The hydrometeoro-logical and hydrological monthly discharge forecasting models are established.

Based on the empirical orthogonal function analytical method,500?hPa,100?hPa and the North Pacific sea surface temperature are used as forecast factors and quantitative model of monthly discharge is established.

Combined with the characteristics of long range hydrologic forecast,PPR method is applied to monthly discharge long range forecast.

Yellow River ,Songhua River and Xi River),the Characteristics for time series change and space change of monthly runoff are studied by random hydrology.

Hydrological alteration diagnosis system was applied to analyze the variation of the Sanshui Station s monthly runoff series from 1959 to 2000.

Linear multi regression of monthly streamflow model is used for analysis.

In view of the differences of the monthly streamflow variation between during the flood season and non-flood season,ensemble prediction model by stages is developed.

The paper introduces the Volterra adaptive filter based on the state space delay-coordinate embedding reconstruction of dynamic system , and predicts average monthly runoff series by second order Volterra adaptive filter, compares the performance with ANN by monthly runoff in 33 years of Shimian Station on Dadu River.

Prediction of monthly runoff based on BP neural network;

Influences of sea surface temperature and meteorological factors on monthly runoff of Hongjiadu Hydropower Station;

Relationship between sea-surface temperature (SST) and monthly runoff of upper Yellow River;