近年来极端气候事件的频发对全球和区域性水循环产生了重大影响,特别是2005—2017年间两次强ENSO(El Nino-Southern Oscillation)事件使得全球陆地水储量出现了较大的年际波动.GRACE(Gravity Recovery and Climate Experiment)重力卫星...近年来极端气候事件的频发对全球和区域性水循环产生了重大影响,特别是2005—2017年间两次强ENSO(El Nino-Southern Oscillation)事件使得全球陆地水储量出现了较大的年际波动.GRACE(Gravity Recovery and Climate Experiment)重力卫星随着数据质量的提高、后处理方法的完善和超过十年的连续观测,捕捉陆地水储量异常的能力明显提高,这为研究2005—2017年间两次强ENSO事件对中国区域陆地水储量变化的影响提供了观测基础.本文综合利用GRACE卫星重力数据、GLDAS水文模型和实测降水资料分析了中国区域陆地水储量年际变化和与ENSO的关系.研究发现:长江流域中、下游地区和东南诸河流域与ENSO存在较高的相关性,与ENSO的相关系数最大值分别为0.55、0.78、0.70,较ENSO分别滞后约7个月、5个月和5个月.其中长江流域下游地区与ENSO的相关性最强,2010/11 La Nina和2015/16 El Nino两次强ENSO事件使得陆地水储量分别发生了约-24.1亿吨和27.9亿吨的波动.在2010/11 La Nina期间,长江流域下游地区和东南诸河流域陆地水储量异常约在2011年4—5月达到谷值,而长江流域中游地区晚1~2月达到谷值.在2015/16 El Nino期间,长江流域中、下游地区和东南诸河流域陆地水储量从2015年9月到2016年7月持续出现正异常信号.其中,2015年秋冬季(2015年9月至2016年1月)陆地水储量异常明显是受此次El Nino同期影响的结果;2016年春季(4—5月)陆地水异常是受到此次厄尔尼诺峰值的滞后影响所致;2016年7月的陆地水储量异常则与西北太平洋存在的异常反气旋环流有关.展开更多
The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model ph...The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model physics and parameters,as well as uncertainties in meteorological forcing data,commonly limit the ability of land surface models(LSMs)to accurately simulate TWS.In this study,the authors show how simulations of TWS anomalies(TWSAs)from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging(BMA)ensemble approach to improve monitoring and predictions.Simulations using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–2008.All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08.The correlation coefficient ranged between 0.5 and 0.8 in the humid regions(e.g.,the Yangtze river basin,Huaihe basin,and Zhujiang basin),but was much lower in the arid regions(e.g.,the Heihe basin and Tarim river basin).The BMA ensemble approach performed better than all individual member simulations.It captured the spatial distribution and temporal variations of TWSAs over China's Mainland and the eight major river basins very well;plus,it showed the highest R value(>0.5)over most basins and the lowest root-mean-square error value(<40 mm)in all basins of China.The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term,high-resolution spatial and temporal TWSA data.展开更多
文摘近年来极端气候事件的频发对全球和区域性水循环产生了重大影响,特别是2005—2017年间两次强ENSO(El Nino-Southern Oscillation)事件使得全球陆地水储量出现了较大的年际波动.GRACE(Gravity Recovery and Climate Experiment)重力卫星随着数据质量的提高、后处理方法的完善和超过十年的连续观测,捕捉陆地水储量异常的能力明显提高,这为研究2005—2017年间两次强ENSO事件对中国区域陆地水储量变化的影响提供了观测基础.本文综合利用GRACE卫星重力数据、GLDAS水文模型和实测降水资料分析了中国区域陆地水储量年际变化和与ENSO的关系.研究发现:长江流域中、下游地区和东南诸河流域与ENSO存在较高的相关性,与ENSO的相关系数最大值分别为0.55、0.78、0.70,较ENSO分别滞后约7个月、5个月和5个月.其中长江流域下游地区与ENSO的相关性最强,2010/11 La Nina和2015/16 El Nino两次强ENSO事件使得陆地水储量分别发生了约-24.1亿吨和27.9亿吨的波动.在2010/11 La Nina期间,长江流域下游地区和东南诸河流域陆地水储量异常约在2011年4—5月达到谷值,而长江流域中游地区晚1~2月达到谷值.在2015/16 El Nino期间,长江流域中、下游地区和东南诸河流域陆地水储量从2015年9月到2016年7月持续出现正异常信号.其中,2015年秋冬季(2015年9月至2016年1月)陆地水储量异常明显是受此次El Nino同期影响的结果;2016年春季(4—5月)陆地水异常是受到此次厄尔尼诺峰值的滞后影响所致;2016年7月的陆地水储量异常则与西北太平洋存在的异常反气旋环流有关.
基金supported by the National Natural Science Foundation of China(Grant Nos.41405083 and 91437220)the Natural Science Foundation of Hunan Province,China(Grant No.2015JJ3098)+1 种基金the Key Research Program of Frontier Sciences,CAS(QYZDY-SSW-DQC012)the Fund Project for The Education Department of Hunan Province(Grant No.16A234)
文摘The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model physics and parameters,as well as uncertainties in meteorological forcing data,commonly limit the ability of land surface models(LSMs)to accurately simulate TWS.In this study,the authors show how simulations of TWS anomalies(TWSAs)from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging(BMA)ensemble approach to improve monitoring and predictions.Simulations using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–2008.All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08.The correlation coefficient ranged between 0.5 and 0.8 in the humid regions(e.g.,the Yangtze river basin,Huaihe basin,and Zhujiang basin),but was much lower in the arid regions(e.g.,the Heihe basin and Tarim river basin).The BMA ensemble approach performed better than all individual member simulations.It captured the spatial distribution and temporal variations of TWSAs over China's Mainland and the eight major river basins very well;plus,it showed the highest R value(>0.5)over most basins and the lowest root-mean-square error value(<40 mm)in all basins of China.The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term,high-resolution spatial and temporal TWSA data.
