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.

Due to the inconsistent datum systems between the Global Navigation Satellite System (GNSS) and total station measurement technologies, the processing results of mixed network data combining GNSS baseline data and terrestrial total station observations are inevitably affected by the Deflection of the Vertical (DOV). To reduce the adverse impact of DOV on the accuracy of three-dimensional combined adjustment results, this paper quantitatively evaluates the DOV calculation accuracy of five Earth gravity field models based on the measured DOV data from the GSVS2017 (Geoid Slope Validation Survey) project. Meanwhile, through comprehensive comparative analysis of two existing DOV correction strategies, this paper proposes another method that adopts DOV values calculated by gravity field models as prior constraints to participate in combined adjustment computation. Finally, comparative experiments are designed using measured mixed control network data to verify the effectiveness of different DOV correction strategies in improving the accuracy of combined adjustment results. The main conclusions are drawn as follows: (1) High-degree global Earth gravity field models present high consistency in DOV calculation accuracy, with the Root Mean Square (RMS) better than 2 arcseconds; (2) The weighting method based on Variance Component Estimation (VCE) can effectively improve the internal fitting accuracy of adjustment results; (3) All three DOV correction strategies can enhance the rigor of the adjustment model and optimize the overall adjustment accuracy. Considering both internal and external coincidence accuracy, the scheme that takes DOV components as unknown parameters and incorporates them into the adjustment model achieves the optimal performance.

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.

The time-variable gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) mission are generally contaminated by the correlation errors, specifically parameter correlation (strong parameter coupling) and observation noise correlation (colored instead of white noise). In this context, we propose a decorrelation approach to pursue an improved time-variable gravity solution following a step-wise processing. The step-wise decorrelation approach comprises three steps, standard, parameter decorrelation, and observation noise decorrelation processes. First, the standard process serves to establish a reliable signal reference for the following. Then, the parameter decorrelation is implemented through the separate estimation of orbit and gravity field parameters. Finally, to achieve the goal of observation noise decorrelation, the post-fit residuals obtained from the result of parameter decorrelation are used to estimate a colored noise model, which is considered to determine the final gravity field model, specifically termed the step-wise decorrelation solution. The basic idea is that under the regularization constraints of separate estimation for dynamic parameters, the reduced dynamic parameter space allows certain low-frequency perturbative errors to emerge in post-fit residuals, enabling comprehensive characterization of observation noise correlation. Using this step-wise decorrelation approach, we process monthly GRACE-FO gravity field time series and evaluate the performance of them from the aspects of signal and noise. Spectral and spatial domain analyses of noise levels confirm significant noise suppression in the final solution. For instance, it achieves 66

Abstract Gravity Recovery and Climate Experiment (GRACE) observation data processed by various institutions yields somewhat different spherical harmonic solutions, which are further used to derive terrestrial water storage (TWS) changes. Combining TWS solutions from different institutions helps to refine the effective signal while removing noise. This study investigates regularization constraints in the context of TWS fusion to enhance the resulting estimates. The considered constraints are Tikhonov regularization of different orders, as well as minimization of month‐to‐month year‐to‐year double differences (MYDD), and triple differences (MYTD). Different accuracy and signal evaluation approaches are implemented for both individual and combined solutions. Compared to individual solutions and unregularized combinations, the regularized TWS combined solutions demonstrate lower noise levels. Among them, the second‐order Tikhonov regularization performs slightly better than other constraints, providing lower noise levels. This study offers a novel perspective for exploring GRACE‐based TWS combination methodologies.

The Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) satellites carry a novel technology demonstration instrument, the laser ranging interferometer (LRI), parallel to the microwave interferometer (MWI) for inter satellite ranging. As critical geometric observations for capturing the Earth gravity field variation, the quality of monthly gravity field solutions and the characterization of residual sub-monthly signals are significantly influenced by the performance of the LRI Level-1B (LRI1B) data products. Currently, multiple versions of LRI1B products are independently processed and released by the Albert Einstein Institute (AEI), the Huazhong University of Science and Technology (HUST), the Jet Propulsion Laboratory (JPL), and the Sun Yat-sen University (SYSU). Here, we examine the performance of multiple LRI1B datasets on GRACE-FO time-variable gravity field recovery through data cross comparison, monthly LRI-based gravity solutions, and post-fit residuals from January 2019 to June 2023. All LRI1B data products maintain high data availability of ∼85%. Direct cross comparisons reveal that the JPL v04 product presents systematically higher noise within the high frequency band exceeding 0.1 Hz. As for monthly gravity field solutions, spectral and geospatial analyses demonstrate that all four gravity solutions are consistent with GRACE-FO Science Data System (SDS) products, achieving comparable noise levels with an average open ocean root mean square of 3.06 cm and signal recovery correlations of ∼0.99 for annual mass variations. However, significant divergences are identified in the post-fit range rate and range acceleration residuals. With the low frequency component excluded, the JPL solution exhibits a noise floor of 4 × 10−10 m/s2 for range acceleration residuals, whereas the AEI, HUST, and SYSU solutions maintain noise levels below 2 × 10−10 m/s2. While the high frequency noise does not degrade monthly solutions, it presents a potential limitation for the extraction of subtle sub-monthly geophysical signals. These findings provide a scientific basis for data product selection and laser data processing strategies for the future mission.

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.

The Gravity Recovery and Climate Experiment (GRACE) mission provides original observations to different institutions for the production of various monthly time-variable gravity field models (TVGFMs). Aiming to optimize the signal-to-noise ratio of TVGFMs, we determine to combine them in the spatial domain. This combination comprises two approaches: one combines spherical harmonic coefficient (SHC) solutions, and the other combines SHC and mascon (mass concentration) solutions together. For each approach, we employ four old weighting schemes and one new scheme named oceanic accuracy weighting. We then assessed the combined models through both internal and external validations. In external validation, we compare the Caspian Sea level changes derived from the combined models with those obtained from satellite altimeter. Our results reveal an improvement of the SHC combination using variance component estimation (VCE) weighting compared to any single SHC solution. Moreover, we found enhanced performance in the combined models with the incorporation of mascon solutions, particularly employing the oceanic accuracy weighting. These findings underscore the efficacy of model combination in improving performance, and emphasize the importance of selecting appropriate weighting strategies for integrating GRACE solutions. Specifically, we recommend VCE weighting scheme for combining SHC solutions only and oceanic accuracy weighting scheme for integrating mascon and SHC solutions.

Understanding hydrological drought is vital for effective disaster mitigation and management. However, challenges like sparse hydrological monitoring stations and data acquisition present significant obstacles for studying hydrological drought in specific regions. This study developed a Global Navigation Satellite System (GNSS) -based runoff index to assess the hydrological drought in Sichuan. Regional equivalent water height (EWH) was estimated using the vertical displacement of continuous GNSS stations based on Green's function method. Combining this with high-precision precipitation and evapotranspiration data, the runoff in Sichuan using the water balance equation, resulting in the standardized runoff index (SRI-GNSS) was calculated. The findings show that Sichuan's hydrological drought duration varies from one to nine months, and SRI-GNSS indicates a trend towards hydrological wetness. The drought's intensity remained stable at around 1.15, but its severity accumulated over longer periods. The transit from meteorological to hydrological drought took approximately two months, with a 48.4% propagation probability. Influences like the El Ni & ntilde;o-Southern Oscillation, Pacific Decadal Oscillation and North Atlantic Oscillation significantly influenced SRI-GNSS variability during 2016-2018. This study addresses gaps in hydrological drought assessments based on runoff in Sichuan Province and highlights the potential of GNSS technology for advancing hydrological research in regions with limited runoff data.

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.

We develop a joint inversion method in the spectral domain that accounts for different signal characteristics in Global Navigation Satellite System (GNSS) and Gravity Recovery and Climate Experiment (GRACE) observations for quantifying terrestrial water storage (TWS) changes in the Yangtze River Basin (YRB). The method seamlessly integrates these geodetic datasets with distinct spatial scales and coverages. We exploit the Slepian basis functions, being spectrally band-limited and spatially concentrated, to put these datasets together in the regional TWS modeling. Our integrated geodetic results reveal variable spatiotemporal patterns of water storage changes within the YRB, hydrological extremes, and their linkage to interannual climate variability. The results indicate that annual precipitation in the middle and lower YRB is twice of that in the Jinsha River Basin (the uppermost basin of YRB), yet the TWS change is less than a half. This large discrepancy in the ratio of water storage and precipitation can be attributed to substantial runoff in the middle and lower YRB. The joint geodetic inversion identifies extreme droughts and flood events in the basin, consistent with the assessments from precipitation anomalies and Global Flood Awareness System. We also find that interannual TWS variations in the Jinsha River Basin are modulated by Indian Ocean Dipole, while those in the upper YRB and middle and lower YRB are modulated by El Nino-Southern Oscillation. Our findings highlight the potential use of geodetic data combination to advance hydrological variabilities in the region and inform water resource management strategies in response to climate change.

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.

The Nile River Basin (NRB) has experienced a notable rise in drought episodes in recent decades. The propagation of meteorological, agricultural, and groundwater drought dynamics in the NRB was investigated in this study. The following drought indices examined the correlation and propagation among meteorological, agricultural, and groundwater droughts. These are the standardized precipitation evapotranspiration index (SPEI), soil moisture index, Gravity Recovery and Climate Experiment, and GRACE Follow-On (GRACE/GRACE-FO)-derived groundwater drought index (GGDI). These droughts were comprehensively evaluated in the NRB from 2003 to 2022. The cross-wavelet transform approach highlighted the links between droughts. The following are the key findings: (1) In the NRB, the cross-wavelet energy spectrum of wavelet coherence can indicate the internal connection between meteorological versus (vs.) agricultural and agricultural versus (vs.) groundwater drought. The time scale with the most significant correlation coefficient is the drought propagation time. (2) The El Niño–Southern Oscillation (ENSO) correlated with agricultural and groundwater drought much more than the Indian Ocean Dipole (IOD), demonstrating that ENSO has an important impact on drought advancement. (3) The R2 values were 0.68 for GGDI vs. standardized soil moisture index (SSI), 0.71 for Blue Nile Region (BNR) GGDI vs. SSI, and 0.55 for SSI vs. Standardized Precipitation Evapotranspiration Index (SPEI). Similarly, in the Lake Victoria Region (LVR), GGDI vs. SSI was 0.51 and SSI vs. SPEI was 0.55, but in the Bahr-el-Ghazal Region (BER), GGDI vs. SSI was 0.61 and SSI vs. SPEI was 0.27 during the whole research period with varied lag durations ranging from 1 to 6 months. Thus, the propagation of drought (i.e., meteorological, agricultural, and groundwater drought) dynamics has the potential to reshape our understanding of drought evolution, which could lead to early drought forecasting across the NRB and similar climatic regions.

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.

The prediction of high-speed railway bridge pier settlement is important for the safety of high-speed railway engineering. At present, a common method in settlement prediction is the curve fitting model in single prediction models. However, it may be difficult to describe the settlement rule of high-speed railway bridge piers using a curve fitting model with limited observation data during time-constrained construction periods. Moreover, relying on only a single prediction model usually does not allow for full exploration of the potential information in the data and poses the problem of poor stability and applicability. To solve this issue, a combined prediction model that uses the optimal nonnegative variable weight combination based on robust weighted total least-squares autoregression (RWTLS-AR) and adaptive dynamic cubic exponential smoothing (ADCES) is proposed to combine the advantages of two single prediction models. The RWTLS-AR model, using a robust weighted total least-squares method, has high prediction accuracy in the case of fewer observation data. At the same time, the adaptive dynamic judgment mechanism is established using the ADCES model to improve stability. The proposed model is applied to the settlement prediction of high-speed railway bridge pier, and three sets of observation data are used for evaluation. A comparison is made with two single prediction models and three other combined prediction models. The results show that the mean absolute error, root-mean-square error, and mean absolute percentage error of the proposed model are respectively 0.092 mm, 0.101, mm and 5.936% in the first set of observations, 0.099 mm, 0.118 mm, and 6.592% in the second set of observations, and 0.177 mm, 0.203 mm, and 15.914% in the third set of observations. This indicates that the proposed model is more accurate and stable than all the aforementioned prediction models.

The Paraná basin is the second largest river basin in South America and provides abundant water resources globally. However, current research lacks hydrological investigation of the region. The vertical crustal deformation recorded by the Global Navigation Satellite System (GNSS) can be used to accurately estimate regional-scale terrestrial water storage (TWS). Therefore, we utilized the daily vertical displacement time series data at 102 GNSS stations to recover the water storage variations in the Paraná basin from 2013 to 2020. To recognize primary spatiotemporal features of TWS changes, we applied the principal component analysis (PCA) method in the inversion strategy. Results indicate that the TWS variations inferred from GNSS generally align in spatiotemporal patterns with estimates from both the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS). However, some discrepancies are evident at local scales. The TWS changes derived from both GNSS and GRACE exhibited generally larger magnitude of oscillations than those estimated by GLDAS, while the GRACE results neglected the evident seasonal oscillation of the water mass in the southeast of the basin. Given the challenge of capturing large-scale runoff variations through in-situ observations, we innovatively applied GNSS and water budget closure method to provide a novel runoff estimate for the Paraná basin. The GNSS-inferred runoff exhibited a strong correlation (correlation coefficient of 0.72) with in-situ observations. Overall, our study fills the critical knowledge gap in geodesy-based hydrological investigation in the Paraná basin. We aim to highlight the immense potential of GNSS for hydrological parameter estimation and provide valuable reference data for regional hydrological research and for water resources management.

The Gravity Recovery and Climate Experiment (GRACE) and its Follow‐On (GRACE‐FO) missions have revolutionized global terrestrial water storage anomalies (TWSA) measurements. However, the 11‐month data gap between the two GRACE missions disrupts the measurement continuity and limits its further applications. Previous attempts to fill this data gap require further improvement in terms of method robustness and product quality. Here, we propose a novel two‐step linear model using precipitation, temperature data, and hydrological model‐simulated TWSA as predictors to fill the 11‐month data gap between the two GRACE missions and generate six global gridded GRACE‐like TWSA products from April 2002 to July 2021. These products are evaluated at grid scale globally and also basin scale for the world's largest 72 river basins. Results indicate that our GRACE‐like data show great consistency with the GRACE/GRACE‐FO observations. While most basins exhibit consistent performance across the six GRACE‐like TWSA products, certain areas with lower signal‐to‐noise ratios show significant variability. Furthermore, we assess the performance of our GRACE‐like data during the data gap using one previous reconstruction, a hydrological model simulation, and the Swarm satellite measurement. The results confirm that our GRACE‐like data exhibit equivalent performance within and outside the data gap. This study introduces a more simple and robust method for predicting the missing data between the two GRACE missions and provides readily applicable continuous GRACE‐like TWSA products for hydrologic applications.