应用GPS数据和Slepian基函数反演川云渝地区陆地水储量变化

局部Slepian函数是将局部区域内的地球物理信号转化为空间谱的一种方法,其可以保证在球面上局部范围内获得最优谱平滑解,非常适用于局部范围地球物理信号的研究.本文利用中国陆态网西南地区72个测站的连续GPS观测资料分析川云渝地区陆地水负荷形变特征,并基于Slepian函数方法解算60阶的空间谱基函数,结合弹性质量负荷理论研究了川云渝地区2011年至2015年陆地水储量变化的时空分布模式.针对Slepian函数的边界效应问题,本文使用GLDAS格网数据计算得到站点处垂直负荷位移时间序列,然后利用该位移数据来进行水储量变化恢复实验,结果表明当边界扩充为3°时能较好地恢复GLDAS模型输出的陆地水储量变化.通过对比区域内GPS、GRACE、GLDAS得到的等效水高以及降雨数据,发现季节性降水是陆地水变化的一个重要驱动因子,GPS反演结果与GRACE和GLDAS数据具有较强的空间一致性.云南地区周年变化要强于川渝地区,其中云南西部的山区陆地水变化最大,约为30 cm,最小为川北以及重庆地区仅为7 cm.相较于GPS反演结果,GRACE与GLDAS明显低估了陆地水储量的季节性变化,分别达到24%和47%.比较分析地区内平均等效水高时间序列的相位发现,GPS得到的陆地水变化与降雨数据一致性较好,而GRACE与GLDAS存在一到两个月左右的时延.同时GPS能较好的探测出2015年1月左右南方地区大范围的强降水,而GRACE与GLDAS并没有体现出该现象,说明GPS能更为灵敏地探测到局部地区陆地水的变化.在站点等效水高时间序列上,GPS与GRACE的相关性总体上要优于GPS与GLDAS,陆地水周年变化较大的云南和四川西部地区站点三种数据间相关性较好,而其他季节性信号不明显的地区则相关性较差.本文的研究表明运用GPS-Slepian方法能够独立地监测高时空分辨率的陆地水储量变化,是作为当前补充GRACE观测资料空缺期的有益尝试.     

利用GPS与GRACE监测陆地水负荷导致的季节性水平形变：以喜马拉雅山地区为例

地表陆地水负荷变化是引起重力场和地壳形变呈现季节性特征的主要因素,并且能够利用地表及空间大地测量技术对其进行有效的监测.本文通过对质量负荷形变效应的理论模拟,描述了水平分量的形变指向以及垂直与水平分量的幅值比可以提高对负荷区域的辨别程度,并且联合GPS坐标时间序列及GRACE模型对喜马拉雅山地区的季节性负荷形变进行了详细对比分析,研究结果显示两者垂直分量的季节性变化具有较好的一致性,且GPS周年项幅值要大于GRACE.而由GRACE解算得到的水平分量结果表明该地区季节性形变主要受东南亚及印度东北部地区的陆地水负荷控制,位于喜马拉雅山地区多数GPS台站的垂直分量及北向分量的初相位与GRACE模型解算结果相近,而部分GPS台站的东向分量与GRACE模型存在明显不同,由此导致GPS与GRACE监测到的形变指向存在差异.通过对GRACE估算精度以及GPS垂直与水平分量幅值比的深入分析,发现GPS对局部周边地区的河流、谷地及农田灌溉等负荷变化造成的形变效应较为敏感,而GRACE由于截断阶次及平滑滤波等影响因素,不仅造成在水平分量上的分辨率远低于垂直分量,而且整体估算精度要低于GPS观测得到的形变信息.     

Crustal loading in vertical GPS time series in Fennoscandia

We compare time series of vertical position from GPS with modelled vertical deformation caused by variation in continental water storage, variation in the level of the Baltic Sea, and variation in atmospheric pressure. Monthly time series are used. The effect of continental water storage was calculated from three different global models. The effect of non-tidal variation in Baltic Sea level was calculated using tide gauge observations along the coasts. Atmospheric loading was computed from a numerical weather model. The loading time series are then compared with three different GPS time series at seven stations in Fennoscandia. A more detailed analysis is computed at three coastal stations. When the monthly GPS time series are corrected using the load models, their root-mean-square scatter shows an improvement between 40 and 0%, depending on the site and on the GPS solution. The modelled load effect shows a markedly seasonal pattern of 15 mm peak-to-peak, of which the uncorrected GPS time series reproduce between 60 and 0%.     

利用GRACE卫星重力资料解算气候驱动的地表周年垂直形变

卫星重力和GPS测量技术可以监测地表流体(大气、海洋和陆地水)质量季节性迁移引起的地表周年形变;与陆地水等地表流体模型综合模拟的地表形变相比,卫星重力的形变监测结果避免了模型的精度不确定性带来的误差.本文利用前60阶GRACE卫星时变重力资料和“去相关”、组合滤波两类滤波方法分别解算了中国及邻区的地表季节性垂直形变,并与区内42个GPS台站上观测到的季节性信号进行了比较,发现采用“去相关”滤波方法处理后的结果优于采用组合滤波处理后的结果.文中采用“去相关”滤波方法,GRACE解算的周年垂直形变的振幅、相位和GPS结果总体上一致;少数站上GRACE和GPS得到的振幅或相位相差较大,主要因素可能与GPS解算策略、GPS观测资料的连续性或局部大气、水文过程等地球物理因素有关.在中国及邻区的陆地上GRACE解算的周年垂直形变的振幅最小值出现在TASH台站东南,约1×10^-3 m;最大值出现在恒河-澜沧江流域,可达10×10^-3 m.文中的结果证实了在中国及邻区可以用GRACE卫星重力这种新手段监测大尺度的地表周年垂直形变.     

滇西地区GPS时间序列中陆地水载荷形变干扰的GRACE分辨与剔除

利用“中国大陆构造环境监测网络”在云南西部地区的13个连续GPS观测站和法国空间大地测量研究组Space Geodesy Research Group）的GRACE时变重力场资料,定量分析了该区域陆地水载荷所产生的非构造形变的量值和变化特点,探讨了利用GRACE分辨和剔除GPS观测中陆地水负荷所引起的非构造形变干扰的依据和模型.结果表明：滇西地区GPS坐标变化时间序列的垂向分量中,普遍包含有明显的年周期非构造形变波动,高值可达12mm,其中约42%源于陆地水迁徙变化所引起的负荷形变;通过主成份分析方法所获取的区域GPS共模误差与GRACE陆地水载荷形变序列的相关性高达0.87,若以GRACE扣除陆地水负荷形变,则滇西地区GPS网共模误差可消除约64%,且物理机制明确.然而,由于目前的GRACE只能有效分辨大约400km范围内陆地水载荷的整体变化,所以对于各GPS站点更加局部化的陆地水负荷非构造形变干扰,尚无法进行有效分辨.     

武汉九峰地震台超导重力仪观测分析研究

连续重力观测和GPS的技术结合能够监测到物质迁移和地壳垂直形变之间的量化关系.和相对重力测量以及绝对重力测量技术相比,其避免了时间分辨率和观测精度低,无法精细描述观测周期内的物质迁移过程问题.本文利用武汉九峰地震台超导重力仪SGC053超过13000 h连续重力观测数据;同址观测的绝对重力仪观测结果;气压数据;周边GPS观测结果;GRACE卫星的时变重力场;全球水储量模型等资料,采用同址观测技术、调和分析法、相关分析方法在扣除九峰地震台潮汐、气压、极移和仪器漂移的基础上,利用重力残差时间序列和GPS垂直位移研究物质迁移和地壳垂直形变之间的量化关系.结果表明:在改正连续重力观测数据的潮汐、气压、极移的影响后,不仅准确观测到2009年的夏秋两季由于水负荷引起的约(6~8)×10^-8m·s^-2短期的重力变化.而且在扣除2.18×10^-8(m·s^-2)/a仪器漂移和水负荷的影响后,验证了本地区长短趋势垂直形变和重力变化之间具有一致的负相关性规律.同时长趋势表明该地区地壳处于下沉,重力处于增大过程,增加速率约为1.79×10^-8(m·s^-2)/a.武汉地区重力梯度关系约为-354×10^-8(m·s^-2)/m.     

基于GPS和GRACE数据的三维地表形变的比较及地球物理解释

GPS技术能以高空间和高时间分辨率监测地表形变.但由于测量原理的不同,GPS监测的地表形变与GRACE存在差异.本文比较了ITRF2008-GPS残差序列与基于CSR的RL05版本的GRACE球谐系数的地表形变序列的差异.结果表明,GPS和GRACE的周年变化在高程方向上具有较好的一致性,但水平方向的差异明显.重点分析了影响GPS/GRACE地表形变差异(尤其是水平方向)的三个因素:不同GPS站时间序列间的不确定性,热弹性形变和区域形变.GPS站地表形变本身的不确定度在一定程度上导致了GPS/GRACE间的差异(特别是水平方向).结合热弹性形变理论指出,由温度变化引起的热弹性形变也是导致GPS/GRACE的南北方向差异的主要原因之一.因此利用GPS数据研究地表质量负载时,必须消除热弹性形变的影响.区域负载对GPS/GRACE水平方向差异的影响也是不可忽略的,特别是对欧洲区域.     

非构造形变对GPS连续站位置时间序列的影响和修正

GPS观测得到的地壳形变场通常包含有构造形变与非构造形变二类信息, 去除其中的非构造形变信息对于有效运用GPS数据研究构造形变场至关重要. 本文运用国际卫星对地观测资料及各类地球物理模型, 定量计算海潮、大气、积雪和土壤水、海洋非潮汐4项负荷效应造成的地壳非构造形变, 并以此研究和修正这些非构造形变对中国地壳运动观测网络GPS基准站位置时间序列的影响. 研究发现此4项负荷效应, 特别是大气、积雪和土壤水, 对于测站垂向位置的影响显著. 通过模型改正可以使测站垂向位置的RMS降低～1 mm, 占其总量的～11％. 对于垂向时间序列的周年项部分, 这一改正可降低其振幅的37％. 研究还表明经过地球物理模型改正和周年、半周年谐波拟合改正的时间序列比起仅经过周年、半周年谐波拟合改正的时间序列更为平滑, 表明地球物理模型改正对于消除非构造形变场的作用不是周年、半周年谐波拟合改正所能替代的.     

Assessment of terrestrial water contributions to polar motion from GRACE and hydrological models

The hydrological contribution to polar motion is a major challenge in explaining the observed geodetic residual of non-atmospheric and non-oceanic excitations since hydrological models have limited input of comprehensive global direct observations. Although global terrestrial water storage (TWS) estimated from the Gravity Recovery and Climate Experiment (GRACE) provides a new opportunity to study the hydrological excitation of polar motion, the GRACE gridded data are subject to the post-processing de-striping algorithm, spatial gridded mapping and filter smoothing effects as well as aliasing errors. In this paper, the hydrological contributions to polar motion are investigated and evaluated at seasonal and intra-seasonal time scales using the recovered degree-2 harmonic coefficients from all GRACE spherical harmonic coefficients and hydrological models data with the same filter smoothing and recovering methods, including the Global Land Data Assimilation Systems (GLDAS) model, Climate Prediction Center (CPC) model, the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis products and European Center for Medium-Range Weather Forecasts (ECMWF) operational model (opECMWF). It is shown that GRACE is better in explaining the geodetic residual of non-atmospheric and non-oceanic polar motion excitations at the annual period, while the models give worse estimates with a larger phase shift or amplitude bias. At the semi-annual period, the GRACE estimates are also generally closer to the geodetic residual, but with some biases in phase or amplitude due mainly to some aliasing errors at near semi-annual period from geophysical models. For periods less than 1-year, the hydrological models and GRACE are generally worse in explaining the intraseasonal polar motion excitations.     

Tackling mass redistribution phenomena by time-dependent GRACE- and terrestrial gravity observations

Time variable gravity field models derived from the satellite mission GRACE have been demonstrated to be consistent with water mass variations in the global hydrological cycle. Independent observations are provided by terrestrial measurements. In order to achieve a maximum of reliability and information gain, ground-based gravity observations may be deployed for comparison with the gravity field variations derived from the GRACE satellite mission. In this context, the data of the network of superconducting gravimeters (SG) of the ‘Global Geodynamics Project’ (GGP) are of particular interest. This study is focused on the dense SG network in Central Europe with its long-term gravity observations. It is shown that after the separation and reduction of local hydrological effects in the SG observations especially for subsurface stations, the time-variable gravity signals from GRACE agree well with the terrestrial observations from the SG station cluster.Station stability of the SG sites with respect to vertical deformations was checked by GNSS based observations. Most of the variability can be explained by loading effects due to changes in continental water storage, and, in general, the stability of all stations has been confirmed.From comparisons based on correlation and coherence analyses in combination with the root mean square (RMS) variability of the time series emerges, that the maximum correspondence between the SG and GRACE time series is achieved when filtering the GRACE data with Gaussian filters of about 1000 km filter length, which is in accordance with previous publications.Empirical Orthogonal Functions (EOF) analysis was applied to the gravity time series in order to identify common characteristic spatial and temporal patterns. The high correspondence of the first modes for GRACE and SG data implies that the first EOF mode represents a large-scale (Central European) time-variable gravity signal seen by both the GRACE satellites and the SG cluster.     

中国区域IGS基准站坐标时间序列非线性变化的成因分析

GPS坐标时间序列呈现显著的季节性变化,通常认为大气压、非潮汐海洋负载及水文负载(统称为地表质量负载)是引起测站谐波变化的主要因素.本文计算了不同地表质量负载造成的测站位移,以此修正中国区域11个IGS基准站的坐标时间序列.建立了地球物理现象与测站季节性变化及噪声特性之间的初步数值联系,认为其会造成测站的噪声特性变化,主要表现为带通及随机漫步噪声特征,且仅能减小测站U分量的周年运动,但并不是造成测站U分量半周年运动及水平方向周年运动的主要原因.深入分析了造成中国区域IGS基准站非线性变化的其他可能因素,重点探讨了周日(S1)、半周日(S2)大气潮汐对基准站周年振幅的贡献,由此提出S1、S2大气潮汐是造成中国区域IGS基准站周年运动,尤其是中南部测站垂向周年运动的主要因素之一.     

基于Forward-Modeling方法的黑河流域水储量变化特征研究

黑河流域陆地水储量变化对流域下游等周边区域水资源的合理利用以及经济和社会发展等有着重要的意义.本文利用2003年1月至2013年12月的GRACE RL05数据反演了黑河流域陆地水储量长时间序列的变化,并针对重力场模型和数据处理中产生的信号泄漏问题,采用Forward-Modeling方法进行了改正并恢复泄漏信号;将GRACE获得的泄漏信号恢复前后的黑河流域水储量变化结果与全球水文模型GLDAS和CPC进行比较分析,结果表明泄漏信号改正后的结果与水文模型结果的时间序列相关性均有明显提高,从其空间分布结果可以看出Forward-Modeling方法有效地恢复初始信号、增强被湮没的信号,泄漏信号误差减小;通过分析黑河流域水储量变化的长时间序列结果,发现其具有明显的阶段性变化特征,即2003—2006年呈明显下降趋势,约为-0.86cm·a-1,在2007—2010年趋于平衡状态,而2011—2013年则呈现缓慢上升趋势约为0.14cm·a-1;联合GRACE数据和GLDAS数据反演了黑河流域地下水储量变化,并与全球降雨数据GPCC进行了比较分析,两者相关性可达到0.88以上.     

Groundwater Monitoring Using GRACE and GLDAS Data after Downscaling Within Basaltic Aquifer System

Gravity Recovery and Climate Experiment (GRACE) satellite mission is ground-breaking information hotspot for the evaluation of groundwater storage. The present study aims at validating the sensitivity of GRACE data to groundwater storage variation within a basaltic aquifer system after its statistical downscaling on a regional scale. The basaltic aquifer system which covers 82.06% area of Maharashtra state in India, is selected as the study area. Five types of basaltic aquifer systems with varying groundwater storage capacities, based on hydrologic characteristics, have been identified within the study area. The spatial and seasonal trend analysis of observed in situ groundwater storage anomalies (ΔGWSano) computed from groundwater level data of 983 wells from the year 2002 to 2016, has been performed to analyze the variation in groundwater storages in the different basaltic aquifer system. The groundwater storage anomalies (ΔGWSDano) have been derived from GRACE Release 05 (RL05) after removing the soil moisture anomaly (ΔSMano) and canopy water storage anomaly (ΔCNOano) obtained from Global Land Data Assimilation System (GLDAS) land surface models (NOAH, MOSAIC, CLM and VIC). The artificial neural network technique has been used to downscale the GRACE and GLDAS data at a finer spatial resolution of 0.125°. The study shows that downscaled GRACE and GLDAS data at a finer spatial resolution is sensitive to seasonal groundwater storage variability in different basaltic aquifer systems and the regression coefficient R has been found satisfactory in the range of 0.696 to 0.818.     

Contributions of terrestrial and GRACE data to the study of the secular geoid changes in North America

This paper tests and discusses different statistical methods for modelling secular rates of change of the geoid in North America. In particular, we use the method of principal component/empirical orthogonal functions (PC/EOF) analysis to model the geoid rates from Gravity Recovery and Climate Experiment (GRACE) satellite data. As demonstrated, the PC/EOF analysis is useful for studying the contributions from different signals (mainly residual hydrology signals and leakage effects) to the GRACE-derived geoid rates. The PC/EOF analysis leads to smaller geoid rates compared to the conventional least-squares fitting of a trend and annual and semi-annual cycles to the time series of the spherical harmonic coefficients. This is because we filter out particular spatiotemporal modes of the regional geoid changes.We apply the method of least-squares collocation with parameters to combine terrestrial data (GPS vertical velocities from the Canadian Base Network and terrestrial gravity rates from the Canadian Gravity Standardization Net) with the GRACE-derived vertical motion to obtain again the geoid rates. The combined model has a peak geoid rate of 1.4 mm/year in the southeastern area of Hudson Bay contrary to the GRACE-derived geoid rates that show a large peak of 1.6–1.7 mm/year west of Hudson Bay. We demonstrate that the terrestrial data, which have a longer time span than the GRACE data, are important for constraining the GRACE-derived secular signal in the areas that are well sampled by the data.     

Separation of mass signals within GRACE monthly gravity field models by means of empirical orthogonal functions

Since 2002 the two GRACE satellites observe the time varying gravity signal mainly caused by the sum of mass variations within the Earth subsystems ocean, atmosphere, and continental hydrosphere. It is a challenging problem to separate the integral GRACE signal and to identify and quantify the mass variations of the individual subsystems. This work proves first by a closed loop simulation that such a decomposition is successful by means of empirical orthogonal functions (EOF) derived from geophysical models and a least-squares adjustment with a multivariate Gauss–Markov model with time coefficients parameterized. The geophysical models are used to synthesize GRACE observations which are subsequently separated leading to time coefficients coinciding with those of the predefined models. In a second step the separation is performed with real, unfiltered time series of 5 years of monthly GRACE gravity field models (with atmospheric and oceanic background models reconstructed) and a limited number of EOFs. The reconstructed time coefficients are in good agreement with the original ones and exhibit high correlations (0.70 for ocean, 0.91 for atmosphere and 0.93 for continental hydrosphere). Analysis of GRACE residuals and the correlation among the time coefficients substantiate a successful identification.     

Comparison of soil moisture products from microwave remote sensing,land model,and reanalysis using global ground observations

High-quality soil moisture (SM) datasets are in great demand for climate, hydrology, and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) (C- and X-bands) and European Space Agency's Climate Change Initiative (ESA CCI)], land surface model [Global Land Data Assimilation System (GLDAS)], and reanalysis data [ECMWF Re-Analysis-Interim (ERA-Interim) and National Centers for Environmental Prediction (NCEP)] under different time scales and various climates and land covers. We find that: (a) ESA CCI and GLDAS have the closest values to the in situ SM on the annual scale, whereas others overestimate the SM; ERA-Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in situ data, while GLDAS performs worst. (b) Overall, the deviations of each product vary in seasons. ESA CCI and ERA-Interim products are closer to the in situ SM at seasonal scales, and AMSR-E and NCEP perform worst in December–February and June–August, respectively. (c) Except for NCEP and ERA-Interim, others can well reflect the intermonthly variation of the in situ SM. (d) Under various climates and land covers, AMSR-E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the Bias and R of each SM product differ obviously under different forest types, especially the AMSR-E products. In summary, SM from ESA CCI is the best, followed by ERA-Interim product, and precipitation is an important auxiliary data for selecting high-quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products.     

Satellite based estimates of terrestrial water storage variations in Turkey

In recent years, the Gravity Recovery and Climate Experiment (GRACE) has provided a new tool to study terrestrial water storage variations (TWS) at medium and large spatial scales, providing quantitative measures of TWS change. Linear trends in TWS variations in Turkey were estimated using GRACE observations for the period March 2003 to March 2009. GRACE showed a significant decrease in TWS in the southern part of the central Anatolian region up to a rate of 4 cm/year. The Global Land Data Assimilation System (GLDAS) model also captured this TWS decrease event but with underestimated trend values. The GLDAS model represents only a part of the total TWS variations, the sum of soil moisture (2 m column depth) and snow water equivalent, ignoring groundwater variations. Therefore, GLDAS model derived TWS variations were subtracted from GRACE derived TWS variations to estimate groundwater storage variations. Results revealed that decreasing trends of TWS observed by GRACE in the southern part of central Anatolia were largely explained by the decreasing trends of groundwater variations which were confirmed by the limited available well groundwater level data in the region.     

Wavelet and Gaussian Approaches for Estimation of Groundwater Variations Using GRACE Data

In this study, a scheme is presented to estimate groundwater storage variations in Iran. The variations are estimated using 11 years of Gravity Recovery and Climate Experiments (GRACE) observations from period of 2003 to April 2014 in combination with the outputs of Global Land Data Assimilation Systems (GLDAS) model including soil moisture, snow water equivalent, and total canopy water storage. To do so, the sums of GLDAS outputs are subtracted from terrestrial water storage variations determined by GRACE observations. Because of stripping errors in the GRACE data, two methodologies based on wavelet analysis and Gaussian filtering are applied to refine the GRACE data. It is shown that the wavelet approach could better localize the desired signal and increase the signal‐to‐noise ratio and thus results in more accurate estimation of groundwater storage variations. To validate the results of our procedure in estimation of ground water storage variations, they are compared with the measurements of pisometric wells data near the Urmia Lake which shows favorable agreements with our results.     

Seasonal Water Storage Variations as Impacted by Water Abstractions: Comparing the Output of a Global Hydrological Model with GRACE and GPS Observations

Better quantification of continental water storage variations is expected to improve our understanding of water flows, including evapotranspiration, runoff and river discharge as well as human water abstractions. For the first time, total water storage (TWS) on the land area of the globe as computed by the global water model WaterGAP (Water Global Assessment and Prognosis) was compared to both gravity recovery and climate experiment (GRACE) and global positioning system (GPS) observations. The GRACE satellites sense the effect of TWS on the dynamic gravity field of the Earth. GPS reference points are displaced due to crustal deformation caused by time-varying TWS. Unfortunately, the worldwide coverage of the GPS tracking network is irregular, while GRACE provides global coverage albeit with low spatial resolution. Detrended TWS time series were analyzed by determining scaling factors for mean annual amplitude (f _GRACE) and time series of monthly TWS (f _GPS). Both GRACE and GPS indicate that WaterGAP underestimates seasonal variations of TWS on most of the land area of the globe. In addition, seasonal maximum TWS occurs 1 month earlier according to WaterGAP than according to GRACE on most land areas. While WaterGAP TWS is sensitive to the applied climate input data, none of the two data sets result in a clearly better fit to the observations. Due to the low number of GPS sites, GPS observations are less useful for validating global hydrological models than GRACE observations, but they serve to support the validity of GRACE TWS as observational target for hydrological modeling. For unknown reasons, WaterGAP appears to fit better to GPS than to GRACE. Both GPS and GRACE data, however, are rather uncertain due to a number of reasons, in particular in dry regions. It is not possible to benefit from either GPS or GRACE observations to monitor and quantify human water abstractions if only detrended (seasonal) TWS variations are considered. Regarding GRACE, this is mainly caused by the attenuation of the TWS differences between water abstraction variants due to the filtering required for GRACE TWS. Regarding GPS, station density is too low. Only if water abstractions lead to long-term changes in TWS by depletion or restoration of water storage in groundwater or large surface water bodies, GRACE may be used to support the quantification of human water abstractions.     

Improved daily GRACE gravity field solutions using a Kalman smoother

Different GRACE data analysis centers provide temporal variations of the Earth's gravity field as monthly, 10-daily or weekly solutions. These temporal mean fields cannot model the variations occurring during the respective time span. The aim of our approach is to extract as much temporal information as possible out of the given GRACE data. Therefore the temporal resolution shall be increased with the goal to derive daily snapshots. Yet, such an increase in temporal resolution is accompanied by a loss of redundancy and therefore in a reduced accuracy if the daily solutions are calculated individually. The approach presented here therefore introduces spatial and temporal correlations of the expected gravity field signal derived from geophysical models in addition to the daily observations, thus effectively constraining the spatial and temporal evolution of the GRACE solution. The GRACE data processing is then performed within the framework of a Kalman filter and smoother estimation procedure.The approach is at first investigated in a closed-loop simulation scenario and then applied to the original GRACE observations (level-1B data) to calculate daily solutions as part of the gravity field model ITG-Grace2010. Finally, the daily models are compared to vertical GPS station displacements and ocean bottom pressure observations.From these comparisons it can be concluded that particular in higher latitudes the daily solutions contain high-frequent temporal gravity field information and represent an improvement to existing geophysical models.