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This is the second paper of a two-part series onintroducing an experimental seasonal hydrological forecasting system overthe Yellow River basin in northern China. While the natural hydrologicalpredictability in terms of initial hydrological conditions (ICs) isinvestigated in a companion paper, the added value from eight North AmericanMultimodel Ensemble (NMME) climate forecast models with a grand ensemble of99 members is assessed in this paper, with an implicit consideration ofhuman-induced uncertainty in the hydrological models through apost-processing procedure. The forecast skill in terms of anomalycorrelation (AC) for 2 m air temperature and precipitation does notnecessarily decrease over leads but is dependent on the target month due toa strong seasonality for the climate over the Yellow River basin. As thereis more diversity in the model performance for the temperature forecaststhan the precipitation forecasts, the grand NMME ensemble mean forecast hasconsistently higher skill than the best single model up to 6 months forthe temperature but up to 2 months for the precipitation. The NMMEclimate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing modelregionalized over the Yellow River basin to produce forecasts of soilmoisture, runoff and streamflow. And the NMME/VIC forecasts are comparedwith the Ensemble Streamflow Prediction method (ESP/VIC) through 6-monthhindcast experiments for each calendar month during 1982–2010. As verifiedby the VIC offline simulations, the NMME/VIC is comparable to the ESP/VICfor the soil moisture forecasts, and the former has higher skill than thelatter only for the forecasts at long leads and for those initialized in therainy season. The forecast skill for runoff is lower for both forecastapproaches, but the added value from NMME/VIC is more obvious, with anincrease of the average AC by 0.08–0.2. To compare with the observedstreamflow, both the hindcasts from NMME/VIC and ESP/VIC are post-processedthrough a linear regression model fitted by using VIC offline-simulatedstreamflow. The post-processed NMME/VIC reduces the root mean squarederror (RMSE) from the post-processed ESP/VIC by 5–15 %. And the reduction occurs mostly during the transition from wet to dry seasons. With the consideration of the uncertainty in the hydrological models, the added value from climate forecast models is decreased especially at short leads, suggesting the necessity of improving the large-scale hydrological models in human-intervened river basins

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models