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Seasonal streamflow forecasts based on climate information can guide water managers toward superior reservoir operations, leading to improved water resources management efficiency. Uncertainty, however, is always present in seasonal streamflow forecasts, affecting the forecast value. Thus, a forecast should not be considered complete without a description of its uncertainty, which is critical for climate risk and water resources management. This study investigates the uncertainties of a seasonal streamflow forecast system for Northeastern Brazil based on climate precipitation forecasts and hydrologic modeling. These two sources of uncertainty are treated independently and then compared in order to guide future investments in the forecast system. Sea surface temperature is considered to be the primary source of uncertainty for the seasonal precipitation forecasts, based upon a 10-member climate model ensemble. Parameter uncertainty is considered to be the only source of uncertainty for the hydrologic model. Estimation of parameter uncertainty is estimated by the Shuffled Complex Evolution Metropolis algorithm, which employs a Markov Chain Monte Carlo scheme to provide the posterior distribution of the parameters and form uncertainty bounds on streamflow forecasts. Results indicate that uncertainties associated with the climate forecast are much larger than those from parameter estimation in the hydrologic model. Although model structure has not been considered in the evaluation of hydrologic uncertainties, this study indicates that future efforts to address the predominant source of uncertainty should focus on the climate prediction models. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: Efficient management of water and energy is an important goal of sustainable development for any nation. Streamflow forecasts, have been used in complex optimization models to maximize water use efficiency and electrical energy production. In this paper we develop a statistical model for the long term forecasts of hydroenergy inflow into the Brazilian hydropower system, which consists of more than 70 hydropower reservoirs. At present, the planning of reservoir operation and energy production in Brazil is made with no reliable long term (one season or longer lead times) streamflow forecasts. Here we use the NINO3 index and the main modes of the tropical Pacific thermocline structure as climate predictors in order to achieve skillfull forecasts at long leads. Cross-validated results show that about 50% of the total hydroenergy inflow can be predicted with moderate accuracy up to 20 month lead time. [Copyright &y& Elsevier]

Summary: 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 Brazil. 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 sub-tropical Atlantic oceans and the low-level zonal wind field over southeast Brazil. Correlation analysis of climate predictors and streamflow 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. [Copyright &y& Elsevier]

We assess the potential benefits from innovative forecasts of the stream flows that replenish reservoirs in the semi-arid state of Ceará, Brazil. Such forecasts have many potential applications. In Ceará, they matter for both water-allocation and participatory-governance issues that echo global debates. Our qualitative analysis, based upon extensive fieldwork with farmers, agencies, politicians and other key actors in the water sector, stresses that forecast value changes as a society shifts. In the case of Ceará, current constraints on the use of these forecasts are likely to be reduced by shifts in water demand, water allocation in the agricultural Jaguaribe Valley, participatory processes for water allocation between this valley and the capital city of Fortaleza, and risk perception. Such changes in the water sector can also have major distributional impacts. [ABSTRACT FROM AUTHOR]