Attribution of hydrologic forecast uncertainty within scalable forecast windows
Hindcasts based on the extended streamflow prediction (ESP) approach are carried out in a typical rainfall-dominated basin in China, aiming to examine the roles of initial conditions (IC), future atmospheric forcing (FC) and hydrologic model uncertainty (MU) in streamflow forecast skill. The combined effects of IC and FC are explored within the framework of a forecast window. By implementing virtual numerical simulations without the consideration of MU, it is found that the dominance of IC can last up to 90 days in the dry season, while its impact gives way to FC for lead times exceeding 30 days in the wet season. The combined effects of IC and FC on the forecast skill are further investigated by proposing a dimensionless parameter ( β) that represents the ratio of the total amount of initial water storage and the incoming rainfall. The forecast skill increases exponentially with β, and varies greatly in different forecast windows. Moreover, the influence of MU on forecast skill is examined by focusing on the uncertainty of model parameters. Two different hydrologic model calibration strategies are carried out. The results indicate that the uncertainty of model parameters exhibits a more significant influence on the forecast skill in the dry season than in the wet season. The ESP approach is more skillful in monthly streamflow forecast during the transition period from wet to dry than otherwise. For the transition period from dry to wet, the low skill of the forecasts could be attributed to the combined effects of IC and FC, but less to the biases in the hydrologic model parameters. For the forecasts in the dry season, the skill of the ESP approach is heavily dependent on the strategy of the model calibration.