<p>A global climate change may have large impacts on water resources on regional and global scales. General circulation models (GCMs) are the most used tools to evaluate climate-change scenarios on a global scale. They are, however, insufficiently describing the effects at the local scale. This thesis evaluates different approaches of statistical downscaling of precipitation from large-scale circulation variables, both concerning the method performance and the optimum choice of predictor variables. </p><p>The analogue downscaling method (AM) was found to work well as “benchmark” method in comparison to more complicated methods. AM was implemented using principal component analysis (PCA) and Teweles-Wobus Scores (TWS). Statistical properties of daily and monthly precipitation on a catchment in south-central Sweden, as well as daily precipitation in three catchments in China were acceptably downscaled.</p><p>A regression method conditioning a weather generator (SDSM) as well as a fuzzy-rule based circulation-pattern classification method conditioning a stochastical precipitation model (MOFRBC) gave good results when applied on Swedish and Chinese catchments. Statistical downscaling with MOFRBC from GMC (HADAM3P) output improved the statistical properties as well as the intra-annual variation of precipitation.</p><p>The studies show that temporal and areal settings of the predictor are important factors concerning the success of precipitation modelling. The MOFRCB and SDSM are generally performing better than the AM, and the best choice of method is depending on the purpose of the study. MOFRBC applied on output from a GCM future scenario indicates that the large-scale circulation will not be significantly affected. Adding humidity flux as predictor indicated an increased intensity both in extreme events and daily amounts in central and northern Sweden.</p>

A global climate change may have large impacts on water resources on regional and global scales. General circulation models (GCMs) are the most used tools to evaluate climate-change scenarios on a global scale. They are, however, insufficiently describing the effects at the local scale. This thesis evaluates different approaches of statistical downscaling of precipitation from large-scale circulation variables, both concerning the method performance and the optimum choice of predictor variables. The analogue downscaling method (AM) was found to work well as “benchmark” method in comparison to more complicated methods. AM was implemented using principal component analysis (PCA) and Teweles-Wobus Scores (TWS). Statistical properties of daily and monthly precipitation on a catchment in south-central Sweden, as well as daily precipitation in three catchments in China were acceptably downscaled. A regression method conditioning a weather generator (SDSM) as well as a fuzzy-rule based circulation-pattern classification method conditioning a stochastical precipitation model (MOFRBC) gave good results when applied on Swedish and Chinese catchments. Statistical downscaling with MOFRBC from GMC (HADAM3P) output improved the statistical properties as well as the intra-annual variation of precipitation. The studies show that temporal and areal settings of the predictor are important factors concerning the success of precipitation modelling. The MOFRCB and SDSM are generally performing better than the AM, and the best choice of method is depending on the purpose of the study. MOFRBC applied on output from a GCM future scenario indicates that the large-scale circulation will not be significantly affected. Adding humidity flux as predictor indicated an increased intensity both in extreme events and daily amounts in central and northern Sweden.

Made available in DSpace on 2017-07-20T13:41:56Z (GMT). No. of bitstreams: 1 GABRIELA LEITE NEVES.pdf: 2850568 bytes, checksum: d212a8a17a32a986fc2b0200c7ee256e (MD5) Previous issue date: 2015-02-09 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Climate change is a matter of research, since there are still many questions about its causes and consequences. The effects of climate change are diverse, and can reach from the biodiversity of the planet to the economic, social and environmental sectors. Thus the knowledge of climate variables, their changes and impacts on natural resources is very important. Many researchers already had observed that water, a natural resource of great value, may be disturbed due to climate change. Thus the study of water availability related to climate change becomes necessary. The aim of this study was to analyze the impact of possible climate changes on water availability in the state of Paraná, Brazil. It were used daily data of precipitation, and maximum and minimum air temperature of 28 locations, with series of data from 1980 to 2009. By means of the PGECLIMA_R tool daily weather data were simulated for the period 2010 to 2099, using the climate scenarios projected in the IPCC AR4-, IPCC AR5- and trend analysis of historical data from 1980 to 2009. For the creation of the latter scenario, there was a parallel study to obtain the changes in temperature for each location, which were then projected in the simulations. In order to quantify the water availability under the proposed conditions, we estimated the water balance for the current and simulated weather data using the methodology of Thornthwaite and Matter (1955). With interpolation tools, the results of changes in temperature and hydric indices obtained by the water balance were mapped for the entire state of Paraná. The hydric indices were then compared in different scenarios and analyzed periods. The results show that the state of Paraná has a great climate variability across regions. The trend analysis of air temperature pointed from reductions to increases in this variable and these were responsible for different impacts on water availability. In general, all scenarios were responsible for reductions in water availability, but the northern region is more susceptible to these reductions. The trend scenario showed reductions in water availability for most regions, but there was also increase in some specific regions of Parana State. / As modificações do clima é assunto de pesquisa, já que ainda existem muitos questionamentos sobre suas causas e consequências. Os efeitos das mudanças climáticas são diversos, e podem atingir desde a biodiversidade do planeta até os setores econômicos, sociais e ambientais. Sendo assim o conhecimento das variáveis climáticas, de suas alterações e seus impactos nos recursos naturais é de suma importância. Muitos pesquisadores já observaram que a água, um recurso natural de grande valor, poderá sofrer perturbações decorrentes das mudanças climáticas. Assim o estudo da disponibilidade hídrica frente às mudanças climáticas se torna necessário. O objetivo desse trabalho foi analisar o impacto de possíveis mudanças climáticas na disponibilidade hídrica no estado do Paraná. Utilizou-se dados diários de precipitação, temperatura máxima e temperatura mínima de 28 localidades, que correspondem a uma série de dados de 1980 a 2009. Por meio da ferramenta PGECLIMA_R, realizaram-se simulações de dados climáticos diários, para o período de 2010 até 2099, considerando os cenários climáticos projetados no IPCC-AR4, IPCC-AR5 e na análise de tendência dos dados históricos no período de 1980 a 2009. Para a criação deste último cenário, realizou-se um estudo paralelo em que se obteve as alterações na temperatura para cada localidade, as quais foram então projetadas nas simulações. A fim de quantificar a disponibilidade hídrica nas condições propostas, estimou-se os balanço hídricos para os dados climáticos atuais e para os simulados, pela metodologia de Thornthwaite e Matter (1955). Com ferramentas de interpolação, os valores das alterações na temperatura e dos índices hídricos obtidos pelo balanço hídrico foram espacializados para todo o estado do Paraná. Os índices hídricos então foram comparados nos diferentes cenários e períodos analisados. Os resultados apontam que o estado do Paraná apresenta uma grande variabilidade climática entre suas regiões. A análise de tendência da temperatura do ar apontou desde reduções a aumentos nessa variável e essas alterações foram responsáveis também por diferentes impactos na disponibilidade hídrica. De forma geral, todos os cenários foram responsáveis por reduções na disponibilidade hídrica, porém a região norte foi aquela que mostrou ser mais suscetível a essas reduções. O cenário de tendência apontou reduções na disponibilidade hídrica para a maioria das regiões, porém houve aumento pontual da mesma em algumas regiões especificas do Estado do Paraná.

The UK’s decarbonisation strategy to increasingly electrify heating and transport will change the demand requirement on the electricity system. Additionally, under a climate change future, it is projected that the decarbonised grid will need to be able to operate under higher average temperatures in the UK, increasing the need for comfort cooling during summer and leading to additional electricity demand. These new demands will result in greater variation between minimum and peak demand as well as a significant increase in overall demand. Concurrently, supply-side decarbonisation programmes may lead to more intermittent renewables such as wind, PV, tidal and wave, elevating variability in electricity generation. Coupled with the anticipated higher variation in demand this brings on several challenges in operating the electricity grid. In order to characterise these challenges this research develops a bespoke electricity dispatch model which builds on hourly models of demand and generation. The hourly demand profiles are based on a high electrification of heating, transport and cooling coupled with future temperatures premised on the UKCP09 high emission scenario climate projections. The demand profiles show a significant increase in peak demand by 2050 reaching 194 GW, mainly due to summer cooling loads which contribute 70% of the demand. The cumulative CO2 emissions budgets of the GB power sector that are consistent with avoiding global climate change to 2°C are used to develop two low carbon generation scenarios distinguished by the amount of intermittent renewable generation technologies. The dispatch model tests the capability of generation scenarios with the use of hourly generation models in meeting future demand profiles out to 2050.The outputs from dispatch model indicate that there are shortages and excesses of generation relative to demand from 2030 onwards. The variability analysis outlines low and high generation periods from intermittent technologies along with the pace at which intermittent generation increases or decreases within an hour. The characterisation of variability analysis reveals the type of reserve capacity or smart solutions that are required to maintain the security of electricity supply. The solutions that could address the challenges quantified from the model outputs in operating a decarbonised GB electricity grid are explored using expert interviews. The analysis of the stakeholder interviews suggests smart grid solutions that include technologies as well as changes in operational procedures in order to enhance the operational resilience of the grid. Active Network Management through monitoring and control, demand management, storage systems and interconnectors are proposed to address challenges arising from varying demand and generation variability.