Accurate seasonal streamflow forecasting is important in reservoir operation, watershed planning, and water resource management, and streamflow forecasting is often based on hydrological models driven by coupled global climate models (CGCMs). To understand streamflow forecasting predictability, this study considered the three largest rivers in China and explored deterministic and probabilistic skill metrics on the monthly scale according to ensemble streamflow hindcasts from the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) driven by multiple climate forcings from the climate system model by the Beijing Climate Center (BCC_CSM1.1m). The effects of initial conditions (ICs) and meteorological forcings (MFs) on skill were investigated using the conventional ensemble streamflow prediction (ESP) and reverse-ESP (revESP). The results revealed the following: (1) Skill declines as lead time increases, and forecasting is generally the most skillful for lead month 1; (2) skill is higher for dry rivers than wet rivers, and higher for dry target months than wet months for the Yellow and Yangtze Rivers, suggesting greater skill in potential drought forecasting than flood forecasting; (3) the relative operating characteristic (ROC) area is greater for abnormal terciles than the near-normal tercile for all three rivers, greater for the above-normal tercile than the below-normal tercile for the Yellow and Yangtze Rivers, but slightly greater for the below-normal tercile than the above-normal tercile for the Xijiang River; and (4) the influence of ICs outweighs that of MFs in dry months, and the period of influence varies from 1 to 3 months; however, the influence of MFs is dominant in wet target months. These findings will help improve the understanding of both the seasonal streamflow forecasting predictability based on coupled climate system/hydrological models and of streamflow forecasting for variable rivers and seasons.
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