Abstract
Abstract. To understand the causes of the past water cycle variations and the influence of climate variability on the streamflow, lake storage, and flood potential, we analyze the changes in streamflow and the underlying drivers in four typical watersheds (Gaosha, Meigang, Saitang, and Xiashan) within the Poyang Lake Basin, based on the meteorological observations at 79 weather stations, and datasets of streamflow and river level at four hydrological stations for the period of 1961-2000. The contribution of different climate factors to the change in streamflow in each watershed is estimated quantitatively using the water balance equations. Results show that in each watershed, the annual streamflow exhibits an increasing trend from 1961–2000. The increases in streamflow by 4.80 m3 s−1 yr−1 and 1.29 m3 s−1 yr−1 at Meigang and Gaosha, respectively, are statistically significant at the 5% level. The increase in precipitation is the biggest contributor to the streamflow increment in Meigang (3.79 m3 s−1 yr−1), Gaosha (1.12 m3 s−1 yr−1), and Xiashan (1.34 m3 s−1 yr−1), while the decrease in evapotranspiration is the major factor controlling the streamflow increment in Saitang (0.19 m3 s−1 yr−1). In addition, radiation and wind contribute more than actual vapor pressure and mean temperature to the changes in evapotranspiration and streamflow for the four watersheds. For revealing the possible change of streamflow due to the future climate change, we also investigate the projected precipitation and evapotranspiration from of the Coupled Model Intercomparison Project phase 3 (CMIP3) under three greenhouse gases emission scenarios (SRESA1B, SRESA2 and SRESB1) for the period of 2061–2100. When the future changes in the soil water storage changes are assumed ignorable, the streamflow shows an uptrend with the projected increases in both precipitation and evapotranspiration (except for the SRESB1 scenario in Xiashan watershed) relative to the observed mean during 1961–2000. Furthermore, the largest increase in the streamflow is found at Meigang (+4.31%) and Xiashan (+3.84%) under the SRESA1B scenario, while the increases will occur at Saitang (+6.87%) and Gaosha (+5.15%) under the SRESB1 scenario.
Highlights
Water resources are influenced by many aspects of environment, economy and society (Kundzewicz et al, 2007; Zhang et al, 2007; Nash and Gleick, 1991; Liu and Fu, 1993; Milly et al, 2005; Gedney et al, 2006; Oki et al, 1995)
The calculated annual mean streamflow is in good agreement with the observation in each watershed, with r above 0.94 at the 5 % significance level, relative mean error (RME) in the range from −3.8 to 0.98 %, and root mean square error (RMSE) ranging from 7.24 to 39.21 m3 s−1
Based on the historical streamflow data of the four gauge stations in Poyang Lake Basin, it is shown that the annual streamflow in the four watersheds exhibits different increasing trends during 1961–2000
Summary
Water resources are influenced by many aspects of environment (especially climate change, such as precipitation, evapotranspiration and temperature), economy and society (Kundzewicz et al, 2007; Zhang et al, 2007; Nash and Gleick, 1991; Liu and Fu, 1993; Milly et al, 2005; Gedney et al, 2006; Oki et al, 1995). Chen et al (2007) found that the mean annual, spring and winter runoff decreased at the 5 % significance level in the Hanjiang Basin, caused by the integrated effects of changes in both precipitation and temperature They projected the increasing trends of runoff during the period of 2021–2050 under three climate scenarios of greenhouse gases emissions using a two-parameter water balance model. The present study aims to: (1) quantify the contributions of various climate variables to the past (1961–2000) streamflow trends in Poyang Lake Basin on the basis of water balance equations, and (2) project the percentage changes of the streamflow in the future (2061–2100) relative to the past, using the precipitation and evapotranspiration data projected by different global coupled atmosphere-ocean general circulation models (AOGCMs) under three greenhouse gases emission scenarios
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