c9a1cc5ee7ca5 (MD5) Previous issue date: 2008-02-15 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / As vespas sociais são insetos predadores que se alimentam, principalmente de lagartas, o que revela seu potencial para o controle biológico. A primeira etapa de programas de manejo integrado de pragas é conhecer as espécies em determinado sistema. Portanto o objetivo desse estudo foi identificar as espécies de vespas sociais capturadas em eucaliptal e testar diferentes métodos de amostragem. Doze coletas mensais foram realizadas em plantio de eucalipto em Coronel Pacheco, Minas Gerais, com duas metodologias: busca ativa nas trilhas e plantio (vespas coletadas com rede entomológica) e armadilhas atrativas (feitas com garrafas “pet” de dois litros, com aberturas laterais) presas ao tronco do eucalipto a aproximadamente 1,5m de altitude do solo. Nestas eram colocados 150ml de substrato atrativo: suco de maracujá ou goiaba (fruta, água e açúcar) ou caldo de sardinha (sardinha em conserva e água). Trinta e seis garrafas (12 garrafas por substrato) foram utilizadas por coletas em quatro transectos retilíneos de 80m (T1 e T2- mata-eucalipto; T3 e T4- eucaliptal), cada um contendo nove armadilhas, distantes 10 metros. Os dados climáticos (temperatura e precipitação) foram obtidos na estação meteorológica da EMBRAPA Gado e Leite em Coronel Pacheco, Minas Gerais. Doze espécies de vespas sociais de seis gêneros foram identificadas. Por meio da busca ativa 10 espécies foram coletadas e as armadilhas seis. A busca ativa registrou a maior diversidade (H’= 0,83), seguida das armadilhas atrativas com maracujá (H’= 0,38), goiaba (H’= 0,29) e sardinha (H’= 0,18). O índice de eficiência foi maior com a xvi busca ativa (83,3%). O número de espécies foi maior nos transectos de borda (T1 e T2), próximos à mata nativa. Cinqüenta por cento das espécies coletadas foram acidentais, 33% acessórias e 17% constantes. As espécies de vespas sociais não apresentaram correlação com as variáveis climáticas (temperatura e precipitação), mas espécies enxameantes foram registradas durante todos os meses de coleta e aquelas de fundação independente não foram obtidas nos meses de janeiro e junho. A melhor metodologia é a combinação de duas ou mais técnicas, pois nenhum método registrou sozinho todas espécies de vespas. A manutenção de faixas de vegetação nativas nas proximidades do eucalipto, como uma estratégia de favorecer a ocorrência natural das espécies de vespas sociais é importante. Todas as estações do ano são favoráveis para o manejo de vespas sociais em eucaliptais, principalmente, para espécies enxameantes. / The social wasps are predators of many insect species, mainly caterpillars, which shows their potential for programs of biological pest control. The first stage of the these programs is to identify species found in a particular system. Twelve monthly samples were made at eucalyptus plantation in Coronel Pacheco, Minas Gerais States Brazil, with two methods: active search on trails and the plantation (wasps collected with entomological net) and attractive traps (made with two-liter "pet" bottles, with a lateral opening) tied to eucalyptus trunk at approximately 1.5m high above the ground. One hundred and fifty ml attraction substrate was put: passion fruit juice or guava juice (fruit, water and sugar) or broth of sardines (sardines preserved and water). Thirty six bottles was used for collection (12 bottles for each substrate) in four transects of 80m (T1 and T2 - forest-eucalyptus; T3 and T4 - eucalyptus plantation), each with nine traps, distant one from another 10m. Climates data (temperature and precipitation) was obtained from the meteorological station of EMBRAPA Cattle and Milk in Coronel Pacheco. Twelve species of social wasps of six genera were identified. The active search sample method collected 10 species and six in the traps. The active search obtaining the greatest diversity (H' = 0.83), followed by attractive traps with passion fruit juice (H' = 0.38), guava juice (H'= 0.29) and sardines (H' = 0.18). The active search (83.3%) brad higher efficiency. was The greatest number of species occurred in transects of edge (T1 and T2), next to the native forest. There was no correlation between the social wasps species xviii and climatic variables (temperature and precipitation), but swarm-founding species have been recorded during every month of collection and independent foundation were not obtained in January and June. Those with, the best methodology is the combination of two or more techniques, since a specific methodology did not all species collected. It is important to maintain fragments of native vegetation nearly eucalyptus plantations, as a strategy to increase natural occurrence of species social wasp. All seasons are favorable to manage social wasp in eucalyptus plantation, especially for swarm-founding species.

There is ample growth in scientific evidence about climate change. Since, hydrometeorological processes are sensitive to climate variability and changes, ascertaining the linkages and feedbacks between the climate and the hydrometeorological processes becomes critical for environmental quality, economic development, social well-being etc. As the river basin integrates some of the important systems like ecological and socio-economic systems, the knowledge of plausible implications of climate change on hydrometeorology of a river basin will not only increase the awareness of how the hydrological systems may change over the coming century, but also prepare us for adapting to the impacts of climate changes on water resources for sustainable management and development. In general, quantitative climate impact studies are based on several meteorological variables and possible future climate scenarios. Among the meteorological variables, sic “cardinal” variables are identified as the most commonly used in impact studies (IPCC, 2001). These are maximum and minimum temperatures, precipitation, solar radiation, relative humidity and wind speed. The climate scenarios refer to plausible future climates, which have been constructed for explicit use for investigating the potential consequences of anthropogenic climate alterations, in addition to the natural climate variability. Among the climate scenarios adapted in impact assessments, General circulation model(GCM) projections based on marker scenarios given in Intergovernmental Panel on Climate Change’s (IPCC’s) Special Report on Emissions Scenarios(SRES) have become the standard scenarios. The GCMs are run at coarse resolutions and therefore the output climate variables for the various scenarios of these models cannot be used directly for impact assessment on a local(river basin)scale. Hence in the past, several methodologies such as downscaling and disaggregation have been developed to transfer information of atmospheric variables from the GCM scale to that of surface meteorological variables at local scale. The most commonly used downscaling approaches are based on transfer functions to represent the statistical relationships between the large scale atmospheric variables(predictors) and the local surface variables(predictands). Recently Support vector machine (SVM) is proposed, and is theoretically proved to have advantages over other techniques in use such as transfer functions. The SVM implements the structural risk minimization principle, which guarantees the global optimum solution. Further, for SVMs, the learning algorithm automatically decides the model architecture. These advantages make SVM a plausible choice for use in downscaling hydrometeorological variables. The literature review on use of transfer function for downscaling revealed that though a diverse range of transfer functions has been adopted for downscaling, only a few studies have evaluated the sensitivity of such downscaling models. Further, no studies have so far been carried out in India for downscaling hydrometeorological variables to a river basin scale, nor there was any prior work aimed at downscaling CGCM3 simulations to these variables at river basin scale for various IPCC SRES emission scenarios. The research presented in the thesis is motivated to assess the impact of climate change on streamflow at river basin scale for the various IPCC SRES scenarios (A1B, A2, B1 and COMMIT), by integrating implications of climate change on all the six cardinal variables. The catchment of Malaprabha river (upstream of Malaprabha reservoir) in India is chosen as the study area to demonstrate the effectiveness of the developed models, as it is considered to be a climatically sensitive region, because though the river originates in a region having high rainfall it feeds arid and semi-arid regions downstream. The data of the National Centers for Environmental Prediction (NCEP), the third generation Canadian Global Climate Model (CGCM3) of the Canadian Center for Climate Modeling and Analysis (CCCma), observed hydrometeorological variables, Digital Elevation model (DEM), land use/land cover map, and soil map prepared based on PAN and LISS III merged, satellite images are considered for use in the developed models. The thesis is broadly divided into four parts. The first part comprises of general introduction, data, techniques and tools used. The second part describes the process of assessment of the implications of climate change on monthly values of each of the six cardinal variables in the study region using SVM downscaling models and k-nearest neighbor (k-NN) disaggregation technique. Further, the sensitivity of the SVM downscaling models to the choice of predictors, predictand, calibration period, season and location is evaluated. The third part describes the impact assessment of climate change on streamflow in the study region using the SWAT hydrologic model, and SVM downscaling models. The fourth part presents summary of the work presented in the thesis, conclusions draws, and the scope for future research. The development of SVM downscaling model begins with the selection of probable predictors (large scale atmospheric variables). For this purpose, the cross-correlations are computed between the probable predictor variables in NCEP and GCM data sets, and the probable predictor variables in NCEP data set and the predictand. A pool of potential predictors is then stratified (which is optional and variable dependant) based on season and or location by specifying threshold values for the computed cross-correlations. The data on potential predictors are first standardized for a baseline period to reduce systemic bias (if any) in the mean and variance of predictors in GCM data, relative to those of the same in NCEP reanalysis data. The standardized NCEP predictor variables are then processed using principal component analysis (PCA) to extract principal components (PCs) which are orthogonal and which preserve more than 98% of the variance originally present in them. A feature vector is formed for each month using the PCs. The feature vector forms the input to the SVM model, and the contemporaneous value of predictand is its output. Finally, the downscaling model is calibrated to capture the relationship between NCEP data on potential predictors (i.e feature vectors) and the predictand. Grid search procedure is used to find the optimum range for each of the parameters. Subsequently, the optimum values of parameters are obtained from the selected ranges, using the stochastic search technique of genetic algorithm. The SVM model is subsequently validated, and then used to obtain projections of predictand for simulations of CGCM3. Results show that precipitation, maximum and minimum temperature, relative humidity and cloud cover are projected to increase in future for A1B, A2 and B1 scenarios, whereas no trend is discerned with theCOMMIT. The projected increase in predictands is high for A2 scenario and is least for B1 scenario. The wind speed is not projected to change in future for the study region for all the aforementioned scenarios. The solar radiation is projected to decrease in future for A1B, A2 and B1 scenarios, whereas no trend is discerned with the COMMIT. To assess the monthly streamflow responses to climate change, two methodologies are considered in this study namely (i) downscaling and disaggregating the meteorological variables for use as inputs in SWAT and (ii) directly downscaling streamflow using SVM. SWAT is a physically based, distributed, continuous time hydrological model that operates on a daily time scale. The hydrometeorologic variables obtained using SVM downscaling models are disaggregated to daily scale by using k-nearest neighbor method developed in this study. The other inputs to SWAT are DEM, land use/land cover map, soil map, which are considered to be the same for the present and future scenarios. The SWAT model has projected an increase in future streamflows for A1B, A2 andB1 scenarios, whereas no trend is discerned with the COMMIT. The monthly projections of streamflow at river basin scale are also obtained using two SVM based downscaling models. The first SVM model (called one-stage SVM model) considered feature vectors prepared based on monthly values of large scale atmospheric variables as inputs, whereas the second SVM model (called two-stage SVM model) considered feature vectors prepared from the monthly projections of cardinal variables as inputs. The trend in streamflows projected using two-stage SVM model is found to be similar to that projected by SWAT for each of the scenarios considered. The streamflow is not projected to change for any of the scenarios considered with the one-stage SVM downscaling model. The relative performance of the SWAT and the two SVM downscaling models in simulating observed streamflows is evaluated. In general, all the three models are able to simulate the streamflows well. Nevertheless, the performance of SWAT model is better. Further, among the two SVM models, the performance of one-stage streamflow downscaling model is marginally better than that of the two-stage streamflow downscaling model.

[pt] Nos últimos anos, observa-se crescente penetração da energia eólica na matriz energética mundial e brasileira. Em 2015, ela já representava (seis por cento) da capacidade total de geração de energia do país, colocando-o na (décima) posição entre os países com capacidade eólica instalada. A crescente penetração dessa fonte de energia e suas características de intermitência e forte sazonalidade, passaram a demandar modelos de otimização capazes de auxiliar tanto a gestão dos sistemas elétricos com geração intermitente de energia eólica, quanto a comercialização dessa energia. Avançaram, assim, os estudos de previsões de médias a cada (dez) minutos, horárias e diárias de geração eólica, para atender a sua inserção na programação dos sistemas elétricos e a sua comercialização em mercados diários e horários. Contudo, poucos estudos deram atenção à previsão e simulação de médias mensais de geração eólica, imprescindíveis para gestão e otimização da comercialização dessa energia no Brasil, visto que esta ocorre essencialmente em base mensal. Neste contexto, insere-se esta tese, que busca avaliar a otimização comercial de um parque eólico no mercado livre de energia brasileiro, considerando diferentes modelos de simulação da incerteza de geração eólica e níveis de aversão ao risco do gestor. Para representar diferentes níveis de aversão ao risco do gestor, desenvolveu-se uma nova função de preferência, capaz de modelar a variação do nível de aversão ao risco de um mesmo gestor, para diferentes faixas de preferência, definidas a partir de percentis αs de VaRα. A função de preferência desenvolvida é uma ponderação entre o valor esperado e níveis de CVaR dos resultados. De certo modo, ela altera as probabilidades dos resultados, de acordo as preferências do gestor, similar ao efeito dos pesos de decisão na Teoria do Prospecto. Para simulação da geração eólica são adotados modelos autorregressivos com sazonalidade representada por dummies mensais (ARX-11) e periódicos (PAR). Considera-se, ainda, a inclusão de variáveis climáticas exógenas no modelo ARX-11, com ganho de capacidade preditiva. Observou-se que, para um gestor neutro ao risco, as diferentes simulações de geração eólica não alteraram a decisão ótima. O mesmo não é válido para um gestor avesso ao risco, especialmente ao ser considerado o modelo de simulação com variáveis climáticas exógenas. Portanto, é importante a definição de um único modelo de simulação a ser considerado pelo gestor avesso ao risco ou, a adoção de alguma técnica multicritério para ponderação de diferentes modelos. O perfil de risco também altera as decisões ótimas do gestor, observando-se redução do desvio-padrão e da média da distribuição dos resultados e, aumento dos CVaRs e prêmio de risco, à medida que aumenta a aversão ao risco. Assim, é importante a especificação de uma única função de preferência, que represente adequadamente o perfil de risco do gestor ou da empresa, para otimização da comercialização. A flexibilidade da função de preferência desenvolvida, ao permitir a definição de diferentes níveis de aversão ao risco do gestor, para diferentes faixas de preferência, contribui para essa especificação. / [en] In recent years, we have seen an increased penetration of wind power in the Brazilian energy matrix and also worldwide. In 2015, wind power already accounted for (six percent) of the Brazilian total power capacity and the country was the (tenth) in the world raking of wind power installed capacity. Due to the growing penetration of the source, its intermittency and strong seasonality, optimization models able to deal with the management of wind power, both in electrical systems operation and in trading environment, are necessary. Thus, we see the growth in the number of studies concerned about wind power forecasts for every (10) minutes, hours and days, meeting the electrical systems and international trading schedules. However, few studies have given attention to the forecasting and simulation of wind power monthly averages, which are essential for the management and optimization of energy trading in Brazil, since its occurs essentially on a monthly basis. In this context, we introduce this thesis, which seeks to assess the commercial optimization of a wind farm in the Brazilian energy free market, considering different simulation models for the wind power production uncertainty and different levels of manager s risk aversion. In order to represent the manager s different levels of risk aversion, we developed a new preference function, which is able to model the variation of risk aversion level of the same manager, for different preference groups. These groups are defined by α s percentiles of VaRα. The developed preference function is a weighted average between expected value of results and CVaR levels. In a way, it changes the odds of the results, according to the manager s preference, similar to the effect of the decision weights on Prospect Theory. We adopted autoregressive models to simulate wind power generation, with seasonality represented by monthly dummies (ARX -11) or periodic model (PAR). Furthermore, we consider the inclusion of climate exogenous variables in the ARX-11 model and obtain predictive gain. We observed that for a risk neutral manager, different simulations of wind power production do not change the optimal decision. However, this does not apply for risk averse managers, especially when we consider the simulation model with climate exogenous variables. Therefore, it is important that the risk averse manager establishes a single simulation model to consider or adopts some multi-criteria technique for weighting different models. The risk profile also changes the manager optimal decision. We observed that increasing risk aversion, the standard deviation and mean of the results distribution decrease, while risk premium and CVaRs increase. Therefore, to proceed the optimization, it is important to specify a single preference function, which represents adequately the manager or company risk profile. The flexibility of the developed preference function, allowing the definition of different manager s risk aversion levels for different preference groups, contributes to this specification.