Uncertainty assessment of hydrologic and climate forecast models in Northeastern Brazil
Article first published online: 7 FEB 2012
Copyright © 2011 John Wiley & Sons, Ltd.
Volume 26, Issue 25, pages 3875–3885, 15 December 2012
How to Cite
Kwon, H.-H., de Assis de Souza Filho, F., Block, P., Sun, L., Lall, U. and Reis, D. S. (2012), Uncertainty assessment of hydrologic and climate forecast models in Northeastern Brazil. Hydrol. Process., 26: 3875–3885. doi: 10.1002/hyp.8433
- Issue published online: 3 DEC 2012
- Article first published online: 7 FEB 2012
- Accepted manuscript online: 27 NOV 2011 09:20PM EST
- Manuscript Accepted: 10 NOV 2011
- Manuscript Received: 26 JUL 2010
- streamflow forecast;
- climate forecast;
- hydrologic model;
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.