Modelagem integrada de meteorologia e recursos hídricos em múltiplas escalas temporais e espaciais: aplicação no Ceará e no setor hidroelétrico brasileiro

Detalhes bibliográficos
Ano de defesa: 2014
Autor(a) principal: Silveira, Cleiton da Silva
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/11327
Resumo: This study aims to develop a planning system on multiple spatial and temporal scales, and apply it to the Brazilian electric sector and Ceará State, Jaguaribe Metropolitan System. For realization of this proposal, we have been considered some temporal scales: short-term (up to 1 month), short term (up to one year) and medium to long term (1-10 years and 10-30 years, respectively). To obtain estimates of the flow of short-term rainfall forecasts from atmospheric models for later entry in the hydrological rainfall-runoff model are used. To short term scale were considered stochastic and statistical models, as the Periodic Autoregressive type (PAR), Periodic Autoregressive with exogenous variables (PARx) and K-nearest neighbor models, and the use of global atmospheric models as input to hydrological rainfall-runoff model Soil Moisture Accounting Procedure (SMAP). For the range of the medium term were considered auto regressive models (AR) and Fourier and wavelets. We used data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) as input in hydrological rainfall-runoff model for long-term scale. For the weather forecast, as the rain threshold adopted in the construction of the contingency table increases, the quality of the forecasts decreases, except for the adjustment index. Thus, the system of numerical prediction proves efficient in detecting the occurrence of rainfall of less intensity, with most satisfactory results in the North and Northeast regions of Brazil. On seasonal scale the models feature up better than the climatology. Likewise, in the range of medium-term models based on Fourier series and wavelets have better likelihood than the weather. In multi-scale, there are differences in the future shown by the projections of the CMIP5 models that were analyzed for RCP8.5 and RCP4.5 the XXI century scenarios, but in the North sector of the National Interconnected System (SIN), most models indicate negative trend, diverging only in magnitude.