Influencia do fenómeno enos na resposta hidrológica anual da sub-bacia Amazonica/Brasil (Regiao Hidrográfica do Xingu/Pará)

Ponencia presentada en: XXX Jornadas Científicas de la AME y el IX Encuentro Hispano Luso de Meteorología celebrado en Zaragoza, del 5 al 7 de mayo de 2008

Desempenho do Modelo CATT-BRAMS em simulações de transporte de poluentes emitidos por incêndios florestais

Ponencia presentada en: XXX Jornadas Científicas de la AME y el IX Encuentro Hispano Luso de Meteorología celebrado en Zaragoza, del 5 al 7 de mayo de 2008.[PT]O objectivo deste trabalho foi avaliar o comportamento do modelo CATT-BRAMS para o transporte atmosférico do monóxido de carbono (CO) e material particulado (PM2.5) emitidos por queimadas diante de uma situação considerada normal (2002) em relação aos incêndios que ocorreram em 2003, considerada uma das mais intensas temporadas de incêndio durante as últimas décadas. As condições iniciais e de contorno foram feitas utilizando as análises do modelo global AVN/NCEP (Aviation run of the National Centers for Environmental Prediction Global Spectral Model) e com a assimilação dos dados de fogos derivados a partir dos productos MODIS/AQUA para a Europa, com o objectivo de identificar as posições das emissões.[EN]The objective of this work was to evaluate the behaviour of the CATT-BRAMS model for the atmospheric transport of the carbon monoxide (CO) and particulate matter (PM2.5) emitted by burning due to a situation considered normal (2002) in relation to the fires that happened in 2003, considered one of the most intense fire seasons during the last decade.. The initial and lateral boundary conditions were provided by the analyses of the global model AVN/NCEP (Aviation run of the National Centers Environmental Global Prediction Spectral Model) and the assimilation of fire data derived from the products MODIS/AQUA to Europe, with the objective of identifying the emissions location

A combined stochastic model for seasonal prediction of precipitation in Brazil

Este artigo discute um modelo de previsão combinada para a realização de prognósticos climáticos na escala sazonal. Nele, previsões pontuais de modelos estocásticos são agregadas para obter as melhores projeções no tempo. Utilizam-se modelos estocásticos autoregressivos integrados a médias móveis, de suavização exponencial e previsões por análise de correlações canônicas. O controle de qualidade das previsões é feito através da análise dos resíduos e da avaliação do percentual de redução da variância não-explicada da modelagem combinada em relação às previsões dos modelos individuais. Exemplos da aplicação desses conceitos em modelos desenvolvidos no Instituto Nacional de Meteorologia (INMET) mostram bons resultados e ilustram que as previsões do modelo combinado, superam na maior parte dos casos a de cada modelo componente, quando comparadas aos dados observados.This article discusses a combined model to perform climate forecast in a seasonal scale. In it, forecasts of specific stochastic models are aggregated to obtain the best forecasts in time. Stochastic models are used in the auto regressive integrated moving average, exponential smoothing and the analysis of forecasts by canonical correlation. The quality control of the forecast is based on the residual analysis and the evaluation of the percentage of reduction of the unexplained variance of the combined model with respect to the individual ones. Examples of application of those concepts to models developed at the Brazilian National Institute of Meteorology (INMET) show good results and illustrate that the forecast of the combined model exceeds in most cases each component model, when compared to observed data

Modelagem hidrológica determinística e estocástica aplicada à região hidrográfica do Xingu- Pará Deterministic and stochastic hydrological modeling applied to the Xingu river basin - Pará

A modelagem hidrológica é uma importante ferramenta no planejamento e gerenciamento de programas de recursos hídricos de bacias hidrográficas. Neste trabalho, foi aplicado o modelo hidrológico determinístico mensal de dois parâmetros e o modelo estocástico, ARIMA, para simular a vazão mensal das sub-bacias da região hidrográfica do Xingu no Estado do Pará. O objetivo principal foi simular a vazão mensal através dos modelos e comparar os seus resultados. O modelo hidrológico determinístico aplicado possui uma estrutura simples e apresentou bons resultados, porém mostrou-se muito sensível a eventos extremos de precipitação. O modelo estocástico ARIMA, conseguiu capturar a dinâmica das séries temporais, apresentando resultados muito satisfatórios na simulação da vazão mensal nas estações da bacia. Ambos os modelos devem ser aplicados com cautela no período chuvoso, onde ocorrem os eventos extremos de precipitação e consequentemente vazões de pico.Hydrologic modeling is an important tool for the planning and management of water resources use in river basins. In this work, a two-parameter monthly deterministic hydrologic model and the stochastic model, ARIMA, were applied to simulate the monthly runoff of the Xingu river basin in the State of Pará. The main objective of this work is to simulate the monthly runoff using the two models and to compare their results. The applied hydrological deterministic model has a simple structure and presented good results, but seems to be very sensitive to extreme precipitation events. The stochastic model ARIMA was able to capture the dynamic of the temporal series, presenting very satisfactory results for the simulation of the monthly runoff at the basin stations. Both models should be applied with caution during the rainy season, when extreme precipitation events and consequently peaks of runoff occur

Regionalization of Climate Change Simulations for the Assessment of Impacts on Precipitation, Flow Rate and Electricity Generation in the Xingu River Basin in the Brazilian Amazon

This study applied regionalization techniques on future climate change scenarios for the precipitation over the Xingu River Basin (XRB) considering the 2021–2080 horizon, in order to assess impacts on the monthly flow rates and possible consequences for electricity generation at the Belo Monte Hydroelectric Power Plant (BMHPP). This is the fourth largest hydroelectric power plant in the world, with a generating capacity of 11,233 MW, and is located in the Brazilian Amazon. Two representative concentration pathways (RCP 4.5 and RCP 8.5) and an ensemble comprising four general circulation models (CanESM2, CNRM-CM5, MPI-ESM-LR and NORESM1-M) were used. The projections based on both scenarios indicated a considerable decrease in precipitation during the rainy season and a slight increase during the dry season relative to the reference period (1981–2010). According to the results, a reduction in the flow rates in Altamira and in the overall potential for power generation in the BMHPP are also to be expected in both analyzed periods (2021–2050 and 2051–2180). The RCP 4.5 scenario resulted in milder decreases in those variables than the RCP 8.5. Conforming to our findings, a reduction of 21.3% in the annual power generation at the BMHPP is expected until 2080, with a corresponding use of 38.8% of the maximum potential of the facility. These results highlight the need for investments in other renewable energy sources (e.g., wind and solar) in order to compensate for the upcoming losses in the BMHPP production

Regionalization of Climate Change Simulations for the Assessment of Impacts on Precipitation, Flow Rate and Electricity Generation in the Xingu River Basin in the Brazilian Amazon

This study applied regionalization techniques on future climate change scenarios for the precipitation over the Xingu River Basin (XRB) considering the 2021–2080 horizon, in order to assess impacts on the monthly flow rates and possible consequences for electricity generation at the Belo Monte Hydroelectric Power Plant (BMHPP). This is the fourth largest hydroelectric power plant in the world, with a generating capacity of 11,233 MW, and is located in the Brazilian Amazon. Two representative concentration pathways (RCP 4.5 and RCP 8.5) and an ensemble comprising four general circulation models (CanESM2, CNRM-CM5, MPI-ESM-LR and NORESM1-M) were used. The projections based on both scenarios indicated a considerable decrease in precipitation during the rainy season and a slight increase during the dry season relative to the reference period (1981–2010). According to the results, a reduction in the flow rates in Altamira and in the overall potential for power generation in the BMHPP are also to be expected in both analyzed periods (2021–2050 and 2051–2180). The RCP 4.5 scenario resulted in milder decreases in those variables than the RCP 8.5. Conforming to our findings, a reduction of 21.3% in the annual power generation at the BMHPP is expected until 2080, with a corresponding use of 38.8% of the maximum potential of the facility. These results highlight the need for investments in other renewable energy sources (e.g., wind and solar) in order to compensate for the upcoming losses in the BMHPP production