The Effect of Large-Scale Atmospheric Uncertainty on Streamflow Predictability

Abstract

Abstract Hydrological processes are strongly coupled with atmospheric processes related, for example, to precipitation and temperature, and a coupled atmosphere–land surface system is required for a meaningful hydrological forecast. Since the atmosphere is a chaotic system with limited predictability, ensemble forecasts offer a practical tool to predict the future state of the coupled system in a probabilistic fashion, potentially leading to a more complete and informative hydrologic prediction. As ensemble forecasts with coupled meteorological–hydrological models are operationally running at major numerical weather prediction centers, it is currently possible to produce a gridded streamflow prognosis in the form of a probabilistic forecast based on ensembles. Evaluation and improvement of such products require a comprehensive assessment of both components of the coupled system. In this article, the atmospheric component of a coupled ensemble forecasting system is evaluated in terms of its ability to provide reasonable forcing to the hydrological component and the effect of the uncertainty represented in the atmospheric ensemble system on the predictability of streamflow as a hydrological variable. The Global Ensemble Forecast System (GEFS) of NCEP is evaluated following a “perfect hydrology” approach, in which its hydrological component, including the Noah land surface model and attached river routing model, is considered free of errors and the initial conditions in the hydrological variables are assumed accurate. The evaluation is performed over the continental United States (CONUS) domain for various sizes of river basins. The results from the experiment suggest that the coupled system is capable of generating useful gridded streamflow forecast when the land surface model and the river routing model can successfully simulate the hydrological processes, and the ensemble strategy significantly improves the forecast. The expected forecast skill increases with increasing size of the river basin. With the current GEFS system, positive skill in short-range (one to three days) predictions can be expected for all significant river basins; for the major rivers with mean streamflow more than 500 m3 s−1, significant skill can be expected from extended-range (the second week) predictions. Possible causes for the loss of skills, including the existence of systematic error and insufficient ensemble spread, are discussed and possible approaches for the improvement of the atmospheric ensemble forecast system are also proposed.