Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing
Article first published online: 5 AUG 2013
©2013. American Geophysical Union. All Rights Reserved.
Water Resources Research
Volume 49, Issue 8, pages 4687–4699, August 2013
How to Cite
2013), Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing, Water Resour. Res., 49, 4687–4699, doi:10.1002/wrcr.20350., , , , and (
- Issue published online: 23 SEP 2013
- Article first published online: 5 AUG 2013
- Accepted manuscript online: 25 JUN 2013 07:55AM EST
- Manuscript Accepted: 4 JUN 2013
- Manuscript Revised: 29 MAY 2013
- Manuscript Received: 2 JAN 2013
- global seasonal streamflow forecasting system;
- relative roles of initial conditions and meteorological forcing
 We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through better climate prediction or by better estimation of ICs through data assimilation depends on the relative importance of these sources of uncertainty. We use the Ensemble Streamflow Prediction (ESP) and reverse ESP (revESP) procedure to explore the impact of both sources of uncertainty at 78 stations on large global basins for lead times upto 6 months. We compare the ESP and revESP forecast ensembles with retrospective model simulations driven by meteorological observations. For each location, we determine the critical lead time after which the importance of ICs is surpassed by that of MF. We analyze these results in the context of prevailing hydroclimatic conditions for larger basins. This analysis suggests that in some basins forecast skill may be improved by better estimation of initial hydrologic states through data assimilation; whereas in others skill improvement depends on better climate prediction. For arctic and snowfed rivers, forecasts of high flows may benefit from assimilation of snow and ice data. In some snowfed basins where the onset of melting is highly sensitive to temperature changes, forecast skill depends on better climate prediction. In monsoonal basins, the variability of the monsoon dominates forecasting skill, except for those where snow and ice contribute to streamflow. In large basins, initial surface water and groundwater states are important sources of skill.