Search Results (16)

The Pearl River Basin (PRB) is a humid subtropical system where frequent floods and recurrent droughts challenge water management. GRACE and GRACE Follow-On provide basin-scale constraints on terrestrial water storage anomalies (TWSA), yet their coarse native resolution limits applications at regional scales. We employ a downscaled TWSA product derived via a joint inversion that integrates GRACE/GFO observations with the high-resolution spatial patterns of WaterGap Global Hydrological Model (WGHM). Validation against GRACE/GFO shows that the downscaled product outperforms WGHM at basin and pixel scales, with consistently lower errors and higher skill, and with improved terrestrial water flux (TWF) estimates that agree more closely with water balance calculations in both magnitude and phase. The TWSA in the PRB exhibits strong seasonality, with precipitation (P) exceeding evapotranspiration (E) and runoff (R) from April to July and storage peaking in July. From 2002 to 2022, the basin alternates between multi-year declines and recoveries. On the annual scale, TWSA covaries with precipitation and runoff, and large-scale climate modes modulate these relationships, with El Niño and a warm Pacific Decadal Oscillation (PDO) favoring wetter conditions and La Niña and a cold PDO favoring drier conditions. extreme gradient boosting (XGBoost) with shapley additive explanations (SHAP) attribution identifies P as the primary driver of storage variability, followed by R and E, while vegetation and radiation variables play secondary roles. Drought and flood diagnostics based on drought severity index (DSI) and a standardized flood potential index (FPI) capture the severe 2021 drought and major wet-season floods. The results demonstrate that joint inversion downscaling enhances the spatiotemporal fidelity of satellite-informed storage estimates and provides actionable information for risk assessment and water resources management. Full article

The Amazon River Basin (ARB) experienced an extreme drought from summer 2023 to spring 2024, driven by complex interactions among multiple climatic and environmental factors. A detailed investigation into this drought is crucial in understanding the entire process of the drought. Here, we employ drought indices derived from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GFO), and Swarm missions to reconstruct the drought’s progression, combined with reanalysis datasets and extreme-climate indices to analyze atmospheric and hydrological mechanisms. Our findings reveal a six-month drought from September 2023, reaching a drought peak of −1.29 and a drought severity of −5.62, with its epicenter migrating systematically from the northwestern to southeastern basin, spatially mirroring the 2015–2016 extreme drought pattern. Reduced precipitation and abnormal warming were the direct causes, which were closely linked to the 2023 El Niño event. This event disrupted atmospheric vertical movements. These changes led to abnormally strong sinking motions over the basin, which interacted synergistically with anomalies in land cover types caused by deforestation, triggering this extreme drought. This study provides spatiotemporal drought diagnostics valuable for hydrological forecasting and climate adaptation planning. Full article

In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is crucial to develop appropriate drought and flood policies based on the drought and flood characteristics of different sub-basins. This study calculated the water storage deficit index (WSDI) based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GFO) mascon model, extended WSDI to the bidirectional monitoring of droughts and floods in the YRB, and verified the reliability of WSDI in monitoring hydrological events through historical documented events. Combined with the wavelet method, it revealed the heterogeneity of climate responses in the three sub-basins of the upper, middle, and lower reaches. The results showed the following. (1) Compared and verified with the Standardized Precipitation Evapotranspiration Index (SPEI), self-calibrating Palmer Drought Severity Index (scPDSI), and documented events, WSDI overcame the limitations of traditional indices and had higher reliability. A total of 21 drought events and 18 flood events were identified in the three sub-basins, with the lowest frequency of drought and flood events in the upper reaches. (2) Most areas of the YRB showed different degrees of wetting on the monthly and seasonal scales, and the slowest trend of wetting was in the lower reaches of the YRB. (3) The degree of influence of teleconnection factors in the upper, middle, and lower reaches of the YRB had gradually increased over time, and, in particular, El Niño Southern Oscillation (ENSO) had a significant impact on the droughts and floods. This study provided a new basis for the early warning of droughts and floods in different sub-basins of the YRB. Full article

Geodetic data, especially from the Global Navigation Satellite System (GNSS) and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GFO), are extensively employed in hydrological drought monitoring across various spatial scales due to their unique spatial resolution. In recent years, Brazil has experienced some of the most severe drought events in decades. This study focuses on Brazil and its northeastern Amazon Plain, investigates the spatiotemporal characteristics of terrestrial water storage (TWS) changes, and calculates the hydrological drought severity index (DSI) and meteorological drought index for comprehensive analysis of drought conditions. The results indicate that the time series of TWS changes derived from different data sources are highly correlated, with correlation coefficients exceeding 0.85, and are consistent with the trend of precipitation variation, reflecting notable seasonal fluctuations, i.e., an increase in precipitation during the spring and summer seasons leads to a rise in TWS, while a decrease in precipitation during the autumn and winter seasons triggers a reduction in TWS. In terms of spatial distribution, the annual amplitude of TWS variation is most pronounced in the northeastern Amazon Plain. The highest amplitude, approximately 800 mm, is observed near the Amazon River Basin, and this amplitude gradually weakens from northeast to southwest. GNSS and GRACE/GFO data reveal four hydrological drought events in Brazil from 2013 to 2024, with two of these events detected using GRACE/GFO data. The most severe droughts occurred between 2023 and 2024, primarily driven by prolonged precipitation deficits and the El Niño phenomenon, lasting up to nine months. Additionally, three distinct drought events were identified in the Amazon Plain, suggesting that its hydrological dynamics significantly influenced Brazil’s drought conditions. These results demonstrate the capability of geodetic data to effectively monitor water deficit and drought duration on both small spatial scales and short timeframes, thereby providing crucial support for timely responses to and the management of hydrological drought events. Full article

This paper presents an analysis of regional terrestrial water storage (TWS) changes and drought characteristics in Southwest China, encompassing Sichuan Province, Chongqing Municipality, Yunnan Province, and Guizhou Province. Existing geodetic datasets, such as those from the Gravity Recovery and Climate Experiment (GRACE) and its successor satellites (GRACE Follow-On), as well as Global Navigation Satellite System (GNSS) data, face significant challenges related to limited spatial resolution and insufficient station distribution. To address these issues, we propose a novel inversion method that integrates GNSS and GRACE/GFO data by establishing virtual stations for GRACE/GFO data and determining the weight values between GNSS and GRACE/GFO using the Akaike Bayesian Information Criterion (ABIC). This method allows for estimating the TWS changes from December 2010 to June 2023 and monitoring drought conditions in conjunction with hydrometeorological data (precipitation, evapotranspiration, and runoff). The results show strong correlations between TWS changes from the joint inversion and GNSS (0.98) and GRACE/GFO (0.69). The Joint Drought Severity Index (Joint-DSI) indicates five major drought events, with the most severe occurring from July 2022 to June 2023, with an average deficit of 86.133 km³. Extreme drought primarily impacts Sichuan and Yunnan, driven by abnormal precipitation deficits. The joint inversion methodology presented in this study provides a practical approach for monitoring TWS changes and assessing drought characteristics in Southwest China. This paper leverages the complementary strengths of GNSS and GRACE/GFO data and offers new insights into regional water resource management and drought detection. Full article

As China’s largest inland river basin and one of the world’s most arid regions, the Tarim River Basin is home to an extremely fragile ecological environment. Therefore, monitoring the water storage changes is critical for enhancing water resources management and improving hydrological policies to ensure sustainable development. This study reveals the spatiotemporal changes of water storage and its driving factors in the Tarim River Basin from 2002 to 2022, utilizing data from GRACE, GRACE-FO (GFO), GLDAS, the glacier model, and measured hydrological data. In addition, we validate GRACE/GFO data as a novel resource that can monitor the ecological water conveyance (EWC) benefits effectively in the lower reaches of the basin. The results reveal that (1) the northern Tarim River Basin has experienced a significant decline in terrestrial water storage (TWS), with an overall deficit that appears to have accelerated in recent years. From April 2002 to December 2009, the groundwater storage (GWS) anomaly accounted for 87.5% of the TWS anomaly, while from January 2010 to January 2020, the ice water storage (IWS) anomaly contributed 57.1% to the TWS anomaly. (2) The TWS changes in the Tarim River Basin are primarily attributed to the changes of GWS and IWS, and they have the highest correlation with precipitation and evapotranspiration, with grey relation analysis (GRA) coefficients of 0.74 and 0.68, respectively, while the human factors mainly affect GWS, with an average GRA coefficient of 0.64. (3) In assessing ecological water conveyance (EWC) benefits, the GRACE/GFO-derived TWS anomaly in the lower reaches of the Tarim River exhibits a good correspondence with the changes of EWC, NDVI, and groundwater levels. Full article

Regarding the terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (-FO) gravity satellite missions, a BEAST (Bayesian estimator of abrupt change, seasonal change and trend)+GMDH (group method of data handling) gap-filling scheme driven by hydrological and meteorological data is proposed. Considering these driving data usually cannot fully capture the trend changes of the TWSA time series, we propose first to use the BEAST algorithm to perform piecewise linear detrending for the TWSA series and then fill the gap of the detrended series using the GMDH algorithm. The complete gap-filling TWSAs can be readily obtained after adding back the previously removed piecewise trend. By comparing the simulated gap filled by BEAST + GMDH using Multiple Linear Regression and Singular Spectrum Analysis with reference values, the results show that the BEAST + GMDH scheme is superior to the latter two in terms of the correlation coefficient, Nash-efficiency coefficient, and root-mean-square error. The real GRACE/GFO gap filled by BEAST + GMDH is consistent with those from hydrological models, Swarm TWSAs, and other literature regarding spatial distribution patterns. The correlation coefficients there between are, respectively, above 0.90, 0.80, and 0.90 in most of the global river basins. Full article

The conventional average acceleration approach relies on K-band range observation, containing an unknown bias, which leads to possible degradation of the precision of Earth’s gravity field modelling. It also suffers from correlated errors caused by three-point numerical differentiation. In this study, an improved approach is proposed that makes use of K-band range-rate observations instead and overcoming the influence of correlated errors by introducing a whitening filter. GRACE-Follow On data spanning the period from January 2019 to December 2022 were processed by the proposed approach and a series of time-varying gravity field models was derived, referred to as SSM-AAA-GFO in this paper. This model series is compared comprehensively with three official model series. Results demonstrate that all model series are highly coincident below degree 30 and reflect similar time-varying gravity field signals in both large and small basins. After filtering, SSM-AAA-GFO shows uncertainty, in the form of equivalent water height below 2.5 cm, which is comparable with three official model series. The comparison results confirm the effectiveness of the proposed approach for precisely modelling a time-varying gravity field based on K-band range-rate observations. Full article

Global Navigation Satellite System (GNSS) vertical displacements are widely used to infer terrestrial water storage (TWS) variations. The traditional Laplacian inversion requires dedicated efforts to determine the optimal parameters, which has an important effect on the spatial patterns. In this study, we develop a new GNSS inversion method with flexible spatial constraints. One major merit is that the new method only requires loose boundary conditions rather than optimal parameters. A closed-loop simulation shows that the inversion using spatial constraints is improved by 7–21% compared with the Laplacian constraints. We apply this method to 18 watersheds across the Contiguous United States (CONUS) to infer daily TWS variations from January 2018 to August 2022. The results show that the amplitudes of monthly TWS time series from the spatial and Laplacian constraints are comparable to the Gravity Recovery and Climate Experiment (GRACE) Follow-On (GFO) in 16 watersheds. Furthermore, the standard deviation between the spatial constraints and GFO is at the same level as that between the Laplacian constraints and GFO. We also extract the daily TWS variations caused by heavy precipitation events in California. Our results demonstrate that spatial constraint inversion supplements the existing constraint strategies of GNSS inversion in hydrogeodesy; therefore, spatial constraint inversion can be an alternative tool for GNSS inversion. Full article

Terrestrial water storage (TWS) is a pivotal component of the global water cycle, profoundly impacting water resource management, hazard monitoring, and agriculture production. The Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-On (GFO), have furnished comprehensive monthly TWS data since April 2002. However, there are 35 months of missing data over the entire GRACE/GFO observational period. To address this gap, we developed an operational approach utilizing singular spectrum analysis and principal component analysis (SSA-PCA) to fill these missing data over mainland China. The algorithm was demonstrated with good performance in the Southwestern River Basin (SWB, correlation coefficient, CC: 0.71, RMSE: 6.27 cm), Yangtze River Basin (YTB, CC: 0.67, RMSE: 3.52 cm), and Songhua River Basin (SRB, CC: 0.66, RMSE: 7.63 cm). Leveraging two decades of continuous time-variable gravity data, we investigated the spatiotemporal variations in TWS across ten major Chinese basins. According to the results of GRACE/GFO, mainland China experienced an average annual TWS decline of 0.32 ± 0.06 cm, with the groundwater storage (GWS) decreasing by 0.54 ± 0.10 cm/yr. The most significant GWS depletion occurred in the Haihe River Basin (HRB) at −2.07 ± 0.10 cm/yr, significantly substantial (~1 cm/yr) depletions occurred in the Yellow River Basin (YRB), SRB, Huaihe River Basin (HHB), Liao-Luan River Basin (LRB), and Southwest River Basin (SWB), and moderate losses were recorded in the Northwest Basin (NWB, −0.34 ± 0.03 cm/yr) and Southeast River Basin (SEB, −0.24 ± 0.10 cm/yr). Furthermore, we identified that interannual TWS variations in ten basins of China were primarily driven by soil moisture water storage (SMS) anomalies, exhibiting consistently and relatively high correlations (CC > 0.60) and low root-mean-square errors (RMSE < 5 cm). Lastly, through the integration of GRACE/GFO and Global Land Data Assimilation System (GLDAS) data, we unraveled the contrasting water storage patterns between northern and southern China. Southern China experienced drought conditions, while northern China faced flooding during the 2020–2023 La Niña event, with the inverse pattern observed during the 2014–2016 El Niño event. This study fills in the missing data and quantifies water storage variations within mainland China, contributing to a deeper insight into climate change and its consequences on water resource management. Full article

High-precision inertial sensors or accelerometers can provide references for free-falling motion in gravitational fields in space. They serve as the key payloads for gravity recovery missions such as CHAMP, the GRACE-type missions, and the planned Next-Generation Gravity Missions. In this work, a systematic method for electrostatic inertial sensor calibration of gravity recovery satellites is suggested, which is applied to and verified with the Taiji-1 mission. With this method, the complete operating parameters including the scale factors, the center of mass offset vector, and the intrinsic biased acceleration can be precisely calibrated with only two sets of short-term in-orbit experiments. This could reduce the gaps in data that are caused by necessary in-orbit calibrations during the lifetime of related missions. Taiji-1 is the first technology-demonstration satellite of the “Taiji Program in Space”, which, in its final extended phase in 2022, could be viewed as operating in the mode of a high–low satellite-to-satellite tracking gravity mission. Based on the principles of calibration, swing maneuvers with time spans of approximately 200 s and rolling maneuvers for 19 days were conducted by Taiji-1 in 2022. Given the data of the actuation voltages of the inertial sensor, satellite attitude variations, precision orbit determinations, the inertial sensor’s operating parameters are precisely re-calibrated with Kalman filters and are relayed to the Taiji-1 science team. The relative errors of the calibrations are <1% for the linear scale factors, <3% for center of mass, and <0.1% for biased accelerations. Data from one of the sensitive axes are re-processed with the updated operating parameters, and the resulting performance is found to be slightly improved over the former results. This approach could be of high reference value for the accelerometer or inertial sensor calibrations of the GFO, the Chinese GRACE-type mission, and the Next-Generation Gravity Missions. This could also create some insight into the in-orbit calibrations of the ultra-precision inertial sensors for future GW space antennas because of the technological inheritance between these two generations of inertial sensors. Full article

This study focuses on the development of a time-variable regional geo-potential model for Antarctica using the spherical cap harmonic analysis (SCHA) basis functions. The model is derived from line-of-sight gravity difference (LGD) measurements obtained from the GRACE-Follow-On (GFO) mission. The solution of a Laplace equation for the boundary values over a spherical cap is used to expand the geo-potential coefficients in terms of Legendre functions with a real degree and integer order suitable for regional modelling, which is used to constrain the geo-potential coefficients using LGD measurements. To validate the performance of the SCHA, it is first utilized with LGD data derived from a L2 JPL (Level 2 product of the Jet Propulsion Laboratory). The obtained LGD data are used to compute the local geo-potential model up to K_max = 20, corresponding to the SH degree and order up to 60. The comparison of the radial gravity on the Earth’s surface map across Antarctica with the corresponding radial gravity components of the L2 JPL is carried out using local geo-potential coefficients. The results of this comparison provide evidence that these basis functions for K_max = 20 are valid across the entirety of Antarctica. Subsequently, the analysis proceeds using LGD data obtained from the Level 1B product of GFO by transforming these LGD data into the SCHA coordinate system and applying them to constrain the SCHA harmonic coefficients up to K_max = 20. In this case, several independent LGD profiles along the trajectories of the satellites are devised to verify the accuracy of the local model. These LGD profiles are not employed in the inverse problem of determining harmonic coefficients. The results indicate that using regional harmonic basis functions, specifically spherical cap harmonic analysis (SCHA) functions, leads to a close estimation of LGD compared to the L2 JPL. The regional harmonic basis function exhibits a root mean square error (RMSE) of 3.71 × 10⁻⁴ mGal. This represents a substantial improvement over the RMSE of the L2 JPL, which is 6.36 × 10⁻⁴ mGal. Thus, it can be concluded that the use of local geo-potential coefficients obtained from SCHA is a reliable method for extracting nearly the full gravitational signal within a spherical cap region, after validation of this method. The SCHA model provides significant realistic information as it addresses the mass gain and loss across various regions in Antarctica. Full article

Among the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on temporal gravity products, the north–south stripe noise in the spherical harmonic coefficient (SHC) products contaminates the inversion of the Earth’s mass field. In this study, GRACE SHC products are adopted to estimate the mass changes in Nicaragua. To improve this estimation, we propose an empirical low-pass filter to suppress stripe noise. After only using our filter, the Nicaragua regional uncertainty diminishes from 123.26 mm to 69.11 mm, and the mean signal-to-noise ratio of all available months (2002–2021) improves from 1.67 to 1.8. Subsequently, our filter is employed to estimate the basin terrestrial water storage (TWS) change in Nicaragua. In the end, TWS change estimations are compared with various observations such as mascon products, hydrological models, and in situ groundwater observation. The main conclusions are as follows: (1) After using the wavelet coherent analysis, there is a negative resonance between TWS and the climate factor (El Nino–Southern Oscillation) with a period of 2~4 years; (2) The significant ~3.8-year periodic signal in groundwater storage change estimation is contributed by GRACE aliasing error. Our work can provide new knowledge and references for mass change in small areas. Full article

This study compares the Gravity Recovery And Climate Experiment (GRACE)/GRACE Follow-On (GFO) errors with the coseismic gravity variations generated by earthquakes above Mw8.0s that occurred during April 2002~June 2017 and evaluates the influence of monthly model errors on the coseismic signal detection. The results show that the precision of GFO monthly models is approximately 38% higher than that of the GRACE monthly model and all the detected earthquakes have signal-to-noise ratio (SNR) larger than 1.8. The study concludes that the precision of the time-variable gravity fields should be improved by at least one order in order to detect all the coseismic gravity signals of earthquakes with M ≥ 8.0. By comparing the spectral intensity distribution of the GFO stack errors and the 2019 Mw8.0 Peru earthquake, it is found that the precision of the current GFO monthly model meets the requirement to detect the coseismic signal of the earthquake. However, due to the limited time length of the observations and the interference of the hydrological signal, the coseismic signals are, in practice, difficult to extract currently. Full article

Surface mass variations inferred from the Global Positioning System (GPS), and observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) complement each other in terms of spatial and temporal coverage. This paper presents an analysis of regional surface mass variations inverted from GPS vertical displacements under different density distributions of GPS stations, and compares the GPS-derived mass variations with GRACE/GFO inversion results in spatial and temporal domains. To this end, GPS vertical displacement data from a total of 85 permanent GPS stations of the Crustal Movement Observation Network of China (CMONOC), the latest GRACE/GFO RL06 spherical harmonic (SH) solutions and GRACE RL06 mascon solutions are used to investigate surface mass variations in four regions or basins, including the Yunnan Province (YNP), Min River Basin (MRB), Jialing River Basin (JLRB), and Wu River Basin (WRB) in Southwest China. Our results showed that the spatial distributions and seasonal characteristics of GPS-derived mass change time series agree well with those from GRACE/GFO observations, especially in regions with relatively dense distributions of GPS stations (e.g., in the YNP and MRB), but there are still obvious discrepancies between the GPS and GRACE/GFO results. Scale factor methods (both basin-scaled and pixel-scaled) were employed to reduce the amplitude discrepancies between GPS and GRACE/GFO results. The results also showed that the one-year gap between the GRACE and GFO missions can be bridged by scaled GPS-derived mass change time series in the four studied regions, especially in the YNP and MRB regions (with relatively dense distributions of GPS stations). Full article