Climate informed monthly streamflow forecasts for the Brazilian hydropower network using a periodic ridge regression model

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: s u m m a r y Streamflow simulation and forecasts have been widely used in water resources management, particularly for flood and drought analysis and for the determination of optimal operational rules for reservoir systems used for water supply and energy production. Here we include climate information in a periodic-auto-regressive model in order to provide monthly streamflow forecasts for 54 hydropower sites in Bra-zil. Large scale climate information is included in the model through the use of climate indices obtained from the sea surface temperature field of the tropical Pacific and subtropical Atlantic oceans and the low-level zonal wind field over southeast Brazil. Correlation analysis of climate predictors and stream-flow data show that the dependence of the latter on climate variability is seasonal and also a function of the lead time of the forecasts. A ridge regression framework is adopted in order to shrink parameter estimates and improve model outputs. The proposed model is compared with an ordinary linear regression based model with predictors selected by the BIC criterion and with the classical linear periodic-auto-regressive model (PAR), where no climate information is used. Cross-validated results show that the inclusion of climate indexes is able to improve forecast skills up to 3 months lead time. Higher skills are observed for reservoirs with large catchment areas. Introduction The ability to adapt management of water resources systems using climate based forecasts is being highlighted as a key climate change adaptation strategy. This is particularly important for the Brazilian hydropower system which supplies most of the electrical energy for Brazil. With more than 70 interconnected (hydraulically and through transmission lines of electrical energy) hydropower reservoirs that account for approximately 79% of the 91 GW installed capacity (Marreco and Carpio, 2006) in the country, the Brazilian hydropower system needs to use streamflow forecasts (e.g. Costa et al., 2007) and optimization models (e.g. Barros et al., 2003) to produce electrical energy with expected maximum reliability and minimum cost. Since the 2001 failure in hydroenergy supply that resulted in a significant decline in the GDP growth rate (1.5% in 2001 against 4.4% in 2000, Gomide, 2004), the system's national operator (ONS), has looked for more skillful …