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
Long-term droughts significantly impact surface and groundwater resources in India,however,observed changes in major river basins have not been well explored.Here we use Standardized Precipitation Index(SPI)and Standa...Long-term droughts significantly impact surface and groundwater resources in India,however,observed changes in major river basins have not been well explored.Here we use Standardized Precipitation Index(SPI)and Standardized Precipitation Evapotranspiration Index(SPEI)at three different time scales(24,48,and 60 months)to identify long-term droughts in India for the observed record of 1951-2015.Drought characteristics(extent,events,frequency,and intensity)are analyzed for different river basins in India.Increasing trend in the areal extent of droughts is observed in two methods with three time scales in the maximum area(63.66%)in India.We use the data from the Gravity Recovery and Climate Experiment(GRACE)to estimate the changes in the terrestrial water storage(TWS)during the period 2002-2015.We identify that major long-term droughts in India occurred from 1966 to 1969,1972,1986-1987,and 2002-2004.The all-India average TWS shows a negative trend from 2002 to 2015 with prominent decline in north Indian river basins and positive trend in south Indian river basins.SPI and SPEI at longer time scales are positively associated with TWS indicating the adverse impacts of droughts on surface and groundwater resources in such a populated region.展开更多
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.
文摘Long-term droughts significantly impact surface and groundwater resources in India,however,observed changes in major river basins have not been well explored.Here we use Standardized Precipitation Index(SPI)and Standardized Precipitation Evapotranspiration Index(SPEI)at three different time scales(24,48,and 60 months)to identify long-term droughts in India for the observed record of 1951-2015.Drought characteristics(extent,events,frequency,and intensity)are analyzed for different river basins in India.Increasing trend in the areal extent of droughts is observed in two methods with three time scales in the maximum area(63.66%)in India.We use the data from the Gravity Recovery and Climate Experiment(GRACE)to estimate the changes in the terrestrial water storage(TWS)during the period 2002-2015.We identify that major long-term droughts in India occurred from 1966 to 1969,1972,1986-1987,and 2002-2004.The all-India average TWS shows a negative trend from 2002 to 2015 with prominent decline in north Indian river basins and positive trend in south Indian river basins.SPI and SPEI at longer time scales are positively associated with TWS indicating the adverse impacts of droughts on surface and groundwater resources in such a populated region.
