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Abstract: Significant quasi-cycles in precipitation time series of 132 climate stations spread over China from 1951 to 2002 have been detected by applying Autocorrelation Spectral Analysis (ASA). By the same method, significant quasi-cycles have also been identified in time series of the potential influencing factors: Southern Oscillation Index (SOI), Sea Surface Temperature (SST) and Sea Level Pressure (SLP). Similarities between some precipitation time series spectra and the spectrum of a potential driving force have been detected; e.g. several series from the Yangtze River''s middle reaches show 3–4-year cycles which are similar to the SOI and several SST signals. These time series have been further investigated by low-pass filtering with the Savitzky–Golay filter and by means of correlation analysis. It is proved that the SOI and the SSTs of the Bay of Bengal are in relatively stable anti-phase. However, no stable link between these signals and the precipitation series in the Yangtze River basin can be detected. Subsequent analyses of 850hPa winds lead to the outcome that certain wind patterns could either suppress or force the SST and SOI teleconnection signals. [Copyright &y& Elsevier]

Abstract: This study investigated future changes of dryness/wetness in ten large river basins: the Songhuajiang River basin, the Liaohe River basin, the Haihe River basin, the Yellow River basin, the Huaihe River basin, the Yangtze River basin, the Pearl River basin, the Southeast River basins, the Southwest River basins and the Northwest Inland River basins across China during the first 50 years of the 21st century. The study was carried out according to the Standardized Precipitation Index (SPI), which is calculated by monthly precipitation data of ECHAM5/MPI-OM. Three anthropogenic greenhouse gas emission scenarios, SRES-A2, SRES-A1B and SRES-B1 have been considered. The frequency of dry/wet periods in these river basins was calculated and the trend detected by the Mann–Kendall non-parametric test. The results reveal that there is a trend towards drier conditions from northeast to southwest China in scenario A2 and a similar spatial distribution pattern in scenario A1B and B1. Areas with a high frequency of drought are detected under all three scenarios. In the next half century, the Haihe River basin, northeast China, is expected to show a significant trend towards drier conditions, which pass the 95% confidence level in scenario A2. In the Yellow River basin, the Huaihe River basin, the Yangtze River basin and the Pearl River basin, an obvious trend towards wetter conditions has been detected in scenario A1B. The Songhuajiang River basin, the Haihe River basin, the Yellow River basin and the Huaihe River basin show a trend towards wetter conditions, which passed the 90% confidence level in scenario B1. The future dryness/wetness frequency situation is complex in the ten large river basins. [ABSTRACT FROM AUTHOR]

Abstract: Gridded climate data sets are widely used in the analysis, modeling and forecasting of the consequences of climate change. The objective of this study is to compare the impact of different climate datasets (station vs. gridded) on the parameterization of a hydrological model (developed using SWAT2005) of the River Xiangxi, the largest tributary of Yangtze River in the Hubei part of the Three Gorges Reservoir. Climate data used in this study derive from two sources: point daily observations from the Xingshan meteorological station (STN) and gridded (0.5°×0.5°) monthly observations of the CRU TS3.0 global dataset (CRU) downscaled to daily data using a weather generator. Data from 1970 to 1974 were applied for sensitivity analyses and autocalibration and subsequently validate hindcasts over the period 1976–1986. Despite there being only slight differences in mean annual precipitation (1003mm vs. 1052mm) between STN and CRU, the data differ more in their estimates of the number of rain days (136 vs. 112) and wet days standard deviation (11.75mm vs. 18.49mm). The mean, maximum and minimum temperatures from CRU are all lower than those from STN. SWAT parameter sensitivity analysis results show slight differences in the relative rank of the most sensitive parameters, with the differences mainly caused by the lower temperature and more intensive rainfall in CRU relative to STN. Autocalibrated parameters showed very similar values, except for the surface runoff lag coefficient which is higher for the CRU dataset compared to that derived from the STN dataset. Statistic results for discharge simulated based on CRU compared rather well with that based on STN CRU as evaluated using the standard statistics of the Nash–Sutcliffe efficiency, coefficient of determination, and percent error. The sensitivity analysis and autocalibration tool embedded in SWAT2005 is a powerful utility in hydrological modeling of the River Xiangxi, and the CRU dataset appears to be appropriate for application to hydrological modeling in this case, thus providing a good basis for climate change studies. [Copyright &y& Elsevier]