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Sökning: WFRF:(Barbosa da Silva António) > (2020-2023)

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1.
  • Tavares, Julia, et al. (författare)
  • Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests
  • 2023
  • Ingår i: Nature. - : Springer Nature. - 0028-0836 .- 1476-4687. ; 617:7959, s. 111-117
  • Tidskriftsartikel (refereegranskat)abstract
    • Tropical forests face increasing climate risk(1,2), yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, ?(50)) and hydraulic safety margins (for example, HSM50) are important predictors of drought-induced mortality risk(3-5), little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters ?(50) and HSM50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both ?(50) and HSM50 influence the biogeographical distribution of Amazon tree species. However, HSM50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM50 are gaining more biomass than are low HSM(50 )forests. We propose that this may be associated with a growth-mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM50 in the Amazon(6,7), with strong implications for the Amazon carbon sink.
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2.
  • Coelho, Frederico, et al. (författare)
  • Forecasting power load curves from spatial and temporal mobile data
  • 2020
  • Ingår i: World Review of Science, Technology and Sustainable Development. - 1741-2234 .- 1741-2242. ; 16:1, s. 4-21
  • Tidskriftsartikel (refereegranskat)abstract
    • This work aims at applying computational intelligence approaches to telecommunication data, in order to associate mobile data to energy consumption load curves. Clustering methods are applied in order to allow the telecommunication network to infer about its topology and consumption load forecasting. Through an extensive analysis of Telecom Italia dataset and power distribution lines data available for the city of Trento, it was possible to confirm the high correlation between them, mainly when voice data is considered. To a great extent, this correlation can be explained by the fact that cellular communication devices are physically present in the service area of the distribution lines and when people are communicating, they are also consuming energy. Based on the aforementioned dataset, load curves for the city of Trento were constructed having as inputs data from telecommunication transactions. Results show that it is possible to use the telecommunication load as the input to predict the energy load, with the proposed model performing better than the naive predictor in 82% of the tested distribution lines.
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