Optimizing GRACE/GRACE-FO data and a priori hydrological knowledge for improved global terrestial water storage component estimates

Abstract. Accurate estimation of terrestrial water storage (TWS) at a high spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5∘ by the resolution of soil and vegetation data sets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5 to 0.05∘) and time span (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05∘ developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. Using improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated on in this paper relies only on publicly accessible global data sets, allowing the reproduction of the 0.05∘ TWS estimates in any study region.

Improving drought simulations within the Murray-Darling Basin by combined calibration/assimilation of GRACE data into the WaterGAP Global Hydrology Model

Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry are suitable for the precise measurement of terrestrial water storage (TWS) and lake water level variations from space. In this study, two GRACE solutions, namely, spherical harmonics (SH) and mascon (MSC), are utilized with the Global Land Data Assimilation System (GLDAS) model to estimate the spatial and temporal variations of TWS in the Upper Indus Basin (UIB) for the study period of January 2003 to December 2016. The TWS estimated by SH, MSC, and the GLDAS model are consistent and generally show negative trends of -4.47 ± 0.38 mm/year, -4.81 ± 0.49 mm/year, and -3.77 ± 0.46 mm/year, respectively. Moreover, we use the GLDAS model data to understand the roles of variations in land surface state variables (snow water equivalent (SWE), soil moisture, and canopy water storage) in enhancing or dissipating the TWS in the region. Results indicate that SWE, which has a significant contribution to GRACE TWS variability, is an important parameter. Spearman's rank correlations are calculated to demonstrate the relationship of the GLDAS land surface state variables and the GRACE signals. A highly positive correlation between SWE with TWS is estimated by SH and MSC as 0.691 and 0.649, respectively, indicating that the TWS signal is mainly reliant on snow water in the study region. The ground water storages estimated by SH and MSC solutions are nearly stable with slight increasing trends of 0.63 ± 0.48 mm/year and 0.29 ± 0.51 mm/year, respectively. We also take advantage of the potential of satellite altimetry in measuring lake water level variations, and our result indicates that Cryosat-2 SARin mode altimetry data can be used in estimating small water bodies accurately in the high mountainous region of the UIB. Moreover, the climate indices data of El-Niño Southern Oscillation and Pacific Decadal Oscillation are analyzed to determine the influence of pacific climatic variability on TWS.

<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>

Abstract. The US–German GRACE (Gravity Recovery and Climate Experiment, 2002–2017) and GRACE-FO (GRACE Follow-On, since 2018) satellite missions observe terrestrial water storage (TWS) variations. Over 20 years of data allow for investigating interannual variations beyond linear trends and seasonal signals. However, the origin of observed TWS changes cannot be determined solely with GRACE and GRACE-FO observations. This study focuses on the northern part of the East African Rift around the lakes of Turkana, Victoria, and Tanganyika. It aims to characterise and analyse the interannual TWS variations compared to meteorological and geodetic observations of the water storage compartments (surface water, soil moisture, and groundwater). We apply the STL (Seasonal-Trend decomposition using LOESS) method to decompose the signal into a seasonal signal, an interannual signal, and residuals. By clustering the interannual TWS dynamics for the African continent, we define the exact outline of the study region. We observe a TWS decrease until 2006, followed by a steady rise until 2016, and then the most significant TWS gain in Africa in 2019 and 2020. Besides meteorological variability, surface water storage variations in the lakes explain large parts of the TWS decrease before 2006. The storage dynamics of Lake Victoria alone contribute up to 50 % of these TWS changes. On the other hand, the significant TWS increase around 2020 can be attributed to nearly equal rises in groundwater and surface water storage, which coincide with a substantial precipitation surplus. Soil moisture explains most of the seasonal variability but does not influence the interannual variations. As Lake Victoria dominates the surface water storage variations in the region, we further investigate the lake and the downstream Nile River. The Nalubaale Dam regulates Lake Victoria's outflow. Water level observations from satellite altimetry reveal the impact of dam operations on downstream discharge and on TWS decreases in the drought years before 2006. On the other hand, we do not find evidence for an impact of the Nalubaale Dam regulations on the strong TWS increase after 2019.

Characterization of spatio-temporal patterns for various GRACE- and GLDAS-born estimates for changes of global terrestrial water storage

Terrestrial Water Storage (TWS) data, now available for more than 15 years, as furnished by the Gravity Recovery and Climate Experiment (GRACE) satellite mission provided remarkable insights into the hydrological cycle. An expanding volume of scientific literature bears testimony to this. Besides identifying the alarming rate of both global groundwater storage depletion and polar ice cap melt, which are considered as its major accomplishments, the GRACE dataset also provided an exceptional resource for the improvement of the hydrological models. Such studies, although large in number, can be broadly classified into two categories: (1) Model evaluation studies, comparing regional or global model outputs with GRACE-derived hydrological parameters and (2) data assimilation techniques, where GRACE data is incorporated into the modeling framework. Model evaluation techniques vary in terms of the choice of hydrologic parameter, which in most cases is TWS or groundwater. On the other hand, data assimilation strategies in most of the studies use ensemble Kalman filter or its variant to incorporate the information derived from GRACE data into the models. This chapter reviews the integration methodologies highlighting the different aspects of model improvement such as the hydrologic parameter of interest, complexity of the model framework, and representation of hydrological processes. However, the present discussion is restricted to conceptual hydrological models and do not include statistical or GRACE-based water balance models.

Improved methods for estimating local terrestrial water dynamics from GRACE in the Northern High Plains

Understanding the shift in drivers of terrestrial water storage decline in the central Inner Mongolian steppe over the past two decades

Recent studies show that data from the Gravity Recovery and Climate Experiment (GRACE) is promising for basin‐ to global‐scale water cycle research. This study provides varied assessments of errors associated with GRACE water storage estimates. Thirteen monthly GRACE gravity solutions from August 2002 to December 2004 are examined, along with synthesized GRACE gravity fields for the same period that incorporate simulated errors. The synthetic GRACE fields are calculated using numerical climate models and GRACE internal error estimates. We consider the influence of measurement noise, spatial leakage error, and atmospheric and ocean dealiasing (AOD) model error as the major contributors to the error budget. Leakage error arises from the limited range of GRACE spherical harmonics not corrupted by noise. AOD model error is due to imperfect correction for atmosphere and ocean mass redistribution applied during GRACE processing. Four methods of forming water storage estimates from GRACE spherical harmonics (four different basin filters) are applied to both GRACE and synthetic data. Two basin filters use Gaussian smoothing, and the other two are dynamic basin filters which use knowledge of geographical locations where water storage variations are expected. Global maps of measurement noise, leakage error, and AOD model errors are estimated for each basin filter. Dynamic basin filters yield the smallest errors and highest signal‐to‐noise ratio. Within 12 selected basins, GRACE and synthetic data show similar amplitudes of water storage change. Using 53 river basins, covering most of Earth's land surface excluding Antarctica and Greenland, we document how error changes with basin size, latitude, and shape. Leakage error is most affected by basin size and latitude, and AOD model error is most dependent on basin latitude.

The water‐receiving area of the South‐to‐North Water Diversion Eastern Route Project (SNWDP‐ER) is one of the most severely affected water‐shortage areas in China, and no previous study has been conducted on the changes in water storage in this area. In this study, we combined the latest Gravity Recovery and Climate Experiment (GRACE) And GRACE Follow‐On products with global models for the first time to analyze changes in water storages in this area from 2002 to 2020, and to investigate the effects of climate change and human activity on changes in water storage. We found that SNWDP‐ER aided the recovery of surface water (nongroundwater) with a recovery rate of 9.44 ± 1.65 mm/yr after its implementation, but had little effect on the recovery of groundwater and terrestrial water storage in the water‐receiving area. Before the SNWDP‐ER was implemented, the rates of decrease of groundwater and terrestrial water storage were only −1.59 ± 0.58 and −5.18 ± 0.75 mm/yr, respectively. After implementation, the rates of decrease of groundwater and terrestrial water storage were −17.7 ± 1.27 and −8.16 ± 1.18 mm/yr, respectively. Groundwater decline, accelerated by human activity and climate change, has led to an accelerated decline in terrestrial water storage. Effects of SNWDP‐ER and stringent policies reducing groundwater use, along with largely increased precipitation in North China on groundwater storage after year 2020 need to be examined in the future. Our results have important implications for the management and evaluation of SNWDP‐ER.

Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we develop and apply deep convolutional neural network (CNN) models to learn the spatiotemporal patterns of mismatch between TWS anomalies (TWSA) derived from GRACE and those simulated by NOAH, a widely used land surface model. Once trained, our CNN models can be used to correct the NOAH‐simulated TWSA without requiring GRACE data, potentially filling the data gap between GRACE and its follow‐on mission, GRACE‐FO. Our methodology is demonstrated over India, which has experienced significant groundwater depletion in recent decades that is nevertheless not being captured by the NOAH model. Results show that the CNN models significantly improve the match with GRACE TWSA, achieving a country‐average correlation coefficient of 0.94 and Nash‐Sutcliff efficient of 0.87, or 14% and 52% improvement, respectively, over the original NOAH TWSA. At the local scale, the learned mismatch pattern correlates well with the observed in situ groundwater storage anomaly data for most parts of India, suggesting that deep learning models effectively compensate for the missing groundwater component in NOAH for this study region.

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

With time series ranging over more than twenty years, terrestrial water storage (TWS) variations observed by the Gravity Recovery and Climate Experiment (GRACE, 2002-2017) and GRACE-Follow-On (GRACE-FO, since 2018) missions are providing unique insights into hydrological dynamics, on a global scale. TWS encompasses changes in all water storage compartments, from soil moisture, surface water storage, snow and ice, to groundwater. GFZ operationally provides monthly TWS grids via the GravIS portal (Gravity Information Service, gravis.gfz.de).Within the G3P project, GFZ recently released a global gravity-based product that includes both TWS variations and also groundwater storage (GWS) variations. GWS is calculated by subtracting the aggregated and filtered storage contributions of the other water storage components, from GRACE/-FO TWS. A challenge for both TWS and GWS is the separation of long-term trends (e.g., resulting from regional human activities and climate change) from stochastic variations as attributable to natural climate variability ("climate noise").To address this challenge, we introduce an unsupervised trend analysis framework that uses power-law noise models to account for long-range memory in the hydrological time series under investigation. This approach requires minimal assumptions about the underlying processes and provides a robust method for separating long-term trends from stochastic variability. By addressing the limitations of existing methods, such as underestimated uncertainties and simplified noise representations, our framework allows for accurate quantification of trend magnitudes and of their significance. Firstly, this is confirmed for TWS, by comparing reported trends to our detected trends. Concerning the GWS product, we observe that anthropogenic depletion of groundwater is a primary driver of freshwater decline. Furthermore, we reveal previously unobserved trends, including increasing groundwater levels in parts of Africa and declining trends in central and eastern Europe. We also demonstrate how the presented method identifies potential false-positive trends, which enhances the reliability of trend detection. This scalable approach for trend analyses is currently extended to integrate uncertainties that arise from measurement system uncertainties, enhancing its applicability to other essential climate variables.

Assessing the interannual and subseasonal variabilities in water storage using multi-source soil moisture products and GRACE/GRACE-FO satellites and its applications