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Evaluation of the impacts of climate change on streamflow through hydrological simulation and under downscaling scenarios: case study in a watershed in southeastern Brazil.

Gabriela Leite NevesMariana Abibi Guimarães Araujo BarbosaPhelipe da Silva AnjinhoTainá Thomassim GuimarãesJorim Sousa das Virgens FilhoFrederico Fábio Mauad
Published in: Environmental monitoring and assessment (2020)
Among the problems related to water security, the effects of climate change on water availability stand out. Researchers have used hydrological models integrated with climate models in order to predict the streamflow behaviour in different hydrographic basins. This work aimed to analyse future climate scenarios for the Ribeirão do Lobo River Basin, located in the state of São Paulo, Brazil. The stochastic generator PGECLIMA_R was used in the simulation of climate data, which were used as input data in the hydrological model SMAP, after it was calibrated and validated for the study site. In all, five future scenarios were generated, with scenarios A, B, C and D projected based on the 5th report of the IPCC and scenario E based on the trend of climate data in the region. Among the scenarios generated, scenario D, which considers an increase of 4.8 °C in air temperature and a reduction of 10% in rainfall, is responsible for the worst water condition in the basin and can reduce up to 72.41% of the average flow and up to 55.50%, 54.18% and 38.17% of the low flow parameters Q90%, Q95% and Q7,10, respectively, until the end of the twenty-first century. However, the E scenario also becomes a matter of concern, since it was responsible for greater increases in temperature and greater reductions in rainfall and, consequently, more drastic monthly reductions in streamflow, which may negatively impact water resources and affect the various uses of water in the Ribeirão do Lobo River Basin.
Keyphrases
  • climate change
  • human health
  • electronic health record
  • big data
  • mental health
  • current status
  • machine learning
  • deep learning
  • artificial intelligence
  • drug induced