RESUMO
Hydroelectric power reservoirs are considered potential contributors to the greenhouse effect in the atmosphere through the emittance of methane and carbon dioxide. We combined in situ sampling and gas chromatography with geostatistical and remote sensing approaches to estimate greenhouse gas (GHG) emissions of a large hydropower reservoir. We used remote sensing data to estimate the water surface and geospatial interpolation to calculate total emissions as a function of reservoir surface area. The CH4 and CO2 gas concentrations were linearly correlated to sampling time, confirming the adequacy of the in situ sampling method to measure GHG diffusive fluxes from reservoir water surfaces. The combination of high purity (99.99%) ISO-norm gas standards with a gas chromatograph, enabled us to achieve low measurement detection limits of 0.16 and 0.60⯵molâ¯mol-1, respectively, for CH4 (using a flame ionization or FID detector) and CO2 (using a thermal conductivity or TCD detector). Our results show that CO2 emissions are significantly (an order of 5.102-103) higher than those of CH4 in both the spatial and temporal domain for this reservoir. The total diffusive GHG emissions over a year (June 2011 to May 2012) of the Tucuruí hydropower reservoir being in operation, in units of tons of carbon, added up to 6.82â¯×â¯103 for CH4 and 1.19â¯×â¯106 for CO2. We show that in situ GHG sampling using small floating gas chambers and high precision gas chromatography can be combined with geospatial interpolation techniques and remote sensing data to obtain estimates of diffusive GHG emissions from large water bodies with fluctuating water surfaces such as hydropower reservoirs. We recommend that more measurements and observations on these emissions are pursued in order to support and better quantify the ongoing discussions on estimates and mitigation of GHG emissions from reservoirs in the Amazon region and elsewhere in the world.
RESUMO
O objetivo do trabalho foi determinar o tamanho adequado de amostra para estimar o volume de fustes de espécies florestais de uma população de árvores a serem cortadas no sistema de manejo florestal da empresa Cikel Brasil Verde Madeiras - Pará. Utilizaram-se as metodologias da amostragem sistemática e do estimador geoestatístico da krigagem ordinária com simulação sequencial, respectivamente para a escolha das amostras e estimação dos volumes dos fustes das árvores. Os resultados mostraram que os métodos podem ser utilizados no cálculo dos volumes de fustes de árvores. Entretanto, o método da krigagem apresenta um efeito de suavização, tendo como conseqüência uma subestimação dos volumes calculados. Neste caso, um fator de correção foi aplicado para minimizar o efeito da suavização. A simulação sequencial indicativa apresentou resultados mais precisos em relação à krigagem, uma vez que tal método apresentou algumas vantagens, tal como a não exigência de amostras com distribuições normais e ausência de efeito de suavização, característico dos métodos de interpolação.
The objective of this study was to determine the appropriate size sample to estimate the stem volumes stems of tree species from a population of trees to be cut in the forest management system of the timber company Cikel Brasil Verde Madeiras - Pará State, Brazil. The methodologies of systematic sampling and geostatistical kriging with sequential simulation were used, respectively, for the choice of samples and estimation of stem volumes of trees. The results showed that the methods can be used to calculate the stem volumes of trees. However, the kriging method has a smoothing effect, which resulted in an underestimation of the volumes. In such case, a correction factor was applied to minimize the effect of smoothing. The sequential simulation indicative presented more accurate results compared to kriging, since this method has certain advantages over kriging, such as not requiring samples with normal distributions and no smoothing effect characteristic of the interpolation methods.