Improvement of Global Hydrological Models Using GRACE Data

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.

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

The Gravity Recovery and Climate Experiment (GRACE) data have been extensively used to evaluate the total terrestrial water storage anomalies (TWSA) from hydrological models. However, which individual water storage components (i.e., soil moisture storage anomalies (SMSA) or groundwater water storage anomalies (GWSA)) cause the discrepancies in TWSA between GRACE and hydrological models have not been thoroughly investigated or quantified. In this study, we applied GRACE mass concentration block (mascon) solutions to evaluate the spatio-temporal TWSA trends (2003–2014) from seven prevailing hydrological models (i.e., Noah-3.6, Catchment Land Surface Model (CLSM-F2.5), Variable Infiltration Capacity macroscale model (VIC-4.1.2), Water—Global Assessment and Prognosis (WaterGAP-2.2d), PCRaster Global Water Balance (PCR-GLOBWB-2), Community Land Model (CLM-4.5), and Australian Water Resources Assessment Landscape model (AWRA-L v6)) in Australia and, more importantly, identified which individual water storage components lead to the differences in TWSA trends between GRACE and hydrological models. The results showed that all of the hydrological models employed in this study, except for CLM-4.5 model, underestimated the GRACE-derived TWSA trends. These underestimations can be divided into three categories: (1) ignoring GWSA, e.g., Noah-3.6 and VIC-4.1.2 models; (2) underrating both SMSA and GWSA, e.g., CLSM-F2.5, WaterGAP-2.2d, and PCR-GLOBWB-2 models; (3) deficiently modeling GWSA, e.g., AWRA-L v6 model. In comparison, CLM-4.5 model yielded the best agreement with GRACE but overstated the GRACE-derived TWSA trends due to the overestimation of GWSA. Our results underscore that GRACE mascon solutions can be used as a valuable and efficient validation dataset to evaluate the spatio-temporal performance of hydrological models. Confirming which individual water storage components result in the discrepancies in TWSA between GRACE and hydrological models can better assist in further hydrological model development.

Land water storage plays a fundamental role in the West African water cycle and has an important impact on climate and on the natural resources of this region. However, measurements of land water storage are scarce at regional and global scales and especially in poorly instrumented endorheic regions, such as most of the Sahel, where little useful information can be derived from river flow measurements and basin water budgets. The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides an accurate measurement of the terrestrial gravity field variations from which land water storage variations can be derived. However, their retrieval is not straightforward, and different methods are employed, which results in different water storage GRACE products. On the other hand, water storage can be estimated by land surface modeling forced with observed or satellite‐based boundary conditions, but such estimates can be highly model dependent. In this study, land water storage by six GRACE products and soil moisture estimations by nine land surface models (run within the framework of the African Monsoon Multidisciplinary Analysis Land Surface Intercomparison Project (ALMIP)) are evaluated over West Africa, with a particular focus on the Sahelian area. The water storage spatial distribution, including zonal transects, its seasonal cycle, and its and interannual variability, are analyzed for the years 2003–2007. Despite the nonnegligible differences among the various GRACE products and among the different models, a generally good agreement between satellite and model estimates is found over the West Africa study region. In particular, GRACE data are shown to reproduce well the water storage interannual variability over the Sahel for the 5 year study period. The comparison between satellite estimates and ALMIP results leads to the identification of processes needing improvement in the land surface models. In particular, our results point out the importance of correctly simulating slow water reservoirs as well as evapotranspiration during the dry season for accurate soil moisture modeling over West Africa.

SUMMARY In this paper, we study the geoid changes caused by the Sumatra-Andaman earthquake (2004 December 26) and the Nias earthquake (2005 March 28) using data from the gravity recovery and climate experiment (GRACE) satellites. New is that we explore the possibility to separate the effects of the Sumatra-Andaman and Nias earthquakes. Based on correlation analysis we investigate the separability of the Sumatra-Andaman and Nias signals, and the feasibility of separating the non-seismic linear trend from the time-dependent post-seismic relaxation effect. As input data, we use 76 months of global geopotential coefficients from the GFZ release 4 (RL04) of GRACE data. To suppress unphysical artefacts in GRACE data (‘striping’), the GRACE coefficients are smoothed with an anisotropic decorrelation filter. Model parameters are estimated through a Bayesian approach. We compare our results with geophysical models of both earthquakes, calculated based on the viscoelastic-gravitational dislocation theory. The slip models used are inverted from GPS measurements. Our findings indicate that, the inversion of GRACE data for the Sumatra-Andaman effect will contain significant signal from the Nias earthquake, and will benefit from removing the modelled Nias effect from the data. Comparing GRACE measurements to geophysical models reveals significant differences. We suggest, how the GPS-calibrated models may be improved to better fit the GRACE data. In the first months after the earthquake, we also find evidence for a fast process in the Andaman sea, possibly due to aseismic slip. Similar phenomena were observed in previous studies.

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.

Abstract. Since 2002, the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GRACE-FO, hereafter GRACE) missions have offered global observations of total water storage (TWS). However, the relatively short record of GRACE data poses a significant challenge for researchers to investigate the full range and long-term variability in TWS. In this study, we present RecGAN, a novel Conditional Generative Adversarial Network (CGAN) comprising a RecNet generator and pixel discriminator. Our approach aims to generate long-term GRACE-like TWS observations by calibrating the WaterGAP Global Hydrology Model (WGHM). The generator is trained to produce observations conforming to the distribution of GRACE data, while the discriminator is trained to assess whether each generated pixel resembles GRACE data. Our results show that RecGAN effectively enhances the consistency between GRACE observations and WGHM-derived TWS changes, achieving improved correlation coefficients, Nash-Sutcliffe Efficiency, and Normalized Root-Mean-Square Error. In addition, RecGAN is robust to different GRACE mascon data, crop sizes used during the training period, and hydrological models targeted for calibration. This study illustrates a promising application of employing CGANs to fine-tune the WGHM output to match GRACE observations. This approach enables the generation of longterm TWS change datasets, which are invaluable for evaluating long-term water storage fluctuations, allocating water resources, and forecasting future hydrological extremes.

<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.

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.

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

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.

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.

Influences of crustal thickening in the Tibetan Plateau on loading modeling and inversion associated with water storage variation

Globally, the groundwater is the most favourable and demandable freshwater resource. The threat to surface water resources and subsurface aquifer systems increased with climate change as well as surplus usage of groundwater in highly populated regions. Thus, in present day, groundwater is the primary resource for the sustainability of agriculture, industries and domestic activities in arid and semi-arid areas of the world. The overexploitation of subsurface water initiates land subduction. As water is the source of life on Earth, so it is essential to monitor and predict the capability of groundwater for secure sustainable management of subsurface water with the extreme climate conditions and population growth. The traditional way of keeping a check on groundwater level change is considering in situ or point measurements using the local network of well data. But these measurements are insufficient as hydrological models depend on the spatial data referring over large areas. Global Positioning System (GPS) and Gravity Recovery and Climate Experiment (GRACE) mission are perfect tools to overcome the drawbacks of the traditional groundwater monitoring. It measures the change in ice sheets and glaciers, near-surface and subsurface GWS changes, as well as sea-level changes by GRACE 1 mission and GRACE, Follow on (FO) mission. Most of the researches are based on GRACE satellite data to monitor GWS changes over a large-scale area as continental or regional achieved successful consequences. Although the past decade GRACE studies exhibited that GRACE solution is capable of developing accurate quantitative estimations for GWS scenarios with the high temporal resolution. Still, it restricts only to continental or regional scale studies. Therefore, most of the recent studies took the step for effective downscaling of GRACE data.

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.