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Abstract: This study investigated uncertainties in the modeling of hydrological impacts of climate change on projected flood frequencies of the Zhujiang River, South China. The hydrological model HBV-D was applied to simulate and project future stream flow based on a multi-model ensemble. As this implies high uncertainties, the magnitude of three uncertainty sources, i.e. emission scenarios, GCM structure, and downscaling techniques, were determined in relation to the observed and projected natural variability. The relative change in each uncertainty source and the overall dominance among the three sources were further analyzed. The changes in flood frequency are projected for five return periods (2, 5, 10, 20, and 50 years) and three future time periods (2020s, 2050s, and 2080s). The results suggest that in comparison to the natural variability of the multi-model ensemble, the uncertainty sources show much stronger variations. The range of their relative change and their dominance vary with the lead-time and return period. In most of the return periods, the dominant uncertainty can primarily be attributed to downscaling techniques and emission scenarios, while GCMs structure is minor in the 2020s. However, downscaling technique is the second dominant source behind GCM structure, while emission scenarios represent the lowest uncertainty ranges of the three sources for the projected flood frequency in the 2050s and 2080s. The uncertainty and projected impact of climate change differs also between the four applied GCMs, as compared to the natural variability MK3_5 shows higher ranges than CCSM3, MK3_5 and ECHAM5. The upper bounds (95% percentile) in uncertainty mostly show an increasing tendency with increasing return period, and partially with increasing lead-time. Hence, the more extreme the return period (higher flood frequency) the higher is the uncertainty of the model projections. It is therefore essential that climate change impact assessments consider a wide range of climate scenarios derived from different GCMs under multiple emission scenarios and including several downscaling techniques. The uncertainty due to natural variability should also be considered more intensely. The projection of flood frequency and the identification and quantification of the uncertainties in the modeling is important for the implementation of adaptation policies into water resource planning in the Zhujiang River basin, South China. This study will enrich the scientific research on the uncertainty from different sources of modeling results in river basin parameters, and help to obtain conclusive results on the importance of different sources of uncertainty. [Copyright &y& Elsevier]

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: 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]