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To project future river discharge in the Yangtze River basin, artificial neural networks were used in this study. Based on observed precipitation, temperature, and river discharge data from 1961 to 2000, artificial neural networks were trained and calibrated. The paper projects river discharges at Yichang and Datong hydrological stations under three greenhouse gas emission scenarios from 2011 to 2050 in the Yangtze River basin, by applying climate projections of global climate model ECHAM5/MPI-OM. The results show that annual river discharges at two hydrological stations have no obvious trends and their decadal variations are also small under three emission scenarios. Seasonal discharges at Yichang and Datong have different trends during 2011–2050. Ratios of river discharge in four seasons within a year tend to change. [ABSTRACT FROM AUTHOR]

Based on the daily observational precipitation data at 147 stations in the Yangtze River Basin during 1960–2005 and projected daily data of 79 grid cells from the ECHAM5/ MPI-OM model in the 20th and 21st century, time series of precipitation extremes which contain AM (Annual Maximum) and MI (Munger Index) are constructed. The distribution feature of precipitation extremes is analyzed based on the two index series. Three principal results were obtained, as stated in the sequel. (i) In the past half century, the intensity of extreme heavy precipitation and drought events was higher in the mid-lower Yangtze than in the upper Yangtze reaches. Although the ECHAM5 model still can’t capture the precipitation extremes over the Yangtze River Basin satisfactorily, spatial pattern of the observed and the simulated precipitation extremes are much similar to each other. (ii) For quantifying the characteristics of extremely high and extremely low precipitation over the Yangtze River Basin, four probability distributions are used, namely: General Extreme Value (GEV), General Pareto (GPA), General Logistic (GLO), and Wakeby (WAK). It was found that WAK can adequately describe the probability distribution of precipitation extremes calculated from both observational and projected data. (iii) Return period of precipitation extremes show spatially different changes under three greenhouse gas emission scenarios. The 50-year heavy precipitation and drought events from simulated data during 1951–2000 will become more frequent, with return period below 25 years, for the most mid-lower Yangtze region in 2001–2050. The changing character of return periods of precipitation extremes should be taken into account for the hydrological design and future water resources management. [ABSTRACT FROM AUTHOR]

The article presents a study on the hydrological and meteorological change in the Yangtze River Basin in China. Significant changes were found in many monthly precipitation datasets that were collected between 1961 and 2000. It states that the strongest relative increase was detected when the monthly precipitation is low in absolute terms. Some of the changes in the monthly precipitation in spring and summer from April to August, tends to aggravate the flood hazard. It also detected significant upward trends in the discharge of the River Yangtze Basin in the middle and lower regions on summer months.

The article discusses a study of the power law correlation properties of sign and magnitude series based on a series of observation records of flow of the River Yangtze in China. The results gave insights on the linear and nonlinear processes of the water cycle. The documented linkage between the sign series and the linear process can be verified with the delayed vector variance method and the surrogate test. The spectra estimated by detrended fluctuation analysis method show different properties of correlations in both sign and magnitude series.