Kelly de Oliveira Barros

Itaipu royalties: The role of the hydroelectric sector in water resource management

For countries dependent on hydroelectricity, water scarcity poses a real risk. Hydroelectric plants are among the most vulnerable enterprises to climate change. Investing in the conservation of the hydrographic basin is a solution found by the hydropower sector. Given the importance of the Itaipu plant to the energy matrix of Brazil and Paraguay, the aim of this study is to review the current distribution of royalties from Itaipu, using the hydrographic basin as a of criterion of analysis. Approximately 98.73% of the Itaipu basin is in Brazil. The flow contributes 99% of the total electricity generated there, while the drop height of the water contributes only 1%. Under the current policy, royalties are shared equally between Brazil and Paraguay. In the proposed approach, each country would receive a percentage for their participation in the drop height and water flow in the output of the turbines, which are intrinsic factors for electricity generation. Thus, Brazil would receive 98.35% of the royalties and Paraguay, 1.65%. The inclusion of the hydrographic basin as a criterion for the distribution of royalties will promote more efficient water resource management, since the payment will be distributed throughout the basin of the plant. The methodology can be applied to hydroelectric projects worldwide.

Markov chains and cellular automata to predict environments subject to desertification

The foremost objective of this study was to analyze the performance of a Markov chain/cellular automata model for predicting land use/land cover changes in environments predisposed to desertification. The study area is the Vieira river basin, located in Montes Claros (MG, Brazil). Land use/land cover prognosis was performed for the year 2005 so that this result could be compared with the ranked image for the same year, taken as ground truth. Kappa indices were used to evaluate the change level that occurred between these two cases. Results from cellular automata were evaluated from those of the Markov chain model. The latter proved to be efficient in the quantitative prediction of changes in land use/land cover. Regarding the cellular automata, an average performance was noted in the spatial distribution of classes. Specifically, with regard to desertification, the use of the CA-Markov model was effective at estimating the total area of the most susceptible class to this process, Bare Soil; however, it was inefficient in its spatialization. Even with the caveats related to the performance of cellular automata, the overall prediction capacity of CA-Markov models can be considered as good.

O uso de SIG no inventário de árvores no campus do instituto politécnico de Bragança (IPB)-Portugal

This article aimed to realize a forest inventory with the incorporation of Geographic Information Systems. The study was carried out at the Santa Apolonia campus, Polytechnic Institute of Braganca, Braganca (IPB), Portugal. In addition of the inventory, it was realized recommendations and the spatial distribution of the individuals surveyed, and individually, the most common species. The use of Geographic Information Systems was a key tool to support the urban inventory accomplished. From the characteristics and spatial distribution can be realized the need to prepare an arborization plan for the area in the campus in study. Thus, measures related to the management of the species can be efficiently applied.