Satellite geodetic observation technologies, specifically the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-on (GRACE-FO), alongside the Global Navigation Satellite System (GNSS), are routinely employed to track terrestrial water storage (TWS) changes. Due to their complementary advantages, the joint inversion of GRACE/-FO and GNSS for TWS changes is also an established practice. As another popular satellite geodetic technique, Time-series Interferometric Synthetic Aperture Radar (TS-InSAR) observes land deformation that contains elastic loading deformation associated with the terrestrial hydrological cycle. However, the application of InSAR elastic loading deformation in geodetic joint inversion for TWS changes remains a gap in the field. In this study, focusing on California during the record-setting drought from August 2019 to September 2021, we isolate the InSAR elastic loading deformation within the Central Valley aquifer and perform GRACE-FO/GNSS/InSAR and GNSS/InSAR joint inversions for TWS changes. The results indicate that the threeconstellation joint inversion distinguishes areas of groundwater storage changes more clearly in space and successfully captures signals of seasonal groundwater pumping and recharge, benefiting from the inclusion of InSAR observations. Temporally, the result shows reasonable agreement with GNSS-only inversion, GRACE-FO/ GNSS joint inversion, as well as GRACE-FO mass concentration (Mascon) and spherical harmonic coefficients (SHCs) solutions. Validation against independent hydrometeorological data confirms the reliability of the inversion performance. During the study period, the region reveals a severe TWS depletion that represents a spatial average exceeding 0.2 m in equivalent water height (EWH). We unlock the potential and reliability of InSAR elastic loading deformation in geodetic joint inversions for TWS changes under suitable conditions such as sufficient GNSS station density and relatively simple InSAR deformation driving mechanisms. We argue that our methodology holds promise for achieving refined TWS changes inversions and informing sustainable water management policies in the context of the widespread application of Sentinel-1 data and the imminent era of NISAR.

Study region Mainland China. Study focus Bridging the nearly one-year data gap between the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) remains a key challenge in terrestrial water storage anomaly (TWSA) studies. However, the adequacy of trend separation in TWSA reconstruction has received limited attention. To address this, a nonlinear trend decomposition framework was developed to isolate continuous, low-frequency trends directly from TWSA time series. Using this framework, we reconstructed the TWSA driven by various hydro-climatic variables (including precipitation, temperature, evapotranspiration, runoff, and CLSM_TWSA), and subsequently conducted comprehensive comparisons and applications. New hydrological insights for the region The nonlinear framework enhances reconstruction accuracy at both basin and grid scales. Across more than 15,000 grid cells nationwide, it outperformed the piecewise linear method, increasing the Pearson correlation coefficient (CC), Nash-Sutcliffe efficiency (NSE), and Kling-Gupta efficiency (KGE) by 2.1%, 4.3%, and 28.4%, respectively, while reducing the normalized root mean square error (NRMSE) by 40.0%. Relative to linear decomposition, the CC, NSE, and KGE improvements reached 5.4%, 10.3%, and 59.3%, respectively, with a 50.0% reduction in NRMSE. Furthermore, the reconstructed TWSA successfully captures a major flood in May 2018 within the gap, and effectively quantifies the relative contributions of human activities and natural climate variability. These prominent advantages achieved in TWSA reconstruction firmly demonstrate the global generalizability and robust application potential of this nonlinear framework.

In this study, we propose a framework that combines InSAR and hydraulic head data to estimate the aquifer parameter, elastic skeletal storage (S-ke). This framework introduces an indicator to quantify the time-independence of S-ke and comprehensively incorporates three major seasonal signal extraction methods: multichannel singular spectrum analysis, continuous wavelet transform, and independent component analysis. We reveal that no single method is universally applicable for seasonal signal extraction at observation wells with varying hydrological properties and aliasing signals to ensure the time-independence of S-ke. The proposed framework is capable of estimating most time-independent and least bias-prone S-ke, addressing the limitation of previous studies that relied on single seasonal signal extraction method while neglecting the time-independence of S-ke. We apply this framework to a study area in the North China Plain, using time-series interferometric synthetic aperture radar to survey land deformation from June 2015 to December 2017. The study area is dominated by four major subsidence bowls, with a maximum cumulative subsidence of approximately 400 mm. We then estimate S-ke with the joint constrains of seasonal InSAR deformation and hydraulic head. The estimated S(ke)range from 3.10 x 10(3) to 16.94 x 10(3) and exhibit spatial heterogeneity. In addition, we quantify the total groundwater storage (TGWS), recoverable groundwater storage (RGWS), and irreversible groundwater storage (IGWS). The TGWS depletions in the major subsidence bowls reach to 1.82 x 108 m3, and the IGWS depletions accounting for 1.25 x 10(8) m(3), and these bowls exhibit varying degrees of unsustainable groundwater exploitation. We argue that the proposed framework can enhance the accuracy of S-ke estimation and transferable to other regions facing groundwater crises to support efforts toward sustainable groundwater management.

Compared to the traditional two-step method, the dynamic one-step method fully utilizes the raw information from the observation data and theoretically yields more accurate time-variable gravity field products. However, due to the problems with the complexity of parameter space and functional model, one-step method remains a key focus and challenge in current research. We study the dynamic one-step method, presents a reasonable data processing strategy, and obtain the 60-degree temporal gravity fields for the years 2021-2022 from GRACE Follow-On (GRACE-FO) GPS and K/Ka Band Ranging (KBR) rang rate data. For the technical details of the one-step method, we focus on analyzing the impact of a priori weighting and empirical parameter son orbit and gravity field determination. The study reveals that when using GPS data with a 30 s sampling, it is necessary to down weight the GPS data appropriately to avoid introducing excessive noise. The recommended a priori weight ratio for code, carrier phase, and rang rate data is 1:10(4):10(14). To ensure the quality of the orbit and the gravity field model, empirical parameters are suggested to be co-estimated with other parameters to absorb residual perturbative force errors. Among various empirical parameters (such as piecewise periodic accelerations and kinematic empirical parameters), piecewise constant accelerations are more effective in absorbing noise in the model while maintaining orbit accuracy. Furthermore, under the same dynamic parameter configuration, the time-variable gravity field model driven from the dynamic one-step method outperforms the two-step method interms of both consistency with the official model and precision. Finally, a comprehensive evaluation of the orbit and time-variable gravity field model over the entire time span is conducted. The results indicate that the orbits determined by the dynamic one-step method meet centimeter-level requirements, with a standard deviation of 1.6cm for the satellite laser ranging (SLR) residuals of twin satellites. The gravity field model exhibits good consistency with the latest RL06.1 models released by CSR (Center for Space Research), JPL (Jet Propulsion Laboratory), and GFZ (Geo Forschungs Zentrum Potsdam). While preserving the full characteristics of time-variable signals, the noise performance is comparable to the CSR model and better than the JPL and GFZ models.

Study regions: The Pearl River Basin, China. Study focus: The terrestrial water storage anomalies (TWSA) tracked by the Gravity Recovery and Climate Experiment (GRACE) mission are valid observations for drought monitoring. However, the coarse resolution and short duration limit their potential applications at local scales. In previous studies, these drawbacks are addressed by statistical downscaling and hindcasting techniques, but usually as two separate processes. In this study, a novel deep learning model is designed to integrate downscaling and hindcasting into a unified framework. Based on the TWSA generated by this model, the drought recovery rate, the propagation threshold of hydrological drought, and their dynamics are investigated with longer duration (1982-2017) and finer spatial resolution (0.1 degree). New hydrological insights for the region: The basin is more vulnerable to hydrological drought in the last three decades. Droughts occurred in 16 % of months, with an average duration of 6.3 months and an average recovery time of 4.5 months before 2002, the proportion increased to 33 %, with an average duration of 7.8 months and an average recovery time of 4.7 months after 2002. The propagation threshold for hydrological drought in the basin is loosening, with basin-averaged changes of 0.03 and 0.02 per year for abnormally dry and moderate drought scenarios, respectively. Nevertheless, the central portion exhibits increased resilience to hydrological drought as precipitation increases and temperature falls.

The flex power technology in satellite navigation systems enhances anti-jamming capabilities but can impact the quality of GPS observations and the accuracy of low Earth orbit determination, such as GRACE Follow-On (GRACE-FO) mission. This study investigates the influence of GPS flex power on Hatch-Melbourne-Wübbena (HMW) linear combinations and GRACE-FO kinematic orbits from January 1 to September 30, 2020. Epoch-differenced K-Band Ranging (KBR) data is introduced in orbit determination during the flex power period to improve both absolute and inter-satellite relative accuracy. The analysis indicates that the influence of early flex power (before February 13, 2020) on HMW combinations and orbits is minimal, whereas the effect of later flex power (after February 14, 2020) is significant: (1) HMW combinations exhibit notable systematic discontinuities even with elevation angles greater than 50 degrees, causing the fixing rate of wide-lane ambiguities to drop from 96% to 80%. (2) Kinematic absolute orbits show significant deteriorations of approximately 9 mm and 4 mm in the three-dimensional direction for float and integer ambiguity resolution (FAR and IAR), while relative accuracy of FAR and IAR orbits decreases by 50% and 46%, respectively. However, using epoch-differenced KBR (DKBR) data, the accuracy of absolute orbits could be increased by up to 15% and the accuracy of relative orbits could be improved by at least 69%, which showcases a positive effect. Thus, this can be considered as an alternative method to improve the accuracy of GRACE-FO orbit during the flex power period.

In this study, we conducted a quantitative analysis of the factors affecting Sentinel-1 interferometric decorrelation in a typical agricultural region in China, where maize and rice, two of the three staple crops in the country, are the primary crop types. Bayesian network and random forest regression analysis were employed to examine the impact of temporal and spatial decorrelation, Doppler centroid differences, and surface cover variations. Among these factors, the temporal baseline is the most significant contributor to decorrelation. This finding opens up the possibility of utilizing Sentinel-1 coherence, which quantifies the degree of decorrelation, as a mapping feature for staple crops. Consequently, we conducted classification experiments for staple crops in this typical agricultural region, utilizing timeseries Sentinel-1 coherence data and considering polarization modes. The highest overall accuracy achieved was 98.08 % for the shortest temporal baseline (12 days), with VV polarization outperforming VH polarization. In comparison, we conducted classification experiments using Sentinel-1 intensity backscatter data in VV and VH channels. The combined use of coherence and backscatter data yielded superior results compared to using either data source alone. Furthermore, we conducted classification experiments based on Sentinel-2 optical remote sensing features. To sum up, we compared the classification performance of Sentinel-1 coherence with that of intensity and optical remote sensing features, demonstrating the reliability of time-series Sentinel-1 coherence as a valuable tool for mapping staple crops in China's crucial grain-producing regions. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

SUMMARY In this study, we analysed the impacts of errors in background force models and observed non-gravitational forces on the pseudo-observations (pre-fits) during gravity field recovery based on the Gravity Recovery and Climate Experiment (GRACE) satellite gravity mission. To reduce these effects, we introduced the stochastic parameters into the functional model of the variational equation integration approach to absorb this type of noise contribution. Simultaneously, the prior variances of observed orbits and K-band range rates used in traditional method are re-estimated with least-squares variance component estimation (LS-VCE) after considering these stochastic parameters. To improve the computing efficiency, a modified method of the calculation of sensitivity matrices related to the introduced stochastic parameters is proposed. Compared to the method of variation of constants widely used in the precise orbit determination and gravity field recovery, the modified method decreases the computational time of these matrices by about four times. Furthermore, an efficient LS-VCE algorithm is derived in a more generalized case. The efficient algorithm only costs 1 per cent of the time of the unoptimized method. With the GRACE data, we analysed the benefits of these refinements in gravity field recovery, and the results show that these improvements can mitigate the impacts of errors in background force models and accelerometer data on recovered gravity field models, especially in the high-degree signals. Furthermore, the quality of results has less dependence on parametrization.

The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions provide unprecedented approaches for tracking terrestrial water storage anomalies (TWSA). However, evaluating longterm hydrologic states requires continuous TWSA without the similar to 11-month gap between the two GRACE missions. Trend prediction is a challenging problem for TWSA gap-filling. There are three common methods for handling trends in previous efforts, i.e., de-trending the TWSA, adding back the long-term or piecewise trends of GRACE/-FO to the detrended predictions, or not performing such trend replacement. However, a single global application of one of these methods will not produce optimal results. Therefore, we designed a framework to select the optimal trend replacement strategy for each grid in this study. Based on this framework, we better filled the gap (excluding Antarctica) using machine learning techniques adopting the Global Land Data Assimilation System (GLDAS) Noah TWSA, precipitation, and temperature as inputs. The median gridwise Nash-Sutcliffe efficiency of the result generated by our framework improves by 0.08 compared to the result of a single long-term trend replacement strategy. Furthermore, we quantitatively evaluated the impact of three predictive strategies on the results: selection of leader machine learning technique, selection of optimal trend replacement strategy, and selection of most relevant inputs. The results indicate that the selection of trend replacement strategy has the greatest influence, followed by the selection of machine learning technique and then the selection of inputs. In addition, we found that in areas with abundant surface water, utilizing surface water anomalies as an additional predictor benefits the results. Our study is expected to provide suggestions for better TWSA predictive strategies.

As a crucial payload on dedicated gravity satellites, the accelerometer (ACC) measures the non-gravitational force acting on the satellite. The unknown scale and bias contaminate the raw ACC data, preventing the direct use in precise orbit determination (POD) and gravity recovery, and thus ACC data need to be calibrated. We analyze the performance of GRACE-FO ACC and calibrate the ACC data from 2018 to 2021 based on a step-by-step calibration method. First, we give an overview of temperature records and ACC operational modes, which reflect the operational status of the ACC. The calibration method consists of four steps: pre-calibration, two POD processes with different parameterizations, and bias fitting. Low-degree/order (10 × 10) spherical harmonic coefficients (SHCs) are estimated with scale, bias, and other dynamic parameters in POD. As an assessment, we compare the calibrated ACC data and the modeled non-gravitational force products. The average differences between them are less than 5 nm/s 2 in the SRF- X direction and 6 nm/s 2 in other directions. The obtained orbits based on ACC data and GPS observations are compared with precise science orbits. Furthermore, six tests show that introducing low-order/degree SHCs could effectively improve the consistency of the calibrated ACC data with non-gravitational force models and enhance orbit determination. Otherwise, empirical accelerations should be estimated with loose constraints to ensure POD results.

Limited by the north-south stripes and noise, it is challenging to estimate mass flux variations with high-precision from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) solutions. To extract more geophysical signals from the GRACE/GRACE-FO spherical harmonic coefficients (SHCs), we developed an improved spatial domain filtering (ISDF) approach based on terrain constraints to obtain the mass flux variations in the southern Tibetan Plateau. From the results of simulated data, compared with the actual signal, the Nash - Sutcliffe efficiency (NSE) calculated using the ISDF method (more than 0.86) is much higher than the traditional filtering result. Compared with the traditional filtering method (i.e. unconstrained Gaussian Filtering), the root mean squared error (RMSE) is reduced by about 27.7% of that calculated using the ISDF method from the simulated data with north-south stripes and noise. Moreover, the signal-to-noise ratio (SNR) calculated using the ISDF method has also been significantly improved. In the GRACE/GRACE-FO SHCs experiments, we compared the results calculated using the ISDF method with the mascon solutions of the three official centres, i.e. Center for Space Research (CSR), Jet Propulsion Laboratory (JPL), and Goddard Space Flight Center (GSFC). The results of time series decomposition show that the signals calculated using the ISDF method agree well with the mean-mascon solution and the annual amplitude and semi-annual amplitude differences between the two data are 0.01 cm and -0.61 cm, respectively. Our results underline that the ISDF is an efficient approach for estimating mass flux variations in the southern Tibetan Plateau from GRACE/GRACE-FO SHCs. Finally, we analysed the mass attenuation rates in three periods and the possible reasons in the southern Tibetan Plateau based on the different hydrological models.

全球正向建模恢复法改正南极泄漏误差的效果取决于初始信号的准确性.球谐系数的截断和滤波导致南极周边海洋信号内泄漏至相邻的南极陆地,造成相应区域初始信号不准确.以美国德克萨斯大学的空间研究中心(Center for Space Research,CSR)Mascon数据为模拟数据,讨论周边海洋信号对南极泄漏误差改正的影响.结果表明,海洋信号内泄漏对南极冰盖的质量变化及其空间分布影响显著,造成东南极Coats Land、Queen Maud Land、Enderby Land和Kemp Land等沿海地区反演结果不准确.引入"流域"函数分离海洋信号,利用全球正向建模恢复法改正南极泄漏误差,通过CSR RL05 GSM(glacial systems model)数据估算出南极冰盖的质量变化速率为-180.66 Gt/a,且质量变化空间分布与Mascon数据一致性较好.

利用重力恢复与气候实验(gravity recovery and climate experiment,GRACE)时变地球重力场模型计算得到非洲奥卡万戈三角洲地区2003-01-2014-12的陆地水储量变化信息,分别采用主成分分析(principal component analysis,PCA)和独立成分分析(independent component analysis,ICA)提取质量变化信号,并与全球陆地数据同化系统(global land data assimilation system,GLDAS)的水文模型进行对比.结果 显示,在奥卡万戈河流域东北部,水储量表现出很强的周期性变化,两种数据空间特征分布的信号出现在相同位置的成分GRACE-IC1和GLDAS-IC1对应的时间序列的相关系数达到0.85.奥卡万戈三角洲地区水储量从2003-01-2011-10呈现上升趋势,两种数据空间特征分布的信号出现在相同位置的成分GRACE-IC2和GLDAS-IC3对应的时间序列的相关系数达到0.81,说明GRACE反演结果与GLDAS水文模型反演结果在研究区域内具有很强的一致性.引入全球降水气候中心降水数据和Water GAP全球水文模型数据对研究区域陆地水储量变化的原因进行分析.实验结果表明,相对于传统的多项式拟合方法,ICA可以在较大区域内直接对特定位置质量变化信号的时空特征进行提取;对比GRACE数据两种方法分解结果的第3成分可以看出,在空间尺度和时间尺度上,ICA方法对信号的分解能力要优于主成分分析方法.

ABSTRACT Estimating terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) solutions is an essential technique for water resources management. TWS estimation accuracy from the GRACE spherical harmonic coefficients (SHCs) is limited by the north–south stripes noise. We developed an iterative filtering (IF) method with high accuracy and efficiency to extract TWS signals in the Amazon Basin . The results show that the root mean squared error of TWS anomalies calculated using IF is ~52% smaller than FM (forward modelling) in synthesized data, while the signal-to-noise ratios (SNR) of TWS anomalies calculated using IF is improved by ~30%. The annual amplitude of TWS anomalies using IF (10.51 ± 0.28 cm) is close to the true signal (10.57 ± 0.28 cm). From the real GRACE experiment, both FM and IF can extract most of the TWS signals. However, the IF’s anti-noise ability (i.e., average SNR = 24.08) is significantly better than FM’s ability (average SNR = 19.01), which indicates the result using IF can achieve the equivalent level of accuracy as mass concentration solutions in the Amazon Basin. Thus, the IF is a robust and efficient method for extracting TWS signals in the Amazon Basin from GRACE SHCs.

Soil moisture (SM) and groundwater (GW) depletion triggered by anthropogenic and natural climate change are influencing food security via crop production per capita decrease in the Nile River Basin (NRB). However, to the best of our understanding, the causes and impact of SM and GW depletion have not been studied yet comprehensively in the NRB. In this study, GW is derived from the Gravity Recovery and Climate Experiment (GRACE) mission, and SM was estimated using the Triple Collocation Analysis (TCA). SM/GW depletion causes were evaluated via the Land Use Land Cover (LULC) and rainfall/temperature change analysis, whereas impact analysis focused on crop production per capita reduction (food insecurity) during SM depletion. The major findings of this study are 1) TCA analyzed SM show a decreasing trend (-0.06 mm/yr) in agricultural land while increasing (+0.21 mm/yr) in forest land, 2) LULC analysis indicated a vast increment of agricultural land (+9%) and bareland (+9%) although the decreasing pattern of forest (-1.5%) and shrubland (-6.9%) during 1990-2019; 3) the impact of SM depletion on crop production per capita caused food insecurity during a drought year, 4) agriculture drought indices and crop production per capita show high correlations (R2 = 0.86 to 0.60) demonstrated that Vegetation Supply Water Index (VSWI) could provide strategic warning of drought impacts on rainfed agricultural regions. In conclusion, SM and GW depletions are mainly caused by human-induced and climate change factors imposing food insecurity challenges in the NRB coupled with increasing temperature and excessive water extraction for irrigation. Therefore, it is highly recommended to rethink and reverse SM/GW depletion causing factors to sustain food security in NRB and similar basins.

In order to overcome the problem of large stripes error and leakage error when using radial point mass method for inversion of GRACE time-varying gravity field, a variance constraint radial point mass method (VCPM) is developed in this study. In this method, the prior variance information of mass blocks is used to design regularization matrix and provides constrain for the inversion procedure. Besides, the iterative regularization method is applied to solve the problem that a single regularization parameter cannot adapt to the uneven global mass variation. In order to validate the reliability of the promoted method, the global mass variation from January 2003 to November 2014 are calculated, and then are compared with results from the GRACE spherical harmonic method, GLDAS2. 1 hydrological model, CSR/JPL RL06 Mascon in terms of global root mean square error (RMSE), long-term trend, annual amplitude, local cryosphere and land basins. The result shows that the values of RMSE of differences between VCPM and CSR Mascon over global, continental and ocean areas are 2. 12 cm, 4.16 cm and 1. 25 cm respectively, which are lower than that of JPL Mascon and CSR Mascon (2. 22 cm and 4. 62 cm) in global and terrestrial areas, and slightly higher than that of CSR Mascon and JPL Mascon (1. 18 cm) in ocean areas. The time series over 8 land basins and inland sea areas are calculated based on the VCPM and two kinds of Mascon datasets, the correlation coefficients between these results would be larger than 0. 86 in most regions. In addition, compared with the traditional point mass method and spherical harmonic potential coefficients method, the VCPM can effectively suppress leakage errors in areas such as Antarctica and Greenland, which indicates that the VCPM method developed in this study could effectively constrain the stripes error, decrease the sea-land signal leakage and greatly improve the accuracy and spatial resolution of the estimated results. Therefore, the VCPM would provide an effective method for calculating earth's surface mass variation with high precision and resolution.

Hydroclimatic extremes such as droughts and floods triggered by human-induced climate change are causing severe damage in the Nile River Basin (NRB). These hydroclimatic extremes are not well studied in a holistic approach in NRB. In this study, the Gravity Recovery and Climate Experiment (GRACE) mission and its Follow on mission (GRACE-FO) derived indices and other standardized hydroclimatic indices are computed for developing monitoring and evaluation methods of flood and drought. We evaluated extreme hydroclimatic conditions by using GRACE/GRACE-FO derived indices such as water storage deficits Index (WSDI); and standardized hydroclimatic indices (i.e., Palmer Drought Severity Index (PDSI) and others). This study showed that during 1950–2019, eight major floods and ten droughts events were identified based on standardized-indices and GRACE/GRACE-FO-derived indices. Standardized-indices mostly underestimated the drought and flood severity level compared to GRACE/GRACE-FO derived indices. Among standardized indices PDSI show highest correlation (r2 = 0.72) with WSDI. GRACE-/GRACE-FO-derived indices can capture all major flood and drought events; hence, it may be an ideal substitute for data-scarce hydro-meteorological sites. Therefore, the proposed framework can serve as a useful tool for flood and drought monitoring and a better understanding of extreme hydroclimatic conditions in NRB and other similar climatic regions.

The Nile River Basin (NRB) is facing extreme pressure on its water resources due to an alarmingly increasing population that is extremely vulnerable in aspects of irrigation and hydropower. The NRB ascends itself to remotely sensed approaches with high resolution of spatial and temporal coverage as disparate to ground-based in-situ observations due to its size and limited access from basin countries. The Gravity Recovery and Climate Experiment (GRACE) allow a unique opportunity to investigate the changes in key components of Terrestrial Water Storage (TWS). Differences in tuning parameters and processing strategies result in GRACE TWS solutions with regionally specific variations and error patterns. We explored the spatiotemporal changes of the TWS time series, trend, uncertainties, and signal-to-noise ratio (SNR) among different GRACE TWS. We had also investigated the key terrestrial water storage components (surface water, soil moisture, and groundwater storage changes). The results show that the uncertainty of GRACE spherical harmonic (SH) solutions are higher than the mass concentration (mascon) over the NRB, and the Center for Space Research-mascons (CSR-M) noted the first best performance. Substantially, significant long-term (2003–2017) negative groundwater and soil moisture trend demonstrates a potential depletion over NRB. Despite an increase in precipitation and TWS time series, the rate of decline noted to increase rapidly from 2008, thus indicating the possibility of human-induced change ( e.g., for irrigation purposes). Thus, the result of this study provides a guiding principle for future studies in TWS change-related hydro-climatic change over NRB and similar basins.

Soil moisture (SM) and groundwater (GW) depletion triggered by anthropogenic and natural climate change are influencing food security via crop production decrease in the Nile River Basin (NRB). In the NRB, to the best of our understanding, the causes/impact of SM/GW depletion has not been studied yet comprehensively. In this study, GW is derived from the Gravity Recovery and Climate Experiment (GRACE) mission, and SM was estimated using the Triple Collocation Analysis (TCA). SM/GW depletion causes were evaluated via the Land Use Land Cover (LULC) and rainfall/temperature change analysis, whereas impact analysis focused on crop production reduction (food insecurity) during SM depletion. The major findings of this study are: 1) TCA analyzed SM show a decreasing trend (-0.06mm/yr) in agricultural land while increasing (+0.21mm/yr) in forest land, 2) LULC analysis indicated vast increment of agricultural land (+9%) and bareland (+9%) although the decreasing pattern of forest (-1.5%) and shrubland (-6.9%) during 1990–2019; 3) the impact of SM depletion on crop production caused food insecurity during a drought year, 4) agriculture drought indices and crop production show high correlations (R 2 =0.86 to 0.60) demonstrated that Vegetation Supply Water Index (VSWI) could provide strategic warning of drought impacts on rainfed agricultural regions. In conclusion, SM and GW depletions are mainly caused by human-induced and climate change factors imposing food insecurity challenges in the NRB coupled with increasing temperature and excessive water extraction for irrigation. Therefore, we recommend reforestation and soil-water-conservation measures to reverse SM/GW depletion trend.

In this paper, GRACE Release-06 monthly time-variable gravity field released by CSR, satellite altimetry data, land surface model data, precipitation data and evaporation data from January 2003 to June 2017 over the Lake Victoria Basin are used to analyze the spatiotemporal variability of terrestrial water storage (TWS), and compare the effect of forward modeling method and basin scale factor method on the correction of leakage error in the basin. Our analysis indicates that the forward modeling method can correct leakage errors effectively, which is applied to recover the signal attenuation caused by leakage in our study. By using the GRACE Release-06 satellite gravity products, a significant increase trend is detected in the basin from January 2003 to June 2017, the estimated rates from spherical harmonic coefficient products and Mascon products are 14. 9 mm.a(-1) and 16. 7 mm.a(-1), respectively. The observation error from GRACE Release-06 projects is smaller than that of Release-05, while the results from Release-05 underestimated the change rate of water storage. From January 2013 to February 2016, both satellite gravimetry and satellite altimetry detect water storage increase over the Lake Victoria Basin while hydrological models show a decrease which is speculated to be caused by dam impoundments. Influenced by El Nino events, rainfall in the basin decreased, which further cause the terrestrial water storage decrease from March 2016 to June 2017. During this period, the decrease trend in TWS detected by GRACE spherical harmonic coefficient products and Mascon products was -100. 3 mm.a(-1) and 129. 7 mm.a(-1), respectively. The results of our study show that satellite observations can provide a feasible way to analyze the impact of human activities and natural changes on regional water storage change in the absence of direct in-situ observation data, which also provides a reference for studying the variations of water storage in lake basins in China.