Search Results (24)

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R²: 0.873, and MAPE: 0.104), and its inversion of QTP’s ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost. Full article

Climate change has intensified the melting of glaciers and permafrost in high-altitude cold regions, leading to more frequent extreme hydrological events. This has caused significant variations in the spatiotemporal distribution of meltwater runoff from the headwater cryosphere, posing a major challenge to regional water security. In this study, the HBV hydrological model was set up and driven by CMIP6 global climate model outputs to investigate the multi-scale temporal variations of runoff under different climate change scenarios in the Tuotuo River Basin (TRB) within the source region of the Yangtze River (SRYR). The results suggest that the TRB will undergo significant warming and wetting in the future, with increasing precipitation primarily occurring from May to October and a notable rise in annual temperature. Both temperature and precipitation trends intensify under more extreme climate scenarios. Under all climate scenarios, annual runoff generally exhibits an upward trend, except under the SSP1-2.6 scenario, where a slight decline in total runoff is projected for the late 21st century (2061–2090). The increase in total runoff is primarily concentrated between May and October, driven by enhanced rainfall and meltwater contributions, while snowmelt runoff also shows an increase, but accounts for a smaller percentage of the total runoff and has a smaller impact on the total runoff. Precipitation is the primary driver of annual runoff depth changes, with temperature effects varying by scenario and period. Under high emissions, intensified warming and glacier melt amplify runoff, while low emissions show stable warming with precipitation dominating runoff changes. Full article

The Qinghai–Tibet Plateau (QTP), a critical hydrological regulator for Asia through its extensive glacier systems, high-altitude lakes, and intricate network of rivers, exhibits amplified sensitivity to climate-driven alterations in precipitation regimes and ice mass balance. While the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized monitoring of terrestrial water storage anomalies (TWSAs) across this hydrologically sensitive region, spatial resolution limitations (3°, equivalent to ~300 km) constrain process-scale analysis, compounded by mission temporal discontinuity (data gaps). In this study, we present a novel downscaling framework integrating temporal gap compensation and spatial refinement to a 0.25° resolution through Gated Recurrent Unit (GRU) neural networks, an architecture optimized for univariate time series modeling. Through the assimilation of multi-source hydrological parameters (glacier mass flux, cryosphere–precipitation interactions, and land surface processes), the GRU-based result resolves nonlinear storage dynamics while bridging inter-mission observational gaps. Grid-level implementation preserves mass conservation principles across heterogeneous topographies, successfully reconstructing seasonal-to-interannual TWSA variability and also its long-term trends. Comparative validation against GRACE mascon solutions and process-based hydrological models demonstrates enhanced capacity in resolving sub-basin heterogeneity. This GRU-derived high-resolution TWSA is especially valuable for dissecting local variability in areas such as the Brahmaputra Basin, where complex water cycling can affect downstream water security. Our study provides transferable methodologies for mountainous hydrogeodesy analysis under evolving climate regimes. Future enhancements through physics-informed deep learning and next-generation climatology–hydrology–gravimetry synergy (e.g., observations and models) could further constrain uncertainties in extreme elevation zones, advancing the predictive understanding of Asia’s water tower sustainability. Full article

Investigating the changes in snow cover caused by climate change is extremely important and has attracted increasing attention in cryosphere and climate research. Optimal remote sensing-based snow datasets can provide long-term daily and global spatial-temporal snow-cover distribution at regional and global scales. However, the application of these snow-cover products is inevitably limited because of the space–time discontinuities caused by cloud obscuration, which poses a significant challenge in snowpack-related studies, especially in High Mountain Asia (HMA), an area that has high-elevation mountains, complex terrain, and harsh environments and has fewer observation stations. To address this issue, we developed an improved five-step hybrid cloud removal strategy by integrating the daily merged snow-cover probability (SCP) algorithm, eight-day merged SCP algorithm, decision tree algorithm, temporal downscaling algorithm, and optimal threshold segmentation algorithm to produce a 21-year, daily cloud-free snow-cover dataset using two daily MODIS snow-cover products over the HMA. The accuracy assessment demonstrated that the newly developed cloud-free snow-cover product achieved a mean overall accuracy of 93.80%, based on daily classified snow depth observations from 86 meteorological stations over 10 years. The time series of the daily percentage of binary snow-cover over HMA was analyzed during this period, indicating that the maximum snow cover tended to change more dramatically than the minimum snow cover. The annual snow-cover duration (SCD) experienced an insignificantly increasing trend over most of the northeastern and southwestern HMA (e.g., Qilian, eastern Kun Lun, the east of Inner Tibet, the western Himalayas, the central Himalayas, and the Hindu Kush) and an insignificant declining trend over most of the northwestern and southeastern HMA (e.g., the eastern Himalayas, Hengduan, the west of Inner Tibet, Pamir, Hissar Alay, and Tien). This new high-quality snow-cover dataset will promote studies on climate systems, hydrological modeling, and water resource management in this remote and cold region. Full article

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole. Full article

Alpine glaciers are a fundamental component of the cryosphere and are significantly sensitive to climate change. One such region is the Hindukush Karakoram Himalaya (HKH) and Tibetan Plateau (TP) region, which contains more than 40,000 glaciers. There are more than 12 glacier inventories available covering parts of (or the entire) HKH region, but these show significant uncertainties regarding the extent of glaciers. Researchers have used different glacier inventories without assessing their accuracy. This study, therefore, assessed the implications of the accuracy of global glacier inventories in hydrological modeling and future water resource planning. The accuracy assessment of most commonly used two global glacier inventories (Global Land Ice Monitoring from Space-GLIMS v 2.0 and Randolph Glacier Inventory-RGI v 6.0) has been carried out for three sub-basins of the Upper Indus Basin—the Swat, the Chitral, and the Kabul River basins (combined, this is referred to as the Great Kabul River Basin)—with a total basin area of 94,552.86 km². Glacier outlines have been compared with various Landsat 7 ETM+, Landsat 8, high-resolution Google Earth images, and manually digitized debris-covered glacier outlines during different years. The total glacier area for the Great Kabul River Basin derived from RGI and GLIMS is estimated to be 2120.35 km² and 1789.94 km², respectively, which was a difference of 16.9%. Despite being sub-basins of the Great Kabul River Basin, the Swat, and the Chitral River basins were different by 54.74% and 19.71%, respectively, between the two inventories, with a greater glacierized area provided by RGI, whereas the Kabul River basin was different by 54.72%, with greater glacierized area provided by GLIMS. The results and analysis show that GLIMS underestimates glacier outlines in the Swat and the Chitral basins and overestimates glacier extents in the Kabul River basin. The underestimation is mainly due to the non-representation of debris-covered glaciers. The overestimation in GLIMS data is due to the digitization of seasonal snow as part of the glaciers. The use of underestimated GLIMS outlines may result in 5–10% underestimation of glacier-melt contribution to flows in the Swat River basin, while an underestimation of 7% to 15% is expected in the Chitral River Basin, all compared to RGI v 6.0 outlines. The overestimation of glacier-melt contribution to flows in the Kabul River basin is insignificant (1% to 2%) using GLIMS data. In summary, the use of the GLIMS inventory will lead to underestimated flows and show that the Great Kabul River Basin (particularly the Chitral River Basin) is less sensitive to climate change effects. Thus, the current study recommends the use of RGI v 6.0 (best glacier inventory) to revisit the existing biased hydro-climate studies and to improve future hydro-climate studies with the concomitant rectification of the MODIS snow coverage data. The use of the best glacier inventory will provide the best estimates of flow sensitivity to climate change and will result in well-informed decision-making, precise and accurate policies, and sustainable water resource management in the study area. The methodology adopted in the current study may also be used in nearby areas with similar hydro-climate conditions, as well as for the most recently released RGI v 7.0 data. Full article

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 μm, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 μm, followed by January and February with 450.77 μm and 417.78 μm, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 μm to 497.141 μm. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow–ice albedo and their radiative forcing effect. Full article

Watershed management requires reliable information about hydrologic ecosystem services (HESs) to support decision-making. In cold alpine regions, the hydrology regime is largely affected by frozen ground and snow cover. However, existing special models of ecosystem services usually ignore cryosphere elements (such as frozen ground and snow cover) when mapping water yield, which limits their application and promotion in cold alpine regions. By considering the effects of frozen ground and snow cover on water yield, a new version of the Seasonal Water Yield model (SWY) in the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) was presented and applied in the Three-River Headwaters Region (TRHR) in southeastern Qinghai-Tibetan Plateau (QTP). Our study found that incorporating the effects of frozen ground and snow cover improved model performance. Frozen ground acts as a low permeable layer, reducing water infiltration, while snow cover affects water yield through processes of melting and sublimation. Both of these factors can significantly impact the distribution of spatial and temporal quickflow and baseflow. The annual average baseflow and water yield of the TRHR would be overestimated by 13 mm (47.58 × 10⁸ m³/yr) and 14 mm (51.24 × 10⁸ m³/yr), respectively, if the effect of snow cover on them is not considered. Furthermore, if the effect of frozen ground on water yield were not accounted for, there would be an average of 6 mm of quickflow misestimated as baseflow each year. Our study emphasizes that the effects of frozen ground and snow cover on water yield cannot be ignored, particularly over extended temporal horizons and in the context of climate change. It is crucial to consider their impacts on water resources in cold alpine regions when making water-related decisions. Our study widens the application of the SWY and contributes to water-related decision-making in cold alpine regions. Full article

As a significant component of the cryosphere, snow cover plays a crucial role in modulating atmospheric circulation and regional hydrological equilibrium. Therefore, studying the dynamics of snow cover and its response to climate change is of great significance for regional water resource management and disaster prevention. In this study, reanalysis climate datasets and a new MODIS snow cover extent product over China were used to analyze the characteristics of climate change and spatiotemporal variations in snow cover in the Keriya River Basin (KRB). Furthermore, the effects of climate factors on snow cover and their coupling effects on runoff were quantitatively evaluated by adopting partial least squares regression (PLSR) method and structural equation modeling (SEM), respectively. Our findings demonstrated the following: (1) Air temperature and precipitation of KRB showed a significant increase at rates of 0.24 °C/decade and 14.21 mm/decade, respectively, while the wind speed did not change significantly. (2) The snow cover frequency (SCF) in the KRB presented the distribution characteristics of “low in the north and high in the south”. The intra-annual variation of snow cover percentage (SCP) of KRB displayed a single peak (in winter), double peaks (in spring and autumn), and stability (SCP > 75%), whose boundary elevations were 4000 m and 6000 m, respectively. The annual, summer, and winter SCP in the KRB declined, while the spring and autumn SCP experienced a trend showing an insignificant increase during the hydrological years of 2001–2020. Additionally, both the annual and seasonal SCF (except autumn) will be further increased in more than 50% of the KRB, according to estimates. (3) Annual and winter SCF were controlled by precipitation, of which the former showed a mainly negative response, while the latter showed a mainly positive response, accounting for 43.1% and 76.16% of the KRB, respectively. Air temperature controlled SCF changes in 45% of regions in spring, summer, and autumn, mainly showing negative effects. Wind speed contributed to SCF changes in the range of 11.23% to 26.54% across annual and seasonal scales. (4) Climate factors and snow cover mainly affect annual runoff through direct influences, and the total effect was as follows: precipitation (0.609) > air temperature (−0.122) > SCP (0.09). Full article

Permafrost impacts the subsurface hydrology and determines the transport of buried biochemical substances. Current evaluations of permafrost mostly focus on the overlying active layer. However, the basic but missing information of permafrost thickness constrains the quantification of trends and effects of permafrost degradation on subsurface hydrological processes. Our study quantified the long-term variations in permafrost thickness on the Tibetan Plateau (TP) between 1851 and 2100 based on layered soil temperatures calculated from eight earth system models (ESMs) of Coupled Model Intercomparison Project (the sixth phase) and validated by field observations and previous permafrost pattern from remote sensing. The calculated permafrost distribution based on ESMs was validated by the pattern derived from the MODIS datasets and field survey. Our results show that permafrost thicker than 10 m covers approximately 0.97 million km${}^2$ of the total area of the TP, which represents an areal extent of over 36.49% of the whole TP. The mean permafrost thickness of the TP was 43.20 m between 1851 and 2014, and it would decrease at an average rate of 9.42, 14.99, 18.78, and 20.75 cm per year under scenarios SSP126, SSP245, SSP370, and SSP585 from 2015 to 2100, respectively. The permafrost thickness will decrease by over 50 cm per year in Qiangtang Basin under SSP585. Our study provides new insights for spatiotemporal changes in permafrost thickness and a basic dataset combined results of remote sensing, field measurements for further exploring relevant hydrological, geomorphic processes and biogeochemical cycles in the plateau cryospheric environment. Full article

Glacier, snow, and ice are the essential components of the Himalayan cryosphere and provide a sustainable water source for different applications. Continuous and accurate monitoring of glaciers allows the forecasting analysis of natural hazards and water resource management. In past literature, different methodologies such as spectral unmixing, object-based detection, and a combination of various spectral indices are commonly utilized for mapping snow, ice, and glaciers. Most of these methods require human intervention in feature extraction, training of the models, and validation procedures, which may create bias in the implementation approaches. In this study, the deep learning classifier based on ENVINet5 (U-Net) architecture is demonstrated in the delineation of glacier boundaries along with snow/ice over the Bara Shigri glacier (Western Himalayas), Himachal Pradesh, India. Glacier monitoring with Landsat data takes the advantage of a long coverage period and finer spectral/spatial resolution with wide coverage on a larger scale. Moreover, deep learning utilizes the semantic segmentation network to extract glacier boundaries. Experimental outcomes confirm the effectiveness of deep learning (overall accuracy, 91.89% and Cohen’s kappa coefficient, 0.8778) compared to the existing artificial neural network (ANN) model (overall accuracy, 88.38% and kappa coefficient, 0.8241) in generating accurate classified maps. This study is vital in the study of the cryosphere, hydrology, agriculture, climatology, and land-use/land-cover analysis. Full article

Global warming affects the hydrological characteristics of the cryosphere. In arid and semi-arid regions where precipitation is scarce, glaciers and snowmelt water assume important recharge sources for downstream rivers. Therefore, the simulation of snowmelt water runoff in mountainous areas is of great significance in hydrological research. In this paper, taking the Hutubi River Basin in the Tianshan Mountains as the study area, we used the “MODIS Daily Cloud-free Snow Cover 500 m Dataset over China” (MODIS_CGF_SCE) to carry out the Snowmelt Runoff Model (SRM) simulation and evaluated the simulation accuracy. The results showed that: (1) The SRM preferably simulated the characteristics of the average daily flow variation of the Hutubi River from May to October, from 2003–2009. The monthly total runoff was maximum in July and minimum in October. Extreme precipitation events influenced the formation of flood peaks, and the interannual variation trend of total runoff from May to October was increased. (2) The mean value of the volume difference (${\mathrm{D}}_{\mathrm{V}}$) during the model validation period was 8.85%, and the coefficient of determination (${\mathrm{R}}^2$) was 0.73. In general, the SRM underestimates the runoff of the Hutubi River, and the simulation accuracy is more accurate in the normal water period than in the high-water period. (3) By analyzing MODIS_CGF_SCE from 2003 to 2009, areas above 3200 m elevation in the Hutubi River Basin were classified as permanent snow areas, and areas below 3200 m were classified as seasonal snow areas. In October, the snow area in the Hutubi River Basin gradually increased, and the increase in snow cover in the permanent snow area was greater than that in the seasonal snow area. The snowmelt period was from March to May in the seasonal snow area and from May to early July in the permanent snow area, and the minimum snow cover was 0.7%. Full article

Snow is the dominant form of precipitation and the main cryospheric feature of the High Arctic (HA) covering its land, sea, lake and river ice surfaces for a large part of the year. The snow cover in the HA is involved in climate feedbacks that influence the global climate system, and greatly impacts the hydrology and the ecosystems of the coldest biomes of the Northern Hemisphere. The ongoing global warming trend and its polar amplification is threatening the long-term stability of the snow cover in the HA. This study presents an extensive review of the literature on observed and projected snow cover conditions in the High Arctic region. Several key snow cover metrics were reviewed, including snowfall, snow cover duration (SCD), snow cover extent (SCE), snow depth (SD), and snow water equivalent (SWE) since 1930 based on in situ, remote sensing and simulations results. Changes in snow metrics were reviewed and outlined from the continental to the local scale. The reviewed snow metrics displayed different sensitivities to past and projected changes in precipitation and air temperature. Despite the overall increase in snowfall, both observed from historical data and projected into the future, some snow cover metrics displayed consistent decreasing trends, with SCE and SCD showing the most widespread and steady decreases over the last century in the HA, particularly in the spring and summer seasons. However, snow depth and, in some regions SWE, have mostly increased; nevertheless, both SD and SWE are projected to decrease by 2030. By the end of the century, the extent of Arctic spring snow cover will be considerably less than today (10–35%). Model simulations project higher winter snowfall, higher or lower maximum snow depth depending on regions, and a shortened snow season by the end of the century. The spatial pattern of snow metrics trends for both historical and projected climates exhibit noticeable asymmetry among the different HA sectors, with the largest observed and anticipated changes occurring over the Canadian HA. Full article

Earth’s gravitational field provides invaluable insights into the changing nature of our planet. It reflects mass change caused by geophysical processes like continental hydrology, changes in the cryosphere or mass flux in the ocean. Satellite missions such as the NASA/DLR operated Gravity Recovery and Climate Experiment (GRACE), and its successor GRACE Follow-On (GRACE-FO) continuously monitor these temporal variations of the gravitational attraction. In contrast to other satellite remote sensing datasets, gravity field recovery is based on geophysical inversion which requires a global, homogeneous data coverage. GRACE and GRACE-FO typically reach this global coverage after about 30 days, so short-lived events such as floods, which occur on time frames from hours to weeks, require additional information to be properly resolved. In this contribution we treat Earth’s gravitational field as a stationary random process and model its spatio-temporal correlations in the form of a vector autoregressive (VAR) model. The satellite measurements are combined with this prior information in a Kalman smoother framework to regularize the inversion process, which allows us to estimate daily, global gravity field snapshots. To derive the prior, we analyze geophysical model output which reflects the expected signal content and temporal evolution of the estimated gravity field solutions. The main challenges here are the high dimensionality of the process, with a state vector size in the order of ${10}^3$ to ${10}^4$, and the limited amount of model output from which to estimate such a high-dimensional VAR model. We introduce geophysically motivated constraints in the VAR model estimation process to ensure a positive-definite covariance function. Full article

In this study, we calculate the hydrological plus cryospheric excitation of polar motion (hydrological plus cryospheric angular momentum, HAM/CAM) using mascon solutions based on observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions. We compare and evaluate HAM/CAM computed from GRACE and GRACE-FO mascon data provided by the Jet Propulsion Laboratory (JPL), the Center for Space Research (CSR), and the Goddard Space Flight Center (GSFC). A comparison with HAM obtained from the Land Surface Discharge Model is also provided. An analysis of HAM/CAM and HAM is performed for overall variability, trends, and seasonal and non-seasonal variations. The HAM/CAM and HAM estimates are validated using the geodetic residual time series (GAO), which is an estimation of the hydrological plus cryospheric signal in geodetically observed polar motion excitation. In general, all mascon datasets are found to be equally suitable for the determination of overall, seasonal, and non-seasonal HAM/CAM oscillations, but some differences in trends remain. The use of an ellipsoidal correction, implemented in the newest solution from CSR, does not noticeably affect the consistency between HAM/CAM and GAO. Analysis of the data from the first two years of the GRACE-FO mission indicates that the current accuracy of HAM/CAM from GRACE-FO mascon data meets expectations, and the root mean square deviation of HAM/CAM components are between 5 and 6 milliarcseconds. The findings from this study can be helpful in assessing the role of satellite gravimetry in polar motion studies and may contribute towards future improvements to GRACE-FO data processing. Full article