Regionalization of Climate Change Simulations for the Assessment of Impacts on Precipitation, Flow Rate and Electricity Generation in the Xingu River Basin in the Brazilian Amazon

This study applied regionalization techniques on future climate change scenarios for the precipitation over the Xingu River Basin (XRB) considering the 2021–2080 horizon, in order to assess impacts on the monthly flow rates and possible consequences for electricity generation at the Belo Monte Hydroelectric Power Plant (BMHPP). This is the fourth largest hydroelectric power plant in the world, with a generating capacity of 11,233 MW, and is located in the Brazilian Amazon. Two representative concentration pathways (RCP 4.5 and RCP 8.5) and an ensemble comprising four general circulation models (CanESM2, CNRM-CM5, MPI-ESM-LR and NORESM1-M) were used. The projections based on both scenarios indicated a considerable decrease in precipitation during the rainy season and a slight increase during the dry season relative to the reference period (1981–2010). According to the results, a reduction in the flow rates in Altamira and in the overall potential for power generation in the BMHPP are also to be expected in both analyzed periods (2021–2050 and 2051–2180). The RCP 4.5 scenario resulted in milder decreases in those variables than the RCP 8.5. Conforming to our findings, a reduction of 21.3% in the annual power generation at the BMHPP is expected until 2080, with a corresponding use of 38.8% of the maximum potential of the facility. These results highlight the need for investments in other renewable energy sources (e.g., wind and solar) in order to compensate for the upcoming losses in the BMHPP production

Regionalization of Climate Change Simulations for the Assessment of Impacts on Precipitation, Flow Rate and Electricity Generation in the Xingu River Basin in the Brazilian Amazon

This study applied regionalization techniques on future climate change scenarios for the precipitation over the Xingu River Basin (XRB) considering the 2021–2080 horizon, in order to assess impacts on the monthly flow rates and possible consequences for electricity generation at the Belo Monte Hydroelectric Power Plant (BMHPP). This is the fourth largest hydroelectric power plant in the world, with a generating capacity of 11,233 MW, and is located in the Brazilian Amazon. Two representative concentration pathways (RCP 4.5 and RCP 8.5) and an ensemble comprising four general circulation models (CanESM2, CNRM-CM5, MPI-ESM-LR and NORESM1-M) were used. The projections based on both scenarios indicated a considerable decrease in precipitation during the rainy season and a slight increase during the dry season relative to the reference period (1981–2010). According to the results, a reduction in the flow rates in Altamira and in the overall potential for power generation in the BMHPP are also to be expected in both analyzed periods (2021–2050 and 2051–2180). The RCP 4.5 scenario resulted in milder decreases in those variables than the RCP 8.5. Conforming to our findings, a reduction of 21.3% in the annual power generation at the BMHPP is expected until 2080, with a corresponding use of 38.8% of the maximum potential of the facility. These results highlight the need for investments in other renewable energy sources (e.g., wind and solar) in order to compensate for the upcoming losses in the BMHPP production

Evaluation of Surface Data Simulation Performance with the Brazilian Global Atmospheric Model (BAM)

In this study, we evaluated the performance of the Brazilian Global Atmospheric Model (BAM), in its version 2.2.1, in the representation of the surface variables solar radiation, temperature (maximum, minimum, and average), and wind speed. Three experiments were carried out for the period from 2016 to 2022 under three different aerosol conditions (constant (CTE), climatological (CLIM), and equal to zero (ZERO)), discarding the first year as a spin-up period. The observations came from a high-resolution gridded analysis that provides Brazil with robust data based on observations from surface stations on a daily scale from 1961 to 2020; therefore, combining the BAM outputs with the observations, our intercomparison period took place from 2017 to 2020, for three timescales: daily, 10-day average, and monthly, targeting different applications. In its different simulations, BAM overestimated solar radiation throughout Brazil, especially in the Amazon; underestimated temperature in most of the northeast, southeast, and south regions; and overestimated in parts of the north and mid-west; while wind speed was only not overestimated in the Amazon region. In relative terms, the simulations with constant aerosol showed better performance than the others, followed by climatological conditions and zero aerosol. The dexterity indices applied in the intercomparison between BAM and observations indicate that BAM needs adjustments and calibration to better represent these surface variables. Where model deficiencies have been identified, these can be used to drive model development and further improve the predictive capabilities