Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectb...Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.展开更多
Accurate surface air temperature(T2m)data are key to investigating eco-hydrological responses to global warming.Because of sparse in-situ observations,T2m datasets from atmospheric reanalysis or multi-source observati...Accurate surface air temperature(T2m)data are key to investigating eco-hydrological responses to global warming.Because of sparse in-situ observations,T2m datasets from atmospheric reanalysis or multi-source observation-based land data assimilation system(LDAS)are widely used in research over alpine regions such as the Tibetan Plateau(TP).It has been found that the warming rate of T2m over the TP accelerates during the global warming slowdown period of 1998–2013,which raises the question of whether the reanalysis or LDAS datasets can capture the warming feature.By evaluating two global LDASs,five global atmospheric reanalysis datasets,and a high-resolution dynamical downscaling simulation driven by one of the global reanalysis,we demonstrate that the LDASs and reanalysis datasets underestimate the warming trend over the TP by 27%–86%during 1998–2013.This is mainly caused by the underestimations of the increasing trends of surface downward radiation and nighttime total cloud amount over the southern and northern TP,respectively.Although GLDAS2.0,ERA5,and MERRA2 reduce biases of T2m simulation from their previous versions by 12%–94%,they do not show significant improvements in capturing the warming trend.The WRF dynamical downscaling dataset driven by ERA-Interim shows a great improvement,as it corrects the cooling trend in ERA-Interim to an observation-like warming trend over the southern TP.Our results indicate that more efforts are needed to reasonably simulate the warming features over the TP during the global warming slowdown period,and the WRF dynamical downscaling dataset provides more accurate T2m estimations than its driven global reanalysis dataset ERA-Interim for producing LDAS products over the TP.展开更多
文章利用重力恢复与气候实验卫星(Gravity Recovery and Climate Experiment,GRACE)时变重力场球谐系数文件,联合全球陆面数据同化系统(Global Land Data Assimilation System,GLDAS)水文模型反演安徽省2003—2016年地下水储量的时空变...文章利用重力恢复与气候实验卫星(Gravity Recovery and Climate Experiment,GRACE)时变重力场球谐系数文件,联合全球陆面数据同化系统(Global Land Data Assimilation System,GLDAS)水文模型反演安徽省2003—2016年地下水储量的时空变化。通过奇异谱分析(Singular Spectrum Analysis,SSA)地下水时间序列,结合热带降雨测量任务(Tropical Rainfall Measuring Mission,TRMM)降雨数据对地下水储量变化规律进行分析。结果表明,安徽省地下水储量在2011年和2014年前后发生较大变化,在2003—2011年的变化率为0.37 cm/a,2011—2014年的下降速率为-0.2 cm/a,2014—2016年的增长速率为1.9 cm/a;进一步与降雨数据关联,发现降雨量是影响安徽省地下水储量年际变化和季节性变化的主要因素。在空间上,安徽省呈现自东北向西南逐渐缓和的趋势,最大亏损出现在皖北地区,为-7.52 mm/a,在西南地区的最大盈余达到8.38 mm/a。展开更多
Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observ...Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observations available for model forcing, to estimate the hydro-meteorological fluxes in East Asia. In this study, Common Land Model （CLM） was used in offline-mode during the summer monsoon period of 2006 in East Asia, with different forcings from Asiaflux, Korea Land Data Assimilation System （KLDAS）, and Global Land Data Assimilation System （GLDAS）, at point and regional scales, separately. The CLM results were compared with observations from Asiaflux sites. The estimated net radiation showed good agreement, with r = 0.99 for the point scale and 0.85 for the regional scale. The estimated sensible and latent heat fluxes using Asiaflux and KLDAS data indicated reasonable agreement, with r = 0.70. The estimated soil moisture and soil temperature showed similar patterns to observations, although the estimated water fluxes using KLDAS showed larger discrepancies than those of Asiaflux because of scale mismatch. The spatial distribution of hydro-meteorological fluxes according to KLDAS for East Asia were compared to the CLM results with GLDAS, and the GLDAS provided online. The spatial distributions of CLM with KLDAS were analogous to CLM with GLDAS, and the standalone GLDAS data. The results indicate that KLDAS is a good potential source of high spatial resolution forcing data. Therefore, the KLDAS is a promising alternative product, capable of compensating for the lack of observations and low resolution grid data for East Asia.展开更多
A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weathe...A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.展开更多
地球重力场的变化是导致陆地水储量变化的重要因素之一,利用GRACE(Gravity Recovery and Climate Experiment)重力场恢复与气候实验重力卫星数据,结合GLDAS(Global Land Data Assimilation Systems)全球陆面数据同化系统和实测地下水位...地球重力场的变化是导致陆地水储量变化的重要因素之一,利用GRACE(Gravity Recovery and Climate Experiment)重力场恢复与气候实验重力卫星数据,结合GLDAS(Global Land Data Assimilation Systems)全球陆面数据同化系统和实测地下水位数据,反演和田地区克里雅河流域11年间四季和田地区的陆地水储量动态变化,模拟计算地下水等效水高变化趋势,构建了地下水水位估算模型。研究结果表明:和田地区春、夏两季的陆地水储量呈现出增加趋势,而秋、冬两季出现亏损状态;GRACE地球重力卫星所反演的陆地水储量比GLDAS同化系统所模拟的水资源变化更为剧烈,但2类数据的动态变化拟合度很高;GLDAS水资源等效水高二阶微分、GLDAS水资源变化倒数一阶微分、GRACE陆地水储量变化倒数变化、地下水储量变化一阶微分的敏感程度最高,构建的多元逐步回归模型明显优于线性函数,且水位深度越浅,该估算模型的适用性越高。展开更多
The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)for...The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)forcing data and the newly developed China Meteorological Administration Land Data Assimilation System(CLDAS)forcing data are used to drive the Noah LSM with multiple parameterizations(Noah-MP)and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over China's Mainland.The monthly soil moisture(SM)and evapotranspiration(ET)simulations are then compared and evaluated against observations.The results show that the Noah-MP driven by the CLDAS forcing data(referred to as CLDASNoah-MP)significantly improves the simulations in most cases over China's Mainland and its eight river basins.CLDASNoahMP increases the correlation coefficient(R)values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in China's Mainland,especially in the eastern monsoon area such as the Huang–Huai–Hai Plain,the southern Yangtze River basin,and the Zhujiang River basin.Moreover,the root-mean-square error is reduced from 0.078 to0.068 m3 m-3 for the SM simulations,and from 12.9 to 11.4 mm month-1 for the ET simulations over China's Mainland,especially in the southern Yangtze River basin and Zhujiang River basin.This study demonstrates that,by merging more in situ and remote sensing observations in regional atmospheric forcing data,offline LSM simulations can better simulate regional-scale land surface hydrological processes.展开更多
土壤湿度不仅是地表水循环的重要组成部分,而且对天气和气候也有重要影响,它的模拟误差严重阻碍了人们对水循环的认知.本文首先评估了1°×1°水平分辨率的全球陆面数据同化产品(Global Land Data Assimilation System,GLD...土壤湿度不仅是地表水循环的重要组成部分,而且对天气和气候也有重要影响,它的模拟误差严重阻碍了人们对水循环的认知.本文首先评估了1°×1°水平分辨率的全球陆面数据同化产品(Global Land Data Assimilation System,GLDAS)对青藏高原中部那曲地区和东部玛曲地区土壤湿度的模拟性能;鉴于GLDAS较粗的分辨率无法精细描述分析区域土壤湿度空间分布特征,于是我们基于通用陆面过程模式(Community Land Surface Model,Version 4.5),开展了高分辨率0.1°×0.1°的模拟,并对高分辨率模拟土壤湿度误差的原因进行了深入分析.结果表明:(1)GLDAS陆面数据同化产品和高分辨率陆面模式模拟结果都可以反映出土壤湿度的季节变化特征,但在非冻结期均存在不同程度的干偏差,尤其是在玛曲地区;(2)对比观测和模拟的土壤湿度发现,观测数据表现出强烈的空间异质性,而模拟结果呈现出的是空间均一性.按照模拟误差进行归类分组,对比模拟性能优劣的两组站点发现,模式物理过程不是模拟性能差异的主要因素,而两组站点间地表特征参数中的土壤质地和地形参数,以及驱动数据均没有体现出空间异质性,这可能是土壤湿度模拟结果没有表现出空间异质性的原因.展开更多
Continental water storage plays a major role in Earth's climate system.However,temporal and spatial variations of continental water are poorly known,particularly in Africa.Gravity Recovery and Climate Experiment(G...Continental water storage plays a major role in Earth's climate system.However,temporal and spatial variations of continental water are poorly known,particularly in Africa.Gravity Recovery and Climate Experiment(GRACE)satellite mission provides an opportunity to estimate terrestrial water storage(TWS)variations at both continental and river-basin scales.In this paper,seasonal and secular variations of TWS within Africa for the period from January 2003 to July 2013 are assessed using monthly GRACE coefficients from three processing centers(Centre for Space Research,the German Research Centre for Geo-sciences,and NASA's Jet Propulsion Laboratory).Monthly grids from Global Land Data Assimilation System(GLDAS)-I and from the Tropical Rainfall Measuring Mission(TRMM)-3B43 models are also used in order to understand the reasons of increasing or decreasing water storage.Results from GRACE processing centers show similar TWS estimates at seasonal timescales with some differences concerning inter-annual trend variations.The largest annual signals of GRACE TWS are observed in Zambezi and Okavango River basins and in Volta River Basin.An increasing trend of 11.60 mm/a is found in Zambezi River Basin and of 9 mm/a in Volta River Basin.A phase shift is found between rainfall and GRACE TWS GRACE TWS is preceded by rainfall by 2-3 months in parts of south central Africa.Comparing GLDAS rainfall with TRMM model,it is found that GLDAS has a dry bias from TRMM model.展开更多
基金Supported by the US Environmental Modeling Center(EMC)Land Surface Modeling Project(granted to Youlong Xia)National Natural Science Foundation of China(51609111,granted to Baoqing Zhang)
文摘Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.
基金Supported by the National Key Research and Development Program of China(2018YFA0606002)National Natural Science Foundation of China(41875105 and 91547103)Startup Fund for Introduced Talents of Nanjing University of Information Science&Technology.
文摘Accurate surface air temperature(T2m)data are key to investigating eco-hydrological responses to global warming.Because of sparse in-situ observations,T2m datasets from atmospheric reanalysis or multi-source observation-based land data assimilation system(LDAS)are widely used in research over alpine regions such as the Tibetan Plateau(TP).It has been found that the warming rate of T2m over the TP accelerates during the global warming slowdown period of 1998–2013,which raises the question of whether the reanalysis or LDAS datasets can capture the warming feature.By evaluating two global LDASs,five global atmospheric reanalysis datasets,and a high-resolution dynamical downscaling simulation driven by one of the global reanalysis,we demonstrate that the LDASs and reanalysis datasets underestimate the warming trend over the TP by 27%–86%during 1998–2013.This is mainly caused by the underestimations of the increasing trends of surface downward radiation and nighttime total cloud amount over the southern and northern TP,respectively.Although GLDAS2.0,ERA5,and MERRA2 reduce biases of T2m simulation from their previous versions by 12%–94%,they do not show significant improvements in capturing the warming trend.The WRF dynamical downscaling dataset driven by ERA-Interim shows a great improvement,as it corrects the cooling trend in ERA-Interim to an observation-like warming trend over the southern TP.Our results indicate that more efforts are needed to reasonably simulate the warming features over the TP during the global warming slowdown period,and the WRF dynamical downscaling dataset provides more accurate T2m estimations than its driven global reanalysis dataset ERA-Interim for producing LDAS products over the TP.
基金supported by Space Core Technology Development Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICTFuture Planning(NRF-2014M1A3A3A02034789)+1 种基金Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2013R1A1A2A10004743)the Korea Meteorological Administration Research and Development Program under Grant Weather Information Service Engine(WISE)project,KMA-2012-0001-A
文摘Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observations available for model forcing, to estimate the hydro-meteorological fluxes in East Asia. In this study, Common Land Model （CLM） was used in offline-mode during the summer monsoon period of 2006 in East Asia, with different forcings from Asiaflux, Korea Land Data Assimilation System （KLDAS）, and Global Land Data Assimilation System （GLDAS）, at point and regional scales, separately. The CLM results were compared with observations from Asiaflux sites. The estimated net radiation showed good agreement, with r = 0.99 for the point scale and 0.85 for the regional scale. The estimated sensible and latent heat fluxes using Asiaflux and KLDAS data indicated reasonable agreement, with r = 0.70. The estimated soil moisture and soil temperature showed similar patterns to observations, although the estimated water fluxes using KLDAS showed larger discrepancies than those of Asiaflux because of scale mismatch. The spatial distribution of hydro-meteorological fluxes according to KLDAS for East Asia were compared to the CLM results with GLDAS, and the GLDAS provided online. The spatial distributions of CLM with KLDAS were analogous to CLM with GLDAS, and the standalone GLDAS data. The results indicate that KLDAS is a good potential source of high spatial resolution forcing data. Therefore, the KLDAS is a promising alternative product, capable of compensating for the lack of observations and low resolution grid data for East Asia.
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002)National Key Research and Development Program of China(2018YFC1506601)+1 种基金National Natural Science Foundation of China(91437220)National Innovation Project for Meteorological Science and Technology(CMAGGTD003-5).
文摘A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.
文摘地球重力场的变化是导致陆地水储量变化的重要因素之一,利用GRACE(Gravity Recovery and Climate Experiment)重力场恢复与气候实验重力卫星数据,结合GLDAS(Global Land Data Assimilation Systems)全球陆面数据同化系统和实测地下水位数据,反演和田地区克里雅河流域11年间四季和田地区的陆地水储量动态变化,模拟计算地下水等效水高变化趋势,构建了地下水水位估算模型。研究结果表明:和田地区春、夏两季的陆地水储量呈现出增加趋势,而秋、冬两季出现亏损状态;GRACE地球重力卫星所反演的陆地水储量比GLDAS同化系统所模拟的水资源变化更为剧烈,但2类数据的动态变化拟合度很高;GLDAS水资源等效水高二阶微分、GLDAS水资源变化倒数一阶微分、GRACE陆地水储量变化倒数变化、地下水储量变化一阶微分的敏感程度最高,构建的多元逐步回归模型明显优于线性函数,且水位深度越浅,该估算模型的适用性越高。
基金Supported by the National Natural Science Foundation of China(91437220 and 41405083)Project Fund from the Education Department of Hunan Province(14C0897)Huaihua University Double First-Class Initiative in Applied Characteristic Discipline of Control Science and Engineering.
文摘The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)forcing data and the newly developed China Meteorological Administration Land Data Assimilation System(CLDAS)forcing data are used to drive the Noah LSM with multiple parameterizations(Noah-MP)and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over China's Mainland.The monthly soil moisture(SM)and evapotranspiration(ET)simulations are then compared and evaluated against observations.The results show that the Noah-MP driven by the CLDAS forcing data(referred to as CLDASNoah-MP)significantly improves the simulations in most cases over China's Mainland and its eight river basins.CLDASNoahMP increases the correlation coefficient(R)values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in China's Mainland,especially in the eastern monsoon area such as the Huang–Huai–Hai Plain,the southern Yangtze River basin,and the Zhujiang River basin.Moreover,the root-mean-square error is reduced from 0.078 to0.068 m3 m-3 for the SM simulations,and from 12.9 to 11.4 mm month-1 for the ET simulations over China's Mainland,especially in the southern Yangtze River basin and Zhujiang River basin.This study demonstrates that,by merging more in situ and remote sensing observations in regional atmospheric forcing data,offline LSM simulations can better simulate regional-scale land surface hydrological processes.
文摘土壤湿度不仅是地表水循环的重要组成部分,而且对天气和气候也有重要影响,它的模拟误差严重阻碍了人们对水循环的认知.本文首先评估了1°×1°水平分辨率的全球陆面数据同化产品(Global Land Data Assimilation System,GLDAS)对青藏高原中部那曲地区和东部玛曲地区土壤湿度的模拟性能;鉴于GLDAS较粗的分辨率无法精细描述分析区域土壤湿度空间分布特征,于是我们基于通用陆面过程模式(Community Land Surface Model,Version 4.5),开展了高分辨率0.1°×0.1°的模拟,并对高分辨率模拟土壤湿度误差的原因进行了深入分析.结果表明:(1)GLDAS陆面数据同化产品和高分辨率陆面模式模拟结果都可以反映出土壤湿度的季节变化特征,但在非冻结期均存在不同程度的干偏差,尤其是在玛曲地区;(2)对比观测和模拟的土壤湿度发现,观测数据表现出强烈的空间异质性,而模拟结果呈现出的是空间均一性.按照模拟误差进行归类分组,对比模拟性能优劣的两组站点发现,模式物理过程不是模拟性能差异的主要因素,而两组站点间地表特征参数中的土壤质地和地形参数,以及驱动数据均没有体现出空间异质性,这可能是土壤湿度模拟结果没有表现出空间异质性的原因.
基金supported by the Main Direction Project of Chinese Academy of Sciences(KJCX2-EW-T03)Shanghai Science and Technology Commission Project(12DZ2273300)National Natural Science Foundation of China(NSFC)Project(11173050 and 11373059)
文摘Continental water storage plays a major role in Earth's climate system.However,temporal and spatial variations of continental water are poorly known,particularly in Africa.Gravity Recovery and Climate Experiment(GRACE)satellite mission provides an opportunity to estimate terrestrial water storage(TWS)variations at both continental and river-basin scales.In this paper,seasonal and secular variations of TWS within Africa for the period from January 2003 to July 2013 are assessed using monthly GRACE coefficients from three processing centers(Centre for Space Research,the German Research Centre for Geo-sciences,and NASA's Jet Propulsion Laboratory).Monthly grids from Global Land Data Assimilation System(GLDAS)-I and from the Tropical Rainfall Measuring Mission(TRMM)-3B43 models are also used in order to understand the reasons of increasing or decreasing water storage.Results from GRACE processing centers show similar TWS estimates at seasonal timescales with some differences concerning inter-annual trend variations.The largest annual signals of GRACE TWS are observed in Zambezi and Okavango River basins and in Volta River Basin.An increasing trend of 11.60 mm/a is found in Zambezi River Basin and of 9 mm/a in Volta River Basin.A phase shift is found between rainfall and GRACE TWS GRACE TWS is preceded by rainfall by 2-3 months in parts of south central Africa.Comparing GLDAS rainfall with TRMM model,it is found that GLDAS has a dry bias from TRMM model.
