Integration of GRACE Data for Improvement of Hydrological Models

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

<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>

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

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.

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.

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.

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.

Hydrological trends captured by assimilating GRACE total water storage data into the CLM5-BGC model

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

The Tibetan Plateau (TP), characterized by its unique regional features and geographical landscape, is a critical area for studying changes in terrestrial water storage (TWS), which are significantly influenced by global warming. In this study, we integrate data from the Global Navigation Satellite System (GNSS) and the Gravity Recovery and Climate Experiment (GRACE) to jointly estimate TWS variations in the TP and examine their spatiotemporal fluctuations in relation to large-scale climate patterns. To evaluate our approach, we conducted two synthetic tests, which showed that the root mean square errors (RMSEs) for the joint inversion were 23–37% lower than those for GNSS inversion, confirming the effectiveness of our method. When applied to real observational data, the joint inversion technique revealed that the spatial patterns and seasonal characteristics of TWS changes closely aligned with independent GRACE and Global Land Data Assimilation System (GLDAS) products, while offering more detailed insights into local hydrological processes. Notably, during the 2015/2016 El Niño event, the central and eastern TP experienced severe drought, primarily driven by precipitation anomalies (~ -150 mm) associated with extreme climate events, leading to a delayed hydrological response to the meteorological drought. Furthermore, we observed significant interannual variability across the TP sub-basins, with a moderate correlation with the El Niño/Southern Oscillation (ENSO) at a one-month lag. Our research highlights the potential of joint inversion using GNSS and GRACE to enhance TWS monitoring in the TP, providing more spatially detailed insights into water storage variability in response to climate change.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-12635-x.

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

Study regionThe Western Cape (WC), South Africa. Study focusThe WC has become increasingly dependent on groundwater in recent years due to repeated droughts. A framework to monitor the regional groundwater levels is urgently required to sustainably manage the WC’s water resources, since the region has inconsistent or unavailable monitoring data. Therefore, this study aims to understand how Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data can be used to monitor groundwater storage variations (ΔGWS) in the WC. In-situ ΔGWS time-series from twelve aquifers in the WC were compared to GRACE and GLDAS data. New hydrological insights for the regionGRACE terrestrial water storage anomalies (ΔTWS) showed moderate positive correlation (r = 0.69) with in-situ ΔGWS from the Adelaide Subgroup Aquifer (ASA), an unconfined aquifer with large areal extent and large ΔGWS. The Table Mountain Group Upper Aquifer Unit (TMG UAU) and Cape Flats Aquifer (CFA) also showed significant positive correlations with GLDAS ΔGWS of 0.83 and 0.73, respectively. Our results suggest that ΔGWS in the ASA can be monitored using GRACE ΔTWS, while GLDAS ΔGWS data can be used to monitor ΔGWS in the unconfined TMG UAU and CFA. GRACE and GLDAS data may be suitable to monitor groundwater availability in other water- and data-scarce regions of Africa.

After about 6 years of GRACE (Gravity Recovery and Climate Experiment) satellite mission operation, an unprecedented global data set on the spatio-temporal variations of the Earth’s water storage is available. The data allow for a better understanding of the water cycle at the global scale and for large river basins. This review summarizes the experiences that have been made when comparing GRACE data with simulation results of global hydrological models and it points out the prerequisites and perspectives for model improvements by combination with GRACE data. When evaluated qualitatively at the global scale, water storage variations on the continents from GRACE agreed reasonably well with model predictions in terms of their general seasonal dynamics and continental-scale spatial patterns. Differences in amplitudes and phases of water storage dynamics revealed in more detailed analyses were mainly attributed to deficiencies in the meteorological model forcing data, to missing water storage compartments in the model, but also to limitations and errors of the GRACE data. Studies that transformed previously identified model deficiencies into adequate modifications of the model structure or parameters are still rare. Prerequisites for a comprehensive improvement of large-scale hydrological models are in particular the consistency of GRACE observation and model variables in terms of filtering, reliable error estimates, and a full assessment of the water balance. Using improvements in GRACE processing techniques, complementary observation data, multi-model evaluations and advanced methods of multi-objective calibration and data assimilation, considerable progress in large-scale hydrological modelling by integration of GRACE data can be expected.

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.

<strong class="journal-contentHeaderColor">Abstract.</strong> The GRACE (Gravity Recovery And Climate Experiment) and GRACE Follow-On (FO) satellite gravity missions enable global monitoring of the mass transport within the Earth&rsquo;s system, leading to unprecedented advances in our understanding of the global water cycle in a changing climate. This study focuses on the quantification of changes in terrestrial water storage based on an ensemble of GRACE and GRACE-FO solutions and two global hydrological models. Significant changes in terrestrial water storage are detected at pluriannual and decadal time -scales in GRACE and GRACE-FO satellite gravity data, that are generally underestimated by global hydrological models. The largest differences (more than 20 cm in equivalent water height) are observed in South America (Amazon, Sao Francisco and Parana river basins) and tropical Africa (Congo, Zambezi and Okavango river basins). Significant differences (a few cm) are observed worldwide at similar timescales, and are generally well correlated with precipitation. While the origin of such differences is unknown, pa rt of it is likely to be climate-related and at least partially due to inaccurate predictions of hydrological models. Slow changes in the terrestrial water cycle may indeed be overlooked in global hydrological models due to inaccurate meteorological forcin g (e.g., precipitation), unresolved groundwater processes, anthropogenic influences, changing vegetation cover and limited calibration/validation datasets. Significant differences between GRACE satellite measurements and hydrological model predictions have been identified, quantified and characterised in the present study. Efforts must be made to better understand the gap between both methods at pluriannual and decadal time-scales, which challenges the use of global hydrological models for the prediction of the evolution of water resources in changing climate conditions.