Monitoring terrestrial water storage changes using GNSS vertical coordinate time series in Amazon River basin

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has different advantages compared with traditional methods; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite's all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology.

SUMMARYSatellite geodetic technologies, such as the Global Navigation Satellite System (GNSS), Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO), have complementary advantages in inferring terrestrial water storage (TWS) changes at regional and basin scales. We introduced a joint inversion strategy to infer TWS changes using GNSS- and GRACE/GFO-derived vertical displacements based on Green's function theory in the Yangtze River Basin (YRB) from January 2011 to December 2020. Additionally, we investigated the performance of variance component estimation (VCE) and Akaike's Bayesian Information Criterion (ABIC) to determine the optimal relative weights of different observation data. The performance of our joint inversion strategy was verified through a closed-loop simulation and multi-source hydrometeorological data [i.e. the time derivative of TWS changes (${\rm d}S/{\rm d}t$) from precipitation (P), evapotranspiration (ET) and run-off (R) based on the water balance equation, called P-ET-R]. The closed-loop simulation shows that the TWS changes from joint inversion have better consistencies with the synthetic signals than those of GNSS- and GRACE-only estimates, and the corresponding root mean square error (RMSE) decreased 1.43−6.28 mm and correlation coefficient (CC) increased 3−10 per cent. The ABIC was more suitable for the joint inversion of measured GRACE/GFO and GNSS data for TWS changes in the YRB. Analysis from the measured data shows that the spatial patterns and seasonal characteristics in TWS changes derived from GNSS, GRACE/GFO and their joint inversion are in good agreement in the YRB. The contribution of GNSS observations to the joint inversion in the upstream of the YRB is greater than that of GRACE/GFO due to the relatively densely distributed GNSS stations, but the opposite is true in the downstream. Furthermore, the joint inversion results have better agreements with P and P-ET-R compared to GNSS- and GRACE/GFO-only estimates in the upstream, and the corresponding CCs increased 5−7 per cent (for P) and 2−5 per cent (for P-ET-R), respectively, which further demonstrates the effectiveness of our joint inversion strategy. Our estimation strategy provides a new insight for joint inversion of GNSS and GRACE/GFO data to obtain more reliable TWS changes.

Satellite observations from the Gravity Recovery and Climate Experiment (GRACE) provide unique measurements of global terrestrial water storage (TWS) changes at different spatial and temporal scales. Large-scale ocean–atmosphere interactions might have significant impacts on the global hydrological cycle, resulting in considerable influences on TWS changes. Quantifying the contributions of large-scale ocean–atmosphere interactions to TWS changes would be beneficial to improving our understanding of water storage responses to climate variability. In the study, we investigate the impact of three major global ocean–atmosphere interactions—El Niño and Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Atlantic Meridional Mode (AMM) on interannual TWS changes in the tropics and subtropics, using GRACE measurements and climate indices. Based on the least square principle, these climate indices, and the corresponding Hilbert transformations along with a linear trend, annual and semi-annual terms are fitted to the TWS time series on global 1° × 1° grids. By the fitted results, we analyze the connections between interannual TWS changes and ENSO, IOD, and AMM indices, and estimate the quantitative contributions of these climate phenomena to TWS changes. The results indicate that interannual TWS changes in the tropics and subtropics are related to ENSO, IOD, and AMM climate phenomena. The contribution of each climate phenomenon to TWS changes might vary in different regions, but in most parts of the tropics and subtropics, the ENSO contribution to TWS changes is found to be more dominant than those from IOD and AMM.

<p>Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) provide time-variable Earth's gravity fields that contain signals related to different processes such as non-steric sea level changes, Terrestrial (surface and sub-surface) Water Storage Changes (TWSC), ice sheet melting, and Post Glacial Rebound (PGR). Although the GRACE(-FO) data represent an accurate superposition of these anomalies, separating them into individual storage and surface deformation contributors is desirable for many geodynamic and hydro-climatic applications. Particularly, for hydrological applications, the PGR is often removed as a linear trend, during the post-processing, using the output of Glacial Isostatic Adjustment (GIA) models. As a result, estimating trends in TWSC depends on the accuracy of GIA models, which has considerable uncertainties. In this study, a hierarchical constrained Bayesian (ConBay) approach is formulated to apply GRACE(-FO) fields and the uplift rate measurements from the Global Navigation Satellite System (GNSS) stations to simultaneously estimate the contribution of TWSC and PGR. The proposed approach is formulated based on a hierarchical Markov Chain Monte Carlo optimization algorithm to update available information within a dynamic multivariate state-space model, while accounting for the uncertainties of models and observations. To evaluate the proposed approach, its numerical implementation is demonstrated over the Great Lakes area in North America on grids with 0.5-degree spatial resolution, covering 2003-2017, where the W3RA water balance model and the ICE-5G(VM2) GIA model are used as a <em>priori</em> information of individual water storage changes and PGR rates. Validations are done against independent measurements, i.e., in-situ USGS groundwater level observations, as well as independent GNSS measurements (not used in the optimization procedure). The results indicate that the bias between GIA model output and the in-situ GNSS observation reduced by 72% (from 7.8 to 2.2 mm/yr) and the root-mean-square-of-differences between USGS measurements and model-derived groundwater changes is reduced by 36%, after merging observations with models through ConBay. The ConBay updates, introduced to the long-term trends, as well as the seasonal and inter-annual components, are found to be realistic.</p><p> </p><p>keywords: Bayesian Signal Separation; GRACE(-FO); GNSS; Terrestrial Water Storage Changes; Post Glacial Rebound; </p>

The monitoring of Poyang Lake water area and storage changes using remote sensing and satellite gravimetry techniques is valuable for maintaining regional water resource security and addressing the challenges of global climate change. In this study, remote sensing datasets from Landsat images (Landsat 5, 7, 8 and 9) and three Gravity Recovery and Climate Experiment (GRACE) and Gravity Follow-on (GRACE-FO) mascon solutions were jointly used to evaluate the water area and storage changes in response to global and regional climate changes. The results showed that seasonal characteristics existed in the terrestrial water storage (TWS) and water area changes of Poyang Lake, with nearly no significant long-term trend, for the period from April 2002 to December 2022. Poyang Lake exhibited the largest water area in June and July every year and then demonstrated a downward trend, with relatively smaller water areas in January and November, confirmed by the estimated TWS changes. For the flood (August 2010) and drought (September 2022) events, the water area changes are 3032 km2 and 813.18 km2, with those estimated TWS changes 17.37 cm and −17.46 cm, respectively. The maximum and minimum Poyang Lake area differences exceeded 2700 km2. The estimated terrestrial water storage changes in Poyang Lake derived from the three GRACE/GRACE-FO mascon solutions agreed well, with all correlation coefficients higher than 0.92. There was a significant positive correlation higher than 0.75 between the area and TWS changes derived from the two independent monitoring techniques. Therefore, it is reasonable to conclude that combined remote sensing with satellite gravimetric techniques can better interpret the response of Poyang Lake to climate change from the aspects of water area and TWS changes more efficiently.

The surface displacements measured by the Global Navigation Satellite System (GNSS) provide a unique insight for studying terrestrial water storage (TWS) changes. In this study, we recovered the TWS changes from GNSS vertical displacements in Southwest China (SWC) using Slepian basis function (SBF) from January 2011 to December 2020. The performance of the TWS changes estimated by SBF was validated against the Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow‐On (GFO) and the GNSS‐inverted TWS changes estimated by Green's function method. The results showed that the spatial patterns, seasonal, and linear trends of the TWS changes derived from GNSS using SBF agreed with the GRACE/GFO estimates. However, there are still evident differences in the local scope, and the GNSS‐derived TWS changes presented stronger amplitudes and more details in the spatio‐temporal domains than the GRACE/GFO estimates in SWC. The unconstrained GNSS inversion results using SBF also presented stronger signal amplitudes than those estimates with Green's function, and the TWS changes estimated by SBF were more reliable than Green's function method for regions with sparsely distributed GNSS stations in SWC. Additionally, the average distance between the GNSS stations can be considered as a reasonable filtering radius of SBF, and the SBF‐estimated TWS changes with different Gaussian filtering radii had comparable signal amplitudes and spatial patterns with the estimates of Green's function and GRACE/GFO.

Global Navigation Satellite System (GNSS) is an effective means to monitor surface deformations associated with changes in terrestrial water storage (TWS). In this study, we introduced a priori constraint matrix and estimated the regularization parameter through an iterative least-squares method to infer the TWS changes using GNSS vertical displacements from December 2010 to February 2021 in the Yunnan Province (YNP), China. The GNSS-inferred TWS changes were validated through the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) estimates, the Global Land Data Assimilation System (GLDAS) hydrological model, and the meteorological data (i.e., precipitation and Drought Severity Index (DSI)). The results demonstrate that the spatial resolution of TWS changes derived from 45 GNSS stations can reach 2°×2° in the YNP, and the a priori constraint is better than the traditional Laplacian matrix constraint for solving the discrete ill-conditioned problem of GNSS inversion. The GNSS-inferred TWS changes are consistent with the TWS changes derived from GRACE/GFO and GLDAS estimates in the spatio-temporal domains over the YNP, but the GNSS inversion results show stronger amplitudes. Furthermore, the ground-based GNSS observations are more sensitive to the TWS changes, and the correlation between precipitation and GNSS-inferred TWS changes is improved by about 11 % and 5 % compared to GRACE/GFO and GLDAS estimates in the YNP, respectively. The GNSS-inferred DSI can well reveal the two severe drought events in the YNP during the study period, which is consistent with the DSI derived from GRACE/GFO and GLDAS estimates, as well as the published self-calibrating Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). The GNSS observations can compensate the limitations of GRACE/GFO observations (e.g., bridging the data gap between GRACE and GFO missions), and help to better investigate the TWS changes in the YNP, which has significant potentials for regional water resource management and extreme climate changes monitoring.

Monitoring time-varying terrestrial water storage changes using daily GNSS measurements in Yunnan, southwest China

This study analyzed terrestrial water storage (TWS) changes across the Inner Niger Delta (IND) in Mali (West Africa) from April 2002 to September 2022 using Gravity Recovery and Climate Experiment (GRACE), GRACE-Follow-On (GRACE-FO), and Global Land Data Assimilation System (GLDAS) products. TWS changes exhibited strong seasonal patterns (−170 mm to 330 mm) with a high correlation between GRACE/GRACE-FO and GLDAS (r = 0.92, RMSE = 35 mm). The TWS trend was positive (7.3 to 9.5 mm/year). Maximum TWS changes occurred in September, while minimum values were observed between April and May. Wavelet analysis identified dominant seasonal cycles (8–16 months). Finally, we examined the climatic effects on TWS changes along the Niger River, from its source in the humid zones of Guinea to the semi-arid Sahelian zones of the IND in Mali. Precipitation (P) and potential evapotranspiration (PE) influence TWS changes only in the humid regions (r = 0.18–0.26, p-value < 10−2). Surface water bodies (SWB) exhibited a significant correlation with TWS in all regions, with r exceeding 0.50 in most cases. Groundwater changes, estimated from GRACE/GRACE-FO and GLDAS, showed strong agreement (r > 0.60, RMSE < 120 mm), with recharge rates increasing in semi-arid and Sahelian regions (r > 0.70, p-value < 10−3). This study highlights that precipitation, surface water bodies, and groundwater recharge appear as primary drivers of TWS in different regions: precipitation in the humid forest of Guinea, surface water bodies in the Southern and Northern Guinea Savanna along the Guinea–Mali border, and groundwater recharge in the semi-arid and IND Sahelian regions of central Mali.

Temporal and spatial variations of terrestrial water storage in the northeastern Tibetan Plateau retrieved by GNSS observations

<p>In this study, we use temporal solutions of the Gravity Recovery and Climate Experiment (GRACE) satellite mission to study the surface mass variations of hydrological origin in North America. The most recent release (RL06) of GRACE Level 2 data from three processing centers (CSR, JPL, GFZ) and mascon products are used in a combination scheme to produce estimates of terrestrial water storage (TWS) changes for the period 2002–2016. The land hydrology signal is isolated from GRACE data by removing the contribution of two major non-hydrologic processes, i.e., the glacial isostatic adjustment (GIA) and the ice mass melting from the glaciated areas of Alaska, Greenland and the Canadian Arctic.</p><p>The examination of long-term TWS trends revealed strong signatures of the 2011–2015 droughts in California and Texas, as well as accumulation of TWS in the central part of North America. Negative long-term TWS trends associated with ice melting were found around the Hudson Bay region. The TWS changes are dominated by a strong annual and semi-annual signal with higher magnitude in Alaska and along the west coast of North America.</p><p>An additional study on the estimation of groundwater storage (GWS) changes is performed using the Global Land Data Assimilation System (GLDAS) model. The GLDAS data are pre-filtered using the same strategy as GRACE data to ensure spectral consistency between them. The general behavior of GWS agrees well with the TWS, especially in terms of positive long-term GWS trends in central North America and strong annual signal in Alaska. Positive GWS trends are also identified in the east US coast.</p>

Investigation of 2020–2022 extreme floods and droughts in Sichuan Province of China based on joint inversion of GNSS and GRACE/GFO data

The GRACE twin satellite gravity mission from 2002 to 2017 has considerably improved investigations on global and regional hydrological changes. However, there are different GRACE solutions and products available which may yield different results for certain regions despite applying the same postprocessing and time span. This is especially the case for the Tibetan Plateau (TP) with its special hydrological conditions represented by localized but strong signals that can overlap or merge with signals inside the plateau, which can falsify the determination of terrestrial water storage (TWS) changes in the TP area. To investigate the effect of GRACE solution selection on inverted TWS changes, we analyze quantitatively the secular and monthly changes for 14 glacier areas and 10 water basins in and around the TP area that have been calculated from 16 different available GRACE solutions. Our analysis provides expectable results. While trend results from different spherical harmonic (SH) GRACE solutions match well, there are significant differences to and between mascon GRACE solutions. This is related to the different processing concepts of mascon solutions and their forced handling in our comparisons. SH solution time series match each other when mass changes are strong with a large amplitude and regular periodicity. However, for regions where small TWS changes are associated with small amplitudes, trends, and/or unstable signal periods, SH solutions can also yield different results. Such behavior is known from a time series analysis. Interestingly though, we find that the COST-G and ITSG SH GRACE solutions are closest to the average of all solutions. Therefore, these solutions appear to be preferable for TWS investigations in regions with highly variable hydrological conditions, such as in the Tibetan Plateau and its surroundings. This also indicates that combined solutions such as COST-G provide a promising pathway for an improved TWS analysis, which should be further elaborated.

Gravity Recovery and Climate Experiment (GRACE) satellites can effectively monitor terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse resolution of GRACE products, there is still a large number of deficiencies that need to be considered when investigating TWS changes in small-scale areas. Hence, it is necessary to downscale the GRACE products with a coarse resolution. First, in order to solve this problem, the present study employs modeling windows of different sizes (Window Size, WS) combined with multiple machine learning algorithms to develop a new machine learning spatial downscaling method (MLSDM) in the spatial dimension. Second, The MLSDM is used to improve the spatial resolution of GRACE observations from 0.5° to 0.25°, which is applied to Guantao County. The present study has verified the downscaling accuracy of the model developed through the combination of WS3, WS5, WS7, and WS9 and jointed with Random Forest (RF), Extra Tree Regressor (ETR), Adaptive Boosting Regressor (ABR), and Gradient Boosting Regressor (GBR) algorithms. The analysis shows that the accuracy of each combined model is improved after adding the residuals to the high-resolution downscaled results. In each modeling window, the accuracy of RF is better than that of ETR, ABR, and GBR. Additionally, compared to the changes in the TWS time series that are derived by the model before and after downscaling, the results indicate that the downscaling accuracy of WS5 is slightly more superior compared to that of WS3, WS7, and WS9. Third, the spatial resolution of the GRACE data was increased from 0.5° to 0.05° by integrating the WS5 and RF algorithm. The results are as follows: (1) The TWS (GWS) changes before and after downscaling are consistent, decreasing at −20.86 mm/yr and −21.79 mm/yr (−14.53 mm/yr and −15.46 mm/yr), respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (CC) values of both are above 0.99 (0.98). (2) The CC between the 80% deep groundwater well data and the downscaled GWS changes are above 0.70. Overall, the MLSDM can not only effectively improve the spatial resolution of GRACE products but also can preserve the spatial distribution of the original signal, which can provide a reference scheme for research focusing on the downscaling of GRACE products.

Terrestrial water storage changes over the Pearl River Basin from GRACE and connections with Pacific climate variability