Submitted by Marco Antonio Oliveira da Silva (maosilva@ipen.br) on 2016-08-26T12:15:03Z No. of bitstreams: 0 / Made available in DSpace on 2016-08-26T12:15:03Z (GMT). No. of bitstreams: 0 / O escoamento bifásico de gás-líquido é encontrado em muitos circuitos fechados que utilizam circulação natural para fins de resfriamento. O fenômeno da circulação natural é importante nos recentes projetos de centrais nucleares para a remoção de calor. O circuito de circulação natural (Circuito de Circulação Natural - CCN), instalado no Instituto de Pesquisas Energéticas e Nucleares, IPEN / CNEN, é um circuito experimento concebido para fornecer dados termo-hidráulicos relacionados com escoamento monofásico ou bifásico em condições de circulação natural. A estimativa de transferência de calor tem sido melhorada com base em modelos que requerem uma previsão precisa de transições de padrão de escoamento. Este trabalho apresenta testes experimentais desenvolvidos no CCN para a visualização dos fenômenos de instabilidade em ciclos de circulação natural básica e classificar os padrões de escoamento bifásico associados aos transientes e instabilidades estáticas de escoamento. As imagens são comparadas e agrupadas utilizando mapas auto-organizáveis de Kohonen (SOM), aplicados em diferentes características da imagem digital. Coeficientes da Transformada Discreta de Cossenos de Quadro Completo (FFDCT) foram utilizados como entrada para a tarefa de classificação, levando a bons resultados. Os protótipos de FFDCT obtidos podem ser associados a cada padrão de escoamento possibilitando uma melhor compreensão da instabilidade observada. Uma metodologia sistemática foi utilizada para verificar a robustez do método. / Dissertação (Mestrado em Tecnologia Nuclear) / IPEN/D / Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2016-05-31T09:35:51Z No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-05-31T10:52:51Z (GMT) No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-05-31T10:52:51Z (GMT). No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2016-03-07 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The climate changes are one of the major threats to the biodiversity and it is expected to increase its impact along the 21st century. The climate change affect all levels of the biodiversity from individuals to biomes, reducing the ecosystem services. Despite of this, the prediction of climate change impacts on biodiversity is still a challenge. Overcoming these issues depends on improvements in different aspects of science that support predictions of climate change impact on biodiversity. The common practice to predict the climate change impact consists in formulate ecological niche models based in the current climate and project the changes based in the future climate predicted by the climate models. However, there are some recognized limitations both in the formulation of the ecological niche model and in the use of predictions from the climate models that need to be analyzed. Here, in the first chapter we review the science behind the climate models in order to reduce the knowledge gap between the scientific community that formulate the climate models and the community that use the predictions of these models. We showed that there is not consensus about evaluate the climate models, obtain regional models with higher spatial resolution and define consensual models. However, we gave some guidelines for use the predictions of the climate models. In the second chapter, we tested if the predictions of correlative ecological niche models fitted with presence-absence match the predictions of models fitted with abundance data on the metrics of climate change impact on orchid bees in the Atlantic Forest. We found that the presence-absence models were a partial proxy of change in abundance when the output of the models was continuous, but the same was not true when the predictions were converted to binary. The orchid bees in general will decrease the abundance in the future, but will retain a good amount of suitable sites in the future and the distance to gained climatic suitable areas can be very close, despite of great variation. The change in the species richness and turnover will be mainly in the western and some regions of southern of the Atlantic Forest. In the third chapter, we discussed the drawbacks in using the estimations of realized niche instead the fundamental niche, such as overpredicting the effect of climate change on species’ extinction risk. We proposed a framework based on phylogenetic comparative and missing data methods to predict the dimensions of the fundamental niche of species with missing data. Moreover, we explore sources of uncertainty in predictions of fundamental niche and highlight future directions to overcome current limitations of phylogenetic comparative and missing data methods to improve predictions. We conclude that it is possible to make better use of the current knowledge about species’ fundamental niche with phylogenetic information and auxiliary traits to predict the fundamental niche of poorly-studied species. In the fourth chapter, we used the framework of the chapter three to test the performance of two recent phylogenetic modeling methods to predict the thermal niche of mammals. We showed that PhyloPars had better performance than Phylogenetic Eigenvector Maps in predict the thermal niche. Moreover, the error and bias had similar phylogenetic pattern for both margins of the thermal niche while they had differences in the geographic pattern. The variance in the performance was explained by taxonomic differences and not by methodological aspects. Finally, our models better predicted the upper margin than the lower margin of the thermal niche. This is a good news for predicting the effect of climate change on species without physiological data. We hope our finds can be used to improve the predictions of climate change effect on the biodiversity in future studies and support the political decisions on minimizing the effects of climate change on biodiversity. / As mudanças climáticas são uma das principais ameaças à biodiversidade e é esperado que aumente seu impacto ao longo do século XXI. As mudanças climáticas afetam todos os níveis de biodiversidade, de indivíduos à biomas, reduzindo os serviços ecossistêmicos. Apesar disso, as predições dos impactos das mudanças climáticas na biodiversidade é ainda um desafio. A superação dessas questões depende de melhorias em diferentes aspectos da ciência que dá suporte para predizer o impacto das mudanças climáticas na biodiversidade. A prática comum para predizer o impacto das mudanças climáticas consiste em formular modelos de nicho ecológico baseado no clima atual e projetar as mudanças baseadas no clima futuro predito pelos modelos climáticos. No entanto, existem algumas limitações reconhecidas na formulação do modelo de nicho ecológico e no uso das predições dos modelos climáticos que precisam ser analisadas. Aqui, no primeiro capítulo nós revisamos a ciência por detrás dos modelos climáticos com o intuito de reduzir a lacuna de conhecimentos entre a comunidade científica que formula os modelos climáticos e a comunidade que usa as predições dos modelos. Nós mostramos que não existe consenso sobre avaliar os modelos climáticos, obter modelos regionais com maior resolução espacial e definir modelos consensuais. No entanto, nós damos algumas orientações para usar as predições dos modelos climáticos. No segundo capítulo, nós testamos se as predições dos modelos correlativos de nicho ecológicos ajustados com presença-ausência são congruentes com aqueles ajustados com dados de abundância nas medidas de impacto das mudanças climáticas em abelhas de orquídeas da Mata Atlântica. Nós encontramos que os modelos com presença-ausência foram substitutos parciais das mudanças na abundância quando o resultado dos modelos foi contínuo (adequabilidade), mas o mesmo não ocorreu quando as predições foram convertidas para binárias. As espécies de abelhas, de modo geral, irão diminuir em abundância no futuro, mas reterão uma boa quantidade de locais adequados no futuro e a distância para áreas climáticas adequadas ganhadas podem estar bem próximo, apesar da grande variação. A mudança na riqueza e na substituição de espécies ocorrerá principalmente no Oeste e algumas regiões no sul da Mata Atlântica. No terceiro capítulo, nós discutimos as desvantagens no uso de estimativas do nicho realizado ao invés do nicho fundamental, como superestimar o efeito das mudanças climáticas no risco de extinção das espécies. Nós propomos um esquema geral baseado em métodos filogenéticos comparativos e métodos de dados faltantes para predizer as dimensões do nicho fundamental das espécies com dados faltantes. Além disso, nós exploramos as fontes de incerteza nas predições do nicho fundamental e destacamos direções futuras para superar as limitações atuais dos métodos comparativos filogenéticas e métodos de dados faltantes para melhorar as predições. Nós concluímos que é possível fazer melhor uso do conhecimento atual sobre o nicho fundamental das espécies com informação filogenética e caracteres auxiliares para predizer o nicho fundamental de espécies pouco estudadas. No quarto capítulo, nós usamos o esquema geral do capítulo três para testar a performance de dois novos métodos de modelagem filogenética para predizer o nicho térmico dos mamíferos. Nós mostramos que o “PhyloPars” teve uma melhor performance que o “Phylogenetic Eigenvector Maps” em predizer o nicho térmico. Além disso, o erro e o viés tiveram um padrão filogenético similar para ambas as margens do nicho térmico, enquanto eles apresentaram diferentes padrões espaciais. A variância na performance foi explicada pelas diferenças taxonômicas e não pelas diferenças em aspectos metodológicos. Finalmente, nossos modelos melhor predizem a margem superior do que a margem inferior do nicho térmico. Essa é uma boa notícia para predizer o efeito das mudanças climáticas em espécies sem dados fisiológicos. Nós esperamos que nossos resultados possam ser usados para melhorar as predições do efeito das mudanças climáticas na biodiversidade em estudos futuros e dar suporte para decisões políticas para minimização dos efeitos das mudanças climáticas na biodiversidade.

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Climate change could aggravate periodic and chronic shortfalls of water, particularly in arid and semi-arid areas of the world (IPCC, 2001). Climate change is likely to accelerate the global hydrological cycle, with increase in temperature, changes in precipitation patterns, and evapotranspiration affecting the water quantity and quality, water availability and demands. The various components of a surface water resources system affected by climate change may include the water availability, irrigation demands, water quality, hydropower generation, ground water recharge, soil moisture etc. It is prudent to examine the anticipated impacts of climate change on these different components individually or combinedly with a view to developing responses to minimize the climate change induced risk in water resources systems. Assessment of climate change impacts on water resources essentially involves downscaling the projections of climatic variables (e.g., temperature, humidity, mean sea level pressure etc.) to hydrologic variables (e.g., precipitation and streamflow), at regional scale. Statistical downscaling methods are generally used in the hydrological impact assessment studies for downscaling climate projections provided by the General Circulation Models (GCMs). GCMs are climate models designed to simulate time series of climate variables globally, accounting for the greenhouse gases in the atmosphere. The statistical techniques used to bridge the spatial and temporal resolution gaps between what GCMs are currently able to provide and what impact assessment studies require are called as statistical downscaling methods. Generally, these methods involve deriving empirical relationships that transform large-scale simulations of climate variables (referred as the predictors) provided by a GCM to regional scale hydrologic variables (referred as the predictands). This general methodology is characterized by various uncertainties such as GCM and scenario uncertainty, uncertainty due to initial conditions of the GCMs, uncertainty due to downscaling methods, uncertainty due to hydrological model used for impact assessment and uncertainty resulting from multiple stake holders in a water resources system. The research reported in this thesis contributes towards (i) development of methodologies for climate change impact assessment of various components of a water resources system, such as water quality, water availability, irrigation and reservoir operation, and (ii) quantification of GCM and scenario uncertainties in hydrologic impacts of climate change. Further, an integrated reservoir operation model is developed to derive optimal operating policies under the projected scenarios of water availability, irrigation water demands, and water quality due to climate change accounting for various sources of uncertainties. Hydropower generation is also one of the objectives in the reservoir operation. The possible climate change impact on river water quality is initially analyzed with respect to hypothetical scenarios of temperature and streamflow, which are affected by changes in precipitation and air temperature respectively. These possible hypothetical scenarios are constructed for the streamflow and river water temperature based on recent changes in the observed data. The water quality response is simulated, both for the present conditions and for conditions resulting from the hypothetical scenarios, using the water quality simulation model, QUAL2K. A Fuzzy Waste Load Allocation Model (FWLAM) is used as a river water quality management model to derive optimal treatment levels for the dischargers in response to the hypothetical scenarios of streamflow and water temperature. The scenarios considered for possible changes in air temperature (+1 oC and +2 oC) and streamflow (-0%, -10%, -20%) resulted in a substantial decrease in the Dissolved Oxygen (DO) levels, increase in Biochemical Oxygen Demand (BOD) and river water temperature for the case study of the Tunga-Bhadra River, India. The river water quality indicators are analyzed for the hypothetical scenarios when the BOD of the effluent discharges is at safe permissible level set by Pollution Control Boards (PCBs). A significant impairment in the water quality is observed for the case study, under the hypothetical scenarios considered. A multi-variable statistical downscaling model based on Canonical Correlation Analysis (CCA) is then developed to downscale future projections of hydro¬meteorological variables to be used in the impact assessment study of river water quality. The CCA downscaling model is used to relate the surface-based observations and atmospheric variables to obtain the simultaneous projection of hydrometeorological variables. Statistical relationships in terms of canonical regression equations are obtained for each of the hydro-meteorological predictands using the reanalysis data and surface observations. The reanalysis data provided by National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used for the purpose. The regression equations are applied to the simulated GCM output to model future projections of hydro-meteorological predictands. An advantage of the CCA methodology in the context of downscaling is that the relationships between climate variables and the surface hydrologic variables are simultaneously expressed, by retaining the explained variance between the two sets. The CCA method is used to model the monthly hydro-meteorological variables in the Tunga-Bhadra river basin for water quality impact assessment study. A modeling framework of risk assessment is developed to integrate the hydro¬meteorological projections downscaled from CCA model with a river water quality management model to quantify the future expected risk of low water quality under climate change. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold DO level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is adopted to evaluate the future fractional removal policies for each of the dischargers by including the predicted future risk levels. The hydro-meteorological projections of streamflow, air temperature, relative humidity and wind speed are modeled using MIROC 3.2 GCM simulations with A1B scenario. The river water temperature is modeled by using an analytical temperature model that includes the downscaled hydro-meteorological variables. The river water temperature is projected to increase under climate change, for the scenario considered. The IFWLAM uses the downscaled projections of streamflow, simulated river water temperature and the predicted lower and upper future risk levels to determine the fraction removal policies for each of the dischargers. The results indicate that the optimal fractional removal levels required for the future scenarios will be higher compared to the present levels, even if the effluent loadings remain unchanged. Climate change is likely to impact the agricultural sector directly with changes in rainfall and evapotranspiration. The regional climate change impacts on irrigation water demands are studied by quantifying the crop water demands for the possible changes of rainfall and evapotranspiration. The future projections of various meteorological variables affecting the irrigation demand are downscaled using CCA downscaling model with MIROC 3.2 GCM output for the A1B scenario. The future evapotranspiration is obtained using the Penman-Monteith evapotranspiration model accounting for the projected changes in temperature, relative humidity, solar radiation and wind speed. The monthly irrigation water demands of paddy, sugarcane, permanent garden and semidry crops quantified at nine downscaling locations covering the entire command area of the Bhadra river basin, used as a case study, are projected to increase for the future scenarios of 2020-2044, 2045-2069 and 2070-2095 under the climate change scenario considered. The GCM and scenario uncertainty is modeled combinedly by deriving a multimodel weighted mean by assigning weights to each GCM and scenario. An entropy objective weighting scheme is proposed which exploits the information contained in various GCMs and scenarios in simulating the current and future climatology. Three GCMs, viz., CGCM2 (Meteorological Research Institute, Japan), MIROC3.2 medium resolution (Center for Climate System Research, Japan), and GISS model E20/Russell (NASA Goddard Institute for Space Studies, USA) with three scenarios A1B, A2 and B1 are used for obtaining the hydro-meteorological projections for the Bhadra river basin. Entropy weights are assigned to each GCM and scenario based on the performance of the GCM and scenario in reproducing the present climatology and deviation of each from the projected ensemble average. The proposed entropy weighting method is applied to projections of the hydro-meteorological variables obtained based on CCA downscaling method from outputs of the three GCMs and the three scenarios. The multimodel weighted mean projections are obtained for the future time slice of 2020-2060. Such weighted mean hydro-meteorological projections may be further used into the impact assessment model to address the climate model uncertainty in the water resources systems. An integrated reservoir operation model is developed considering the objectives of irrigation, hydropower and downstream water quality under uncertainty due to climate change, uncertainty introduced by fuzziness in the goals of stakeholders and uncertainty due to the random nature of streamflow. The climate model uncertainty originating from the mismatch between projections from various GCMs under different scenarios is considered as first level of uncertainty, which is modeled by using the weighted mean hydro-meteorological projections. The second level of uncertainty considered is due to the imprecision and conflicting goals of the reservoir users, which is modeled by using fuzzy set theory. A Water Quantity Control Model (WQCM) is developed with fuzzy goals of the reservoir users to obtain water allocations among the different users of the reservoir corresponding to the projected demands. The water allocation model is updated to account for the projected demands in terms of revised fuzzy membership functions under climate change to develop optimal policies of the reservoir for future scenarios. The third level of uncertainty arises from the inherent variability of the reservoir inflow leading to uncertainty due to randomness, which is modeled by considering the reservoir inflow as a stochastic variable. The optimal monthly operating polices are derived using Stochastic Dynamic Programming (SDP), separately for the current and for the future periods of 2020-2040 and 2040-2060 The performance measures for Bhadra reservoir in terms of reliability and deficit ratios for each reservoir user (irrigation, hydropower and downstream water quality) are estimated with optimal SDP policy derived for current and future periods. The reliability with respect to irrigation, downstream water quality and hydropower show a decrease for 2020-2040 and 2040-2060, while deficit ratio increases for these periods. The results reveal that climate change is likely to affect the reservoir performance significantly and changes in the reservoir operation for the future scenarios is unable to restore the past performance levels. Hence, development of adaptive responses to mitigate the effects of climate change is vital to improve the overall reservoir performance.

Au sein de l’hémisphère sud, l’Afrique australe et le sud-ouest de l’océan Indien forment l’une des trois zones préférentielles de développement des interactions entre les tropiques et les moyennes latitudes. Il s’agit de la Zone de Convergence Sud-Indienne (ZCSI) où se forment, principalement en été austral, des systèmes synoptiques (entre 3 et 5 jours) caractérisés par des bandes nuageuses orientées nord-ouest/sud-est : les Talwegs Tropicaux-Tempérés (TTT). Cette recherche doctorale vise à améliorer les connaissances liées aux TTT, avec une étude sur la dynamique associée à ces systèmes et une analyse de leur évolution possible au cours du XXIème siècle. La première partie de l’étude s’attache à identifier les conditions favorables à la formation et au développement des TTT. Une classification en régimes de temps est utilisée pour identifier les TTT d’une part, et les perturbations des moyennes latitudes d’autre part, permettant de mieux documenter la variabilité spatio-temporelle des TTT et les conditions de leur développement. Ces événements sont responsables d’environ 20% des précipitations saisonnières sud-africaines. Cette contribution augmente selon un gradient ouest-est. La comparaison entre les deux classifications confirme que les perturbations des moyennes latitudes sont une condition nécessaire pour le développement de TTT, mais non suffisante. Dans les tropiques, des advections d’humidité depuis l’océan Atlantique tropical associées à un excès d'énergie statique humide sur le canal du Mozambique forment les conditions supplémentaires favorables à la convection atmosphérique profonde à proximité du continent. La seconde partie étudie comment les TTT, les précipitations et de manière plus générale le climat d’Afrique australe, pourraient évoluer au cours du XXIème siècle sous l’effet du forçage radiatif associé aux émissions de gaz à effet de serre (GES). Cette approche est multi-modèle (huit modèles climatiques sont retenus de l’exercice 5 du GIEC) et multi-trajectoire (RCP 8.5 et 2.6). Les huit modèles restituent convenablement les caractéristiques actuelles du climat d’Afrique australe ainsi que les bandes nuageuses associées aux TTT, tant en termes de variabilité spatiale que de fréquence d’occurrences. Sur le XXIème siècle, ils divergent sur l’évolution des précipitations saisonnières (NDJF). En revanche, ils convergent sur l’augmentation des quantités précipitées par jour de pluie sur le sud-est de l’Afrique australe. Ces changements ne sont pas à relier à une évolution spatio-temporelle des TTT, leur structure spatiale, leur fréquence d’occurrence et leur contribution aux précipitations restent stationnaires tout au long du siècle, mais à des événements pluviogènes extrêmes plus fréquents et plus intenses. / In the Southern Hemisphere, Southern Africa and the south-west Indian Ocean are one of the three preferred regions where interactions between the tropics and midlatitudes develop. This is the South Indian Convergence Zone (SICZ), where northwest-southeast oriented cloud bands form at the synoptic scale (between 3 and 5 days). These bands are mainly found during the austral summer and are commonly referred to as tropical temperate troughs (TTTs). This research aims at improving our knowledge related to TTTs, with a study on the dynamics associated with these systems, and an analysis of their possible evolution during the 21st century.The first part of this thesis aims at identifying favorable conditions for the formation and the development of TTTs. Weather regimes analysis is used to identify TTTs on the one hand and mid-latitude perturbations on the other hand, allowing us to better document the spatial and temporal variability of TTTs together with background climate conditions. The events identified account for 20% of seasonal rainfall on average. Their contribution increases according to a west to east gradient. The comparison between these two classifications, partitioned using a k-means clustering, first confirms that midlatitude perturbations are a necessary condition for TTT development, but they are not sufficient. An excess of moist static energy over the Mozambique Channel partly supplied by advections from remote regions (mostly the southern Atlantic basin and the south-west Indian Ocean) form additional conditions favoring deep atmospheric convection over and near the Southern Africa. The second part investigates possible changes in precipitation, TTTs and more generally climate over Southern Africa during the 21st century in response to radiative forcing associated with greenhouse gas emissions (GHG). A multi-model (height climate models taken on the IPCC Fifth Assessment Report) and multi-scenario (RCP 8.5 and 2.6) approach is chosen. All models are skillful to reproduce Southern Africa current climate characteristics and cloud bands associated with TTTs, both in terms of spatial variability and frequency of occurrences. During the 21th century, there is no consensus between the models on the future evolution of seasonal rainfall (NDJF). However, all simulate an increase in the amounts precipitated by rainy day over the south-east part of southern Africa. These changes are not related to an evolution of TTTs: their spatial patterns, frequency of occurrences and contribution to rainfall remain stationary throughout the 21st century, but they associated with extreme rainfall events that become more frequent and more intense.

Understanding climate change and variability at urban scale is essential for water resource management, land use planning, and development of adaption plans. However, there are serious challenges to meet these goals due to unavailability of observed and / or simulated high resolution spatial and temporal climate data. Recent efforts made possible the availability of high resolution climate data from non-hydrostatic regional climate model (RCM) and statistically downscaled General Circulation Models (GCMs). This study investigates trends in climate and urbanization and their impact on surface water supply for the city of Addis Ababa, Ethiopia. The methodology presented in this study focused on the observed and projected NIMRHadGEM2- AO model and Special Report on Emissions Scenarios (SRES) of B2 and A2 of HadCM3 model are also employed for rainfall, maximum temperature and minimum temperature data using for climate analysis. Water Evaluation and Planning (WEAP) modeling system was used for determination of climate and urbanization impacts on water. Land-Sat images were analyzed using Normalized Differencing Vegetation Index (NDVI). Statistical downscaling model (SDSM) was employed to investigate the major changes and intensity of the urban heat island (UHI). The result indicates monthly rainfall anomalies with respect to the baseline mean showing wet anomaly in summer (kiremt) during 2030s and 2050s, and a dry anomaly in the 2080s under A2 and B2 scenarios with exception of a wet anomaly in September over the city. The maximum temperature anomalies under Representative Concentration Pathways (RCPs) also show warming during near, mid and end terms. The mean monthly minimum temperature anomalies under A2 and B2 scenarios are warm but the anomalies are much lower than RCPs. The climate under the RCP 8.5 and high population growth (3.3 %) scenario will lead to the unmet demand of 462.77 million m3 by 2039. Future projection of urban heat island under emission pathway of A2 and B2 scenario shows that, the nocturnal UHI will be intense in winter or dry season episodes in the city. Under A2 scenario the highest urban warming will occur during October to December (2.5 ºC to 3.2 ºC). Under RCP 8.5 scenario the highest urban warming will occur during October to December (0.5 ºC to 1.0 °C) in the 2050s and 2080s. Future management and adaptation strategies are to expand water supply to meet future demand and to implement demand side water management systems of the city and UHI / College of Agriculture and Environmental Sciences / Ph. D. (Environmental Management)