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 ***,inadequacies in model physics and ...
详细信息
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 ***,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 *** 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 *** using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–*** the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–*** 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 *** 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 *** 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.
暂无评论