Abstract. We quantify uncertainty in the impacts of climate change on the discharge of Rio Grande, a major tributary of the Paraná River in South America and one of the most important basins in Brazil for water supply and hydroelectric power generation. We consider uncertainty in climate projections associated with the greenhouse-gas emission scenarios (A1b, A2, B1, B2) and increases in global mean air temperature of 1 to 6 • C for the HadCM3 GCM (Global Circulation Model) as well as uncertainties related to GCM structure. For the latter, multimodel runs using 6 GCMs (CCCMA CGCM31, CSIRO Mk30, IPSL CM4, MPI ECHAM5, NCAR CCSM30, UKMO HadGEM1) and HadCM3 as baseline, for a +2 • C increase in global mean temperature. Pattern-scaled GCM-outputs are applied to a large-scale hydrological model (MGB-IPH) of Rio Grande Basin. Based on simulations using HadCM3, mean annual river discharge increases, relative to the baseline or control run period , by +5% to +10% under the SRES emissions scenarios and from +8% to +51% with prescribed increases in global mean air temperature of between 1 and 6 • C. Substantial uncertainty in projected changes to mean river discharge (−28% to +13%) under the 2 • C warming scenario is, however, associated with the choice of GCM. We conclude that, in the case of Rio Grande Basin, the most important source of uncertainty derives from the GCM rather than the emission scenario or the magnitude of rise in mean global temperature.

Precipitation and temperature projections from global models, made available through the Coupled Model Intercomparison Project Phase 5 (CMIP5), and actually used in the AR5 by Intergovernmental Panel on Climate Change -IPCC-AR5), are assessed here for São Francisco River Basin. RCP 4.5 and RCP 8.5 are considered througout the period 2011 to 2100. Incidentally, global models are evaluated concerning their representativity related to 1961-2000 climatology from Brazilian National Meteorological Institute (INMET) data. Two indexes were evaluated: correlation and square mean error. The analisys of projections was performed through the assessment of yearly average values of 30 years period (2011-2040, 2041-2070 e 2071-2100). Additionally to evaluate trends and variability it was considered 10 year moving averages, linear regression and Mann-Kendall-Sen method. Approximately 28% analyzed models do not capture the seasonal precipitation reliably. All models present positive trend for temperature, and despite of divergence on precipitation, the models projected anomalies between -20 and 20 for each time-slice for this variable.

We quantify uncertainty in the impacts of climate change on the discharge of the Rio Grande, a major tributary of the River Paraná in South America and one of the most important basins in Brazil for water supply and hydro-electric power generation. We consider uncertainty in climate projections associated with the SRES (greenhouse-gas) emission scenarios (A1b, A2, B1, B2) and increases in global mean air temperature of 1 to 6 °C for the HadCM3 GCM as well as uncertainties related to GCM structure. For the latter, multimodel runs using 6 GCMs (CCCMA CGCM31, CSIRO Mk30, IPSL CM4, MPI ECHAM5, NCAR CCSM30, UKMO HadGEM1) and HadCM3 as baseline, for a + 2 °C increase in global mean temperature. Pattern-scaled GCM-outputs are applied to a large-scale hydrological model (MGB-IPH) of the Rio Grande Basin. Based on simulations using HadCM3, mean annual river discharge increases, relative to the baseline period (1961–1990), by + 5% to + 10% under the SRES emissions scenarios and from + 8% to + 51% with prescribed increases in global mean air temperature of between 1 and 6 °C. Substantial uncertainty in projected changes to mean river discharge (− 28% to + 13%) under the 2 °C warming scenario is, however, associated with the choice of GCM. We conclude that, in the case of the Rio Grande Basin, the most important source of uncertainty derives from the GCM rather than the emission scenario or the magnitude of rise in mean global temperature