Air temperature and snow cover variability are sensitive indicators of climate change. This study was undertaken to forecast and quantify the potential streamflow response to climate change in the Jhelum River basin. ...Air temperature and snow cover variability are sensitive indicators of climate change. This study was undertaken to forecast and quantify the potential streamflow response to climate change in the Jhelum River basin. The implications of air temperature trends （＋0.11℃decade） reported for the entire north-west Himalaya for past century and the regional warming （＋0.7℃/decade） trends of three observatories analyzed between last two decades were used for future projection of snow cover depletion and stream flow. The streamflow was simulated and validated for the year 2007-2008 using snowmelt runoff model （SRM） based on in-situ temperature and precipitation with remotely sensed snow cover area. The simulation was repeated using higher values of temperature and modified snow cover depletion curves according to the assumed future climate. Early snow cover depletion was observed in the basin in response to warmer climate. The results show that with the increase in air temperature, streamfiow pattern of Jhelum will be severely affected. Significant redistribution of streamflow was observed in both the scenarios. Higher discharge was observed during spring-summer months due to early snowmelt contribution with water deficit during monsoon months. Discharge increased by 5%-40% during the months of March to May in 2030 and 2050. The magnitude of impact of air temperature is higher in the scenario-2 based on regional warming. The inferences pertaining to change in future streamflow pattern can facilitate long term decisions and planning concerning hydro-power potential, waterresource management and flood hazard mapping in the region.展开更多
In this study, discharge at the outlet of Xijiang River, the biggest sub-basin of the Zhujiang River, was simulated and projected from 1961 to 2099 using the hydrological model HBV-D. The model uses precipitation and ...In this study, discharge at the outlet of Xijiang River, the biggest sub-basin of the Zhujiang River, was simulated and projected from 1961 to 2099 using the hydrological model HBV-D. The model uses precipitation and temperature data from CISRO/MK3 5, MPI/ECHAM5, and NCAR/CCSM3 under three greenhouse gas emission scenarios (SRES A2, A1B, B1). The results in water resources and flood frequency suggest that annual precipitation and annual runoff would increase after 2050 relative to the reference period of 1961-1990. In addition, increasing trends have been projected in area averaged monthly precipitation and runoff from May to October, while decreasing trends in those from December to February. More often and larger floods would occur in future. Potential increase in runoff during the low-flow season could ease the pressure of water demand until 2030, but the increase in runoff in the high-flow season, with more often and larger floods, more pressure on flood control after 2050 is expected.展开更多
The 2015/2016 El Nio was one of the strongest El Nio events in history, and this strong event was preceded by a weak El Nio in 2014. This study systematically analyzed the dynamical processes responsible for the genes...The 2015/2016 El Nio was one of the strongest El Nio events in history, and this strong event was preceded by a weak El Nio in 2014. This study systematically analyzed the dynamical processes responsible for the genesis of these events. It was found that the weak 2014 El Nio had two warming phases, the spring-summer warming was produced by zonal advection and downwelling Kelvin waves driven by westerly wind bursts(WWBs), and the autumn-winter warming was produced by meridional advection, surface heating as well as downwelling Kelvin waves. The 2015/2016 extreme El Nio, on the other hand, was primarily a result of sustained zonal advection and downwelling Kelvin waves driven by a series of WWBs, with enhancement from the Bjerknes positive feedback. The vast difference between these two El Nio events mainly came from the different amount of WWBs in 2014 and 2015. As compared to the 1982/1983 and 1997/1998 extreme El Nio events, the 2015/2016 El Nio exhibited some distinctive characteristics in its genesis and spatial pattern. We need to include the effects of WWBs to the theoretical framework of El Nio to explain these characteristics, and to improve our understanding and prediction of El Nio.展开更多
文摘Air temperature and snow cover variability are sensitive indicators of climate change. This study was undertaken to forecast and quantify the potential streamflow response to climate change in the Jhelum River basin. The implications of air temperature trends （＋0.11℃decade） reported for the entire north-west Himalaya for past century and the regional warming （＋0.7℃/decade） trends of three observatories analyzed between last two decades were used for future projection of snow cover depletion and stream flow. The streamflow was simulated and validated for the year 2007-2008 using snowmelt runoff model （SRM） based on in-situ temperature and precipitation with remotely sensed snow cover area. The simulation was repeated using higher values of temperature and modified snow cover depletion curves according to the assumed future climate. Early snow cover depletion was observed in the basin in response to warmer climate. The results show that with the increase in air temperature, streamfiow pattern of Jhelum will be severely affected. Significant redistribution of streamflow was observed in both the scenarios. Higher discharge was observed during spring-summer months due to early snowmelt contribution with water deficit during monsoon months. Discharge increased by 5%-40% during the months of March to May in 2030 and 2050. The magnitude of impact of air temperature is higher in the scenario-2 based on regional warming. The inferences pertaining to change in future streamflow pattern can facilitate long term decisions and planning concerning hydro-power potential, waterresource management and flood hazard mapping in the region.
基金supported by the National Basic Research Program of China (No. 2010CB428401)
文摘In this study, discharge at the outlet of Xijiang River, the biggest sub-basin of the Zhujiang River, was simulated and projected from 1961 to 2099 using the hydrological model HBV-D. The model uses precipitation and temperature data from CISRO/MK3 5, MPI/ECHAM5, and NCAR/CCSM3 under three greenhouse gas emission scenarios (SRES A2, A1B, B1). The results in water resources and flood frequency suggest that annual precipitation and annual runoff would increase after 2050 relative to the reference period of 1961-1990. In addition, increasing trends have been projected in area averaged monthly precipitation and runoff from May to October, while decreasing trends in those from December to February. More often and larger floods would occur in future. Potential increase in runoff during the low-flow season could ease the pressure of water demand until 2030, but the increase in runoff in the high-flow season, with more often and larger floods, more pressure on flood control after 2050 is expected.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41690121, 41690124, 41690120, 41506025 & 41621064)the National Program on Global Change and Air-Sea Interaction (Grant Nos. GASI-IPOVAI-04 & GASI-IPOVAI-06)the Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ15D060004)
文摘The 2015/2016 El Nio was one of the strongest El Nio events in history, and this strong event was preceded by a weak El Nio in 2014. This study systematically analyzed the dynamical processes responsible for the genesis of these events. It was found that the weak 2014 El Nio had two warming phases, the spring-summer warming was produced by zonal advection and downwelling Kelvin waves driven by westerly wind bursts(WWBs), and the autumn-winter warming was produced by meridional advection, surface heating as well as downwelling Kelvin waves. The 2015/2016 extreme El Nio, on the other hand, was primarily a result of sustained zonal advection and downwelling Kelvin waves driven by a series of WWBs, with enhancement from the Bjerknes positive feedback. The vast difference between these two El Nio events mainly came from the different amount of WWBs in 2014 and 2015. As compared to the 1982/1983 and 1997/1998 extreme El Nio events, the 2015/2016 El Nio exhibited some distinctive characteristics in its genesis and spatial pattern. We need to include the effects of WWBs to the theoretical framework of El Nio to explain these characteristics, and to improve our understanding and prediction of El Nio.
