IMPACTS OF LAND COVER AND GREENHOUSE GAS (GHG) CONCENTRATION CHANGES ON THE HYDROLOGICAL CYCLE IN AMAZON BASIN: A REGIONAL CLIMATE MODEL STUDY

O modelo regional BRAMS (Brazilian Regional Atmospheric Modeling System) acoplado ao esquema de vegetação dinâmica General Energy and Mass Transport Model (GEMTM) e cenários de usos da terra na Amazônia e de aumento na concentração dos gases do efeito estufa na atmosfera produzidos a partir das simulações climáticas do Modelo de Circulação Geral Community Climate System Model (CCSM3), do National Center for Atmospheric Research (NCAR), são utilizados para avaliar os impactos no ciclo hidrológico da bacia amazônica. A projeção de desflorestamento para o ano de 2050 e cenário de emissão dos gases do efeito estufa (A2) afetam de forma significativa os balanços de energia e de água, a estrutura dinâmica da atmosfera e, consequentemente, a convergência de umidade e massa na bacia. As mudanças são mais intensas na simulação que existe o efeito combinando do desflorestamento e aumento dos gases do efeito estufa. No cenário de desflorestamento, o mecanismo de retroalimentação positivo é estabelecido, no qual as alterações na circulação regional reduziram a convergência de umidade e a precipitação na região. Nos cenários de aumento dos gases do efeito estufa, sem e com desflorestamento, o mecanismo de retroalimentação é negativo (positivo) na estação úmida (seca), no qual as mudanças na circulação regional também conduziram a redução na precipitação. Os resultados indicam que a rápida destruição da floresta e as mudanças no clima regional decorrente de ações antropogênicas podem tornar-se um processo irreversível, e que as mudanças no ciclo hidrológico e as perturbações na complexa relação solo-planta-atmosfera podem desencadear alterações significativas nos ecossistemas naturais da Amazônia, já que os mesmos não apresentam grande capacidade de adaptação à magnitude das mudanças no clima

Uma analise diagnostica sobre o metodo DELTA-EDDINGTON para avaliação de irradiancias.

The reliability of the Delta-Eddington method for computing shortwave irradiances in plane-parallel atmospheres is discussed from comparisons with benchmark calculations under specified conditions. Two different choices were adopted for the fractional scattering into the forward peak, the key parameter in this method. The application of these choices provided almost the same absorptances, and associated to very similar systematic errors. These results indicate that such systematic errors would be related mainly to the basic assumptions of the Delta-Eddington method, like the suppression of high-order moments of the phase function in estimating the backscattering of sun direct radiation

The Master Super Model Ensemble System (MSMES)

A statistical model of weather forecast has been implemented at the weather laboratory at the University of São Paulo (MASTER/IAG/USP Laboratory - www.master.iag.usp.br). This statistical forecast is obtained on a routine daily basis from an ensemble of six global models and fourteen regional models of numerical weather prediction (NWP). The optimal combination of the several individual forecasts is obtained by the weighted mean of the forecasts after bias removal. The weights are provided by the inverse of the mean square error (MSE) of each forecast. The evaluation metric is based on the fit of the forecast to the surface data. METAR, SYNOP and the Center for Weather Forecasting and Climate Studies CPTEC automatic weather stations. . The predicted variables are: a) temperature; b) dew point temperature; c) zonal wind; d) meridional wind; e) sea level pressure; f) precipitation. Precipitation estimates provided by TRMM, NAVY and CPTEC are treated separately. To evaluate the statistical model, the MSE and bias averaged in 15 days period are calculated for each station. The choice of the 15 days period is based on the fact that the forecast errors are somewhat influences by the intraseasonal oscillation. Real time forecasts are available at the MASTER homepage. The statistical models evaluation indicates that the products are very robust and competitive. Separate evaluation is provided for different regions of Brazil and the statistical combination is better than any individual forecast in the mean sense. Concerning precipitation, the results are not statistically sound for the 6 hour accumulated precipitation (TRMM and NAVY), but for 24 hours accumulated precipitation the results are very robust. To appraise the accuracy of this forecast an uncertainness index is calculated. Low values of this index indicate that there isn't large dispersion between forecasts and also indicate that these forecasts are similar to statistical model forecast, thus increasing the confidence in the statistical forecasts.Pages: 1751-175

Air pollution and respiratory diseases: ecological time series

ABSTRACT CONTEXT AND OBJECTIVE: Exposure to air pollutants is one of the factors responsible for hospitalizations due to respiratory diseases. The objective here was to estimate the effect of exposure to particulate matter (such as PM2.5) on hospitalizations due to certain respiratory diseases among residents in Volta Redonda (RJ). DESIGN AND SETTING: Ecological time series study using data from Volta Redonda (RJ). METHODS: Data on hospital admissions among residents of Volta Redonda (RJ), between January 1, 2012, and December 31, 2012, due to pneumonia, acute bronchitis, bronchiolitis and asthma, were analyzed. Daily data on PM2.5 concentrations were estimated through the CCATT-BRAMS model. The generalized additive Poisson regression model was used, taking the daily number of hospitalizations to be the dependent variable and the PM2.5 concentration to be the independent variable, with adjustment for temperature, relative humidity, seasonality and day of the week, and using lags of zero to seven days. Excess hospitalization and its cost were calculated in accordance with increases in PM2.5 concentration of 5 µg/m3. RESULTS: There were 752 hospitalizations in 2012; the average concentration of PM2.5 was 17.2 µg/m3; the effects of exposure were significant at lag 2 (RR = 1.017), lag 5 (RR = 1.022) and lag 7 (RR = 1,020). A decrease in PM2.5 concentration of 5 µg/m3 could reduce admissions by up to 76 cases, with a decrease in spending of R$ 84,000 a year. CONCLUSION: The findings from this study provide support for implementing public health policies in this municipality, which is an important steelmaking center

Air pollution and respiratory diseases: ecological time series

ABSTRACT CONTEXT AND OBJECTIVE: Exposure to air pollutants is one of the factors responsible for hospitalizations due to respiratory diseases. The objective here was to estimate the effect of exposure to particulate matter (such as PM2.5) on hospitalizations due to certain respiratory diseases among residents in Volta Redonda (RJ). DESIGN AND SETTING: Ecological time series study using data from Volta Redonda (RJ). METHODS: Data on hospital admissions among residents of Volta Redonda (RJ), between January 1, 2012, and December 31, 2012, due to pneumonia, acute bronchitis, bronchiolitis and asthma, were analyzed. Daily data on PM2.5 concentrations were estimated through the CCATT-BRAMS model. The generalized additive Poisson regression model was used, taking the daily number of hospitalizations to be the dependent variable and the PM2.5 concentration to be the independent variable, with adjustment for temperature, relative humidity, seasonality and day of the week, and using lags of zero to seven days. Excess hospitalization and its cost were calculated in accordance with increases in PM2.5 concentration of 5 µg/m3. RESULTS: There were 752 hospitalizations in 2012; the average concentration of PM2.5 was 17.2 µg/m3; the effects of exposure were significant at lag 2 (RR = 1.017), lag 5 (RR = 1.022) and lag 7 (RR = 1,020). A decrease in PM2.5 concentration of 5 µg/m3 could reduce admissions by up to 76 cases, with a decrease in spending of R$ 84,000 a year. CONCLUSION: The findings from this study provide support for implementing public health policies in this municipality, which is an important steelmaking center

Short term ensemble flood forecasting

Nesse trabalho é apresentada uma aplicação da abordagem da previsão de cheias por conjunto em curto prazo a uma bacia de médio porte localizada na região sudeste do Brasil, a bacia do rio Paraopeba. Para geração das previsões de vazões, a metodologia utiliza um conjunto de previsões de precipitação associada à modelagem chuva-vazão conceitual com o modelo hidrológico MGB-IPH. O experimento foi realizado durante três períodos chuvosos entre os anos de 2008 e 2011. Como parâmetro de referência na avaliação do desempenho das previsões por conjunto é utilizada uma previsão hidrológica determinística, baseada em uma previsão de precipitação única obtida da combinação ótima de diversas saídas de modelos meteorológicos, com diferentes condições iniciais e parametrizações. Nos resultados das avaliações das previsões de eventos do tipo dicótomos, que consideram a superação ou não de níveis ou vazões limite de alerta, as previsões por conjunto mostraram superioridade em relação à previsão determinística, sendo possível obter na maior parte dos casos analisados um aumento na proporção de detecções corretas da ocorrência do evento de cheia mantendo as taxas de alarmes falsos em níveis reduzidos. Esse benefício foi, de modo geral, maior em maiores antecedências e vazões limite de alerta, situações mais importantes num contexto de prevenção de cheias.The forecasting and issuing of early warnings are a key element to prevent the impact of flood events. An alternative to extend the forecasting horizon is the use of rainfall-runoff modeling coupled with precipitation forecasts derived from numerical weather prediction (NWP) models. The present research assesses the performance of short term ensemble flood forecasting in a medium size tropical basin, based on data and streamflow forecasting tools available in operational mode in Brazil. The Paraopeba River basin (12,150 km²), located in the upper portion of the São Francisco River basin, in Southeastern Brazil, was selected as a case study. The proposed methodology used the MGB-IPH hydrological model coupled to an ensemble of precipitation forecasts generated by several NWP models with different initial conditions and parameterizations. The results are several scenarios of streamflow forecasts. A single deterministic streamflow forecast, based on a quantitative precipitation forecast derived from the optimal combination of several outputs of NWP models, was used as a reference to assess the performance of the streamflow ensemble forecasts. The results of the ensemble flood forecasting were assessed by deterministic and probabilistic performance measures, with the ensemble mean being used by the former, and specific assessment measured by the latter. Based on the deterministic assessment, the ensemble mean showed similar results to those obtained by the deterministic reference forecast, although presenting a better performance over most of the ensemble members. Based on the probabilistic performance measures, results for predictions of dichotomous events which consider whether warming limit flows are surpassed or not, showed that the 9th decile of the ensemble was superior to the deterministic forecast and even the ensemble mean. In most cases, an increase was observed in the proportion of correctly forecasted events while keeping false alarm rates at low levels. This benefit was generally higher for higher flow thresholds and for longer lead times, which are the most important parameters for flood mitigation

Poluentes do ar e internações devido a doenças cardiovasculares em São José do Rio Preto, Brasil

Resumo O presente estudo teve como objetivo estimar os efeitos de poluentes ambientais sobre o número de internações por doenças cardiovasculares. Foi um estudo ecológico com dados de internações hospitalares de residentes em São José do Rio Preto, São Paulo, Brasil, com diagnóstico nas categorias de I-00 a I-99, entre 01/10/11 e 30/09/12. Os poluentes analisados foram partículas finas (PM2,5), ozônio, monóxido de carbono, óxido de nitrogênio e dióxido de nitrogênio. Foram estimados pelo modelo CCATT-BRAMS. O uso do modelo aditivo de regressão de Poisson foi utilizado para estimar associação entre a exposição ao PM2,5 e internação por doença cardiovascular. Foram calculados os excessos de internação e os gastos por estas doenças. Observou-se que a exposição ao PM2,5 no quinto dia após a exposição (lag 5) foi significativo para internação e aumentou em 15 ppts segundo incremento de 10µg /m3 na concentração de PM2,5. Foram identificadas 650 internações evitáveis com custos da ordem de R$ 1,9 milhão. Desse modo, foi possível identificar associação entre exposição ao PM2,5 e internações devido a doenças cardiovasculares em cidades de médio porte como São José do Rio Preto fornecendo subsídios aos gestores municipal e regional de Saúde

Synergetic measurements of aerosols over Sao Paulo, Brazil using LIDAR, sunphotometer and satellite data during the dry season

A backscattering LIDAR system, the first of this kind in Brazil, has been set-up in a suburban area in the city of Sao Paulo (23degrees33'S, 46degrees44' W) to provide the vertical profile of the aerosol backscatter coefficient at 532 nm up to an altitude of 4-6 km above sea level (asl). The measurements have been carried out during the second half of the so-called Brazilian dry season, September and October 2001 and during the first half of the dry season in August and September 2002. The LIDAR data are presented and analysed in synergy with aerosol optical thickness (AOT) measurements obtained by a CIMEL sun-tracking photometer in the visible spectral region and with satellite measurements obtained by the MODIS sensor. This synergetic approach has been used, not only to validate the LIDAR data, but also to derive a typical value ( 45 sr) of the so-called extinction-to-backscatter ratio (LIDAR ratio) during the dry season. The satellite data analysis offers additional information on the spatial distribution of aerosols over Brazil including the determination of aerosol source regions over the country. The LIDAR data were also used to retrieve the Planetary Boundary Layer (PBL) height, aerosol layering and the structure of the lower troposphere over the city of Sao Paulo. These first LIDAR measurements over the city of Sao Paulo during the dry season showed a significant variability of the AOT in the lower troposphere (0.5-5 km) at 532 nm. It was also found that the aerosol load is maximized in the 1-3 km height region, although up to 3 km thick aerosol layers were also detected in the 2.5-5.5 km region in certain cases. Three-dimensional 96-hours air mass back-trajectory analysis was also performed in selected cases to determine the source regions of aerosols around Sao Paulo during the dry season.Pages: 1523-153