Journal
JOURNAL OF HYDROLOGY
Volume 519, Issue -, Pages 2967-2977Publisher
ELSEVIER
DOI: 10.1016/j.jhydrol.2014.05.045
Keywords
Streamflow forecasting; Data assimilation; Multi-modeling; Ensemble streamflow prediction
Funding
- NOAA-MAPP [NA11OAR4310140]
Ask authors/readers for more resources
Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. This study proposes a method to account for all threes sources of uncertainty, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. In currently available forecast products, only a partial accounting of uncertainty is performed, with the focus primarily on meteorological forcing. For example, the Ensemble Streamflow Prediction (ESP) technique uses meteorological climatology to estimate total uncertainty, thus ignoring initial condition and modeling uncertainty. In order to manage all three sources of uncertainty, this study combines ESP with ensemble data assimilation, to quantify initial condition uncertainty, and Sequential Bayesian Combination, to quantify model errors. This gives a more complete description of seasonal hydrologic forecasting uncertainty. Results from this experiment suggest that the proposed method increases the reliability of probabilistic forecasts, particularly with respect to the tails of the predictive distribution. (C) 2014 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available