Coastal zones face increased risks from the combined effects of climate-driven sea-level rise and vertical land motion (VLM), which together determine rates of relative sea-level (RSL) change. While oceanic contributions to RSL are increasingly well monitored and projected, land subsidence (i.e., negative VLM) remains one of the least systematically observed and most spatially heterogeneous components of RSL, despite its potential to locally exceed climate-driven ocean rise by an order of magnitude. This observational gap is especially pronounced in rapidly urbanizing and data-limited regions, where sparse tide-gauge and GNSS networks hinder the identification of subsidence hotspots and their evolving impacts on coastal risks.

In this talk, I present a framework that leverages satellite geodesy as a climate observing system to resolve the spatiotemporal dynamics of land subsidence and quantify its contribution to present and future relative sea-level change, using Java Island, Indonesia, as a regional-scale case study. We generated high-spatial resolution (75 m) contemporary VLM fields from using multi-geometry Sentinel-1 interferometric synthetic aperture radar (InSAR), revealing widespread and temporally evolving subsidence patterns with rates exceeding 1 cm per year across multiple coastal and inland urban centers. While Jakarta has dominated the subsidence narrative in Indonesia, we find that several other coastal cities, including Cirebon, Pekalongan, Tegal, and Semarang, are sinking two to three times faster, with localized rates approaching 10 cm per year.

To disentangle the dominant drivers of deformation, we applied unsupervised machine-learning spatiotemporal clustering to InSAR time series, guided by geological and land-use information. This analysis reveals nonlinear and spatially heterogeneous subsidence behaviors primarily associated with groundwater extraction in urban, industrial, and agricultural regions, alongside localized deformation linked to natural processes such as volcanism. Finally, we constructed synthetic tide-gauge records at 5-km spacing along the 1,500 km northern coastline by integrating InSAR-derived VLM with satellite altimetry and probabilistic sea-level projections. These virtual gauges show that neglecting land subsidence leads to systematic underestimation of RSL change by more than 90% in some locations and that subsidence will remain the dominant contributor to RSL rise across much of the coastline through 2050.

This work illustrates how geodetic observing systems can fill critical observational gaps in coastal climate research, enabling spatially explicit, process-informed RSL estimates and providing a transferable framework for improving sea-level risk assessments in vulnerable, data-sparse regions worldwide.

How to cite: Ohenhen, L.: Resolving land subsidence contribution to present and future relative sea level change using satellite geodesy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8826, https://doi.org/10.5194/egusphere-egu26-8826, 2026.

Variations in sea level are a globally comprehensively measurable indicator of the effect of climate change on the Earth system. Satellite geodesy provides data with global coverage to analyze sea level changes in space and time, but also to investigate the individual contributions to sea level from the subsystems oceans, continental hydrology, glaciers, ice sheets, and the solid Earth. Particularly valuable for this purpose are time-variable satellite gravity, realized by the GRACE and GRACE-FO missions, and satellite altimetry over the oceans, realized, e.g., by the Jason-1/-2/-3 and Sentinel-6 reference missions. However, previous studies show that the uncertainty of the estimated Antarctic Ice Sheet’s contribution to sea level remains large, primarily due to errors in the glacial isostatic adjustment (GIA) correction. We use a global fingerprint inversion method that evaluates GRACE and ocean altimetry data in a globally consistent framework and enables the quantification of individual contributions to sea level on a monthly basis on global grids. The inversion is additionally supplemented by observations from Argo floats. The parametrization of the contributions from steric effects, ice sheets, glaciers, hydrology, and GIA are realized by time-invariant sea-level fingerprints obtained from a priori information. This includes, e.g., the locations of mass changes or statistically obtained information from geophysical model simulations. In a methodological advancement of the inversion method, we have implemented a new parametrization of the ice mass changes (IMC) of the Antarctic ice sheet. Previously, IMC and corresponding sea level change has been estimated only on basin level for 27 large ice catchment areas, so-called drainage basins. However, this coarse parametrization of IMC prevents the inversion method from better resolving errors in the GIA correction in upcoming inversion implementations. We have therefore introduced a high-resolution parametrization based on individual grid points with a resolution of up to 50 km, resulting in up to 4755 Antarctic mass balance parameters to be estimated in a globally consistent way. In order to solve this inverse problem, we introduced altimetry over ice sheets as an additional observation at a 10 km spatial and a monthly temporal resolution. We present and discuss results from different variants of parametrization of IMC and different variants of implementation of ice altimetry observations. This methodological advancement presented here is a necessary step towards minimizing GIA-related errors when determining the sea level budget utilizing this global framework in the future.

How to cite: Willen, M. O., Uebbing, B., Horwath, M., and Kusche, J.: A global inversion for sea-level contributions from satellite data: towards improving Antarctica's representation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10826, https://doi.org/10.5194/egusphere-egu26-10826, 2026.

The Gravity Recovery And Climate Experiment (GRACE) and its follow-on mission, GRACE-FO, have observed global mass changes and transports, expressed as total water storage anomalies (TWSA), for over two decades. However, for climate change attribution and other applications, multi-decadal TWSA time series are required. This need has prompted several studies on reconstructing TWSA using regression or machine learning techniques, aided by predictor variables such as rainfall and sea surface temperature. However, the training period is limited to a couple of years, making it hard to capture interannual signals accurately. Furthermore, learned relationships between climate variables and water storage cannot be transferred straightforwardly to the past. To overcome the limitation and provide a more long-term, consistent dataset, we derive a preliminary reconstruction and combine it with large-scale time-variable pre-GRACE gravity information from geodetic satellite laser ranging (SLR) and Doppler Orbitography by Radiopositioning Integrated on Satellite (DORIS) tracking from Löcher et al. (2025). We reconstruct GRACE-like TWSA for the global land, excluding Greenland and Antarctica, from 1984 onward. We find that the seasonal cycle of our new reconstruction is consistent with that of previously published purely climate-data-based reconstructions. Moreover, in many regions, TWSA trends were markedly different in the pre-GRACE timeframe, and we thus suggest caution when interpolating GRACE-derived trends.

How to cite: Hacker, C., Gutknecht, B. D., Löcher, A., and Kusche, J.: Reconstructing terrestrial water storage anomalies based on climate data and pre-GRACE satellite observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16439, https://doi.org/10.5194/egusphere-egu26-16439, 2026.

The Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO provide unique observations of Earth’s time-variable gravity field, enabling direct monitoring of mass redistribution expressed as equivalent water height (EWH). While gridded Level-2B products are widely used across hydrology, glaciology, and solid-Earth studies, uncertainty information remains fragmented or inaccessible to end users. In practice, this has led to the widespread use of empirical or ad hoc uncertainty estimates, limiting data assimilation and other geophysical applications that require spatially and temporally resolved observational error information.

We present TUD-L2B-EWH_UNC-GRACE, a globally gridded Level-2B GRACE(-FO) EWH data product that provides a comprehensive and transparent characterisation of uncertainty alongside the mass anomaly fields. Unlike conventional approaches that rely on propagation of full normal matrices or impose assumptions on error correlations, TUD-L2B-EWH_UNC combines ensemble statistics from multiple independently processed Level-2 solutions to quantify pre-processing uncertainties. These include contributions from ocean tide model differences, parametrisation strategies, and uncertainty in the Atmosphere and Ocean De-aliasing (AOD1B) background model.

Post-processing uncertainties associated with filtering, leakage, and Glacial Isostatic Adjustment (GIA) are quantified separately. Filtering-related uncertainty is evaluated using a known-pair approach, while GIA uncertainty is assessed using an ensemble of 56 published GIA models. Error fields are provided for a suite of anisotropic filtering strategies (DDK(2–7)), enabling systematic assessment of filtering choices, leakage effects, and model dependence on the total uncertainty budget.

TUD-L2B-EWH_UNC is the first Level-2B EWH dataset to deliver end-to-end, spatially and temporally resolved uncertainty fields in a user-ready gridded format. This design supports consistent uncertainty handling across hydrological, glaciological, and solid-Earth applications. Ancillary tidal corrections and climatological fits of signal and leakage-related errors are distributed separately through the companion products TUD-L2B-EWH_CLIM-GRACE and TUD-L2B-EWH_CLIM_LEAKAGE-GRACE. All datasets are publicly available (DOI: doi.org/10.4121/4fc748e8-01c7-4f06-87da-653937b078f7) via the TU Delft GRACE Portal (https://grace-cube.lr.tudelft.nl/).

How to cite: Cuadrat-Grzybowski, M. and de Teixeira da Encarnacao, J. G.: TUD-L2B-EWH_UNC: A Monthly Global Level-2B GRACE(-FO) EWH Uncertainty Product, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11806, https://doi.org/10.5194/egusphere-egu26-11806, 2026.

Under the assumption that a warming climate leads to an intensification of the global water cycle, it is hypothesized that also the occurrence frequency and severity of extreme events such as droughts and floods will increase in the upcoming decades. GRACE/-FO observations of terrestrial water storage (TWS) have been used in the past to identify and analyse extreme events both on a global and regional scale. However, these analyses are restricted by the limited spatial and temporal resolution of current satellite gravimetry observations. Especially, flooding events tend to occur very locally and with short temporal (sub-monthly) extent, thus capturing them is challenging. Future satellite gravimetry missions, particularly the double-pair constellation MAGIC, are expected to significantly enhance the spatial and temporal resolution. In this study, we globally investigate the benefit MAGIC can achieve to detect wet extreme events using long-term (50 years) end-to-end simulations of GRACE-C and MAGIC.

The simulation environment is based on the acceleration approach and considers tidal and non-tidal background model errors as well as instrument noise of the acceleration and ranging instruments following the current MAGIC mission design studies. As input and reference, we use the daily output of a climate model (GFDL-CM4) from the CMIP6 archive that has been identified as a realistic representation of water storage evolution in previous studies. To explore the improved temporal and spatial resolution expected from the MAGIC constellation, we (i) compare extreme values derived from 5-daily gravity field simulations to those from monthly fields, and (ii) show how the weaker spatial filtering required for MAGIC has a positive influence on the detectability of extremes.

For the analysis two different approaches are exploited: One method focuses solely on the stochastic characteristics of the time series in terms of extreme value theory, evaluating the magnitude-frequency relationship of large TWS values by calculating expected return levels of wet extremes. The other approach builds on the fact that a 50-years simulation time series allows to derive statistically meaningful conclusions from directly comparing reference and simulation output on a time series level. We evaluate the time of occurrence of wet extremes on the basis of classification scores assessing correctly and incorrectly identified extreme events.

How to cite: Middendorf, K., Jensen, L., Schlaak, M., Haas, J., Dobslaw, H., Pail, R., Güntner, A., and Eicker, A.: Benefits of future satellite gravimetry missions for characterizing extreme wet events in terrestrial water storage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10722, https://doi.org/10.5194/egusphere-egu26-10722, 2026.

Abstract:

Climate change has been proven to exacerbate the ongoing deformations of the Earth's surface in Germany. Also, human activities such as mining, fluid extraction, and reservoir-induced seismicity cause local surface deformations. Therefore, long-term forecasts of Earth's surface movements are needed for infrastructure planning, hazard mitigation, and the sustainable management of natural resources in Germany. By applying Machine Learning (ML) and statistical analyses, we develop scientific scenarios of climate change to forecast surface movements in Germany over the next two decades. Together with Global Navigation Satellite Systems (GNSS), data from five interdisciplinary fields, including the Sun and Moon ephemerides, polar motions, surface loadings, gravity variations, and meteorology, are utilized as features for training ML-based forecast models. Our results indicate that the accuracy of regression ML models reaches millimeter levels, and the decadal forecast models produce fewer than 2% extreme values in the total predictions per year. Based on climate change scenarios, the findings reveal that the average intra-plate motions in Germany will accelerate from ~1.2 mm/yr to ~1.5mm/yr over the next two decades. The annual variations across the 346 GNSS monitoring stations are predicted to increase from 4.7mm to 5.1mm. Surface deformations will be more severe in the southeastern regions and river basins such as the Elbe, Weser, Ems, and Rhine. Significant extensions are expected in the Eifel volcanic region, while notable compressions may occur along the Upper Rhine Graben and the Saxony region in the next twenty years. Additionally, experimental functions showing the statistical distribution of Earth's surface deformation trends in Germany over the next two decades have been proposed. Potentially, the methodology in this study can also be adapted to forecast surface movements related to climate change in polar regions.

Keywords:

Climate change, Surface deformation, Movement forecast, Machine learning, GNSS

How to cite: Le, N., Kłos, A. K., Pham, T. T. T., Luong, T. T., Nguyen, C., and Thomas, M.: Scientific Scenarios of Climate Change for Decadal Forecasts of Earth’s Surface Movements in Germany , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9263, https://doi.org/10.5194/egusphere-egu26-9263, 2026.

In existing GNSS-based terrestrial water storage (TWS) inversion studies, the PREM model is commonly adopted, and crustal structural heterogeneity is often neglected. Here, we conduct a comprehensive assessment of how different Earth models affect inversion results using both checkerboard-model experiments and continuous smooth-model experiments. The results show that, under realistic hydrological loading conditions within the study region (100°E–115°E, 25°N–40°N), inversion differences among global 1-D reference Earth models are below 2%, whereas the differences between global 1-D reference models and regional crustal models are ~11%; meanwhile, discrepancies between the two regional crustal models remain below 4%. Application to observed GNSS coordinate time series in Yunnan indicates that the spatial pattern of the annual equivalent water height (EWH) amplitude derived from GNSS is broadly consistent with that from the GLDAS hydrological model; however, the choice of Earth model can still substantially alter the magnitude of the inferred amplitude and its spatial distribution. Correlation analyses further suggest that Earth-model dependence is weak for large-scale inversions, but becomes non-negligible at smaller spatial scales. For a representative small-scale subregion (101.75°E–102°E, 22.75°N–23°N), we therefore recommend using the AK135F model to construct Green’s functions. Overall, our findings demonstrate that Earth-model selection is a key source of uncertainty in GNSS-based TWS inversion, and provide practical guidance for choosing appropriate Earth models to improve inversion accuracy.

How to cite: He, J. and Li, Z.: Impact of Earth Model Selection on Terrestrial Water Storage Inversion from GNSS Vertical Displacements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17088, https://doi.org/10.5194/egusphere-egu26-17088, 2026.

Monitoring essential climate variables such as Sea level rise, Earth’s Energy Imbalance, and ice-mass changes relies critically on space-geodetic observations of surface deformation and variations of the gravity field.
In particular, satellite geodesy provides decades-long, globally consistent records that are fundamental for quantifying climate-driven surface mass redistribution. However, these observations integrate both mass changes from the oceans, atmosphere, cryosphere and continental hydrology and the associated solid Earth response. Isolating climate variables from geodetic data therefore requires models that reflect the solid Earth response across timescales relevant to contemporary variability.
Yet, a critical assumption underlies much of current space-geodetic standard processing: the solid Earth response to surface mass variations is treated as purely elastic, i.e. instantaneous and fully recoverable. However, there is a growing body of evidence from laboratory rock mechanics experiments and geophysical observations suggesting that the Earth’s mantle exhibits a time-dependent, recoverable anelastic response across intermediate timescales  that could significantly affect geodetic at decadal to centennial timescales.
Here, we exploit several decades of Satellite Laser Ranging (SLR) observations towards passive spherical satellites to constrain key parameters governing the time-dependent mantle anelasticity. Owing to long-term measurements and sensitivity to low-degree gravity field variations, including solid Earth tides (C20, C30) and the pole tide (C21/S21), SLR observations are particularly well suited to probing deep Earth mantle rheology over decadal timescales.
We combine analytical orbit perturbation theory with the Hill-Clohessy-Wiltshire equations to quantify the sensitivity of the SLR observables to rheology and to choose an optimal parametrization. We then numerically estimate the solid Earth transient rheological properties from the SLR time series using an anelasticity framework consistent with seismic attenuation theory. Our results are compared with independent rheological constraints and yield a new set of frequency-dependent Love numbers that capture the Earth’s mantle transient rheology across decadal timescales.
We further show that accounting for this  transient rheology by incorporating the corresponding frequency-dependent Love numbers into the modeling of solid Earth tides, pole tide and surface loading-induced deformation, introduces systematic differences in climate-relevant geodetic time-series, including  satellite altimetry sea level rise estimates and ocean mass trends derived from satellite gravimetry.
More broadly, our results show that as space geodetic records become longer, data processing cannot rely solely on an  elastic solid Earth assumption. Instead, it must account for solid Earth transient rheology and the fact that geodetic observables will increasingly depend on the cumulative loading history, strengthening the need for interdisciplinary geodetic, geophysical and climate studies.

How to cite: Rousselet, M., Couhert, A., Chanard, K., Exertier, P., and Fleitout, L.: Constraining transient solid Earth rheology using satellite orbit perturbations to assess the dynamics of climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18067, https://doi.org/10.5194/egusphere-egu26-18067, 2026.

The SING project aims to evaluate the added value of the NGGM and MAGIC missions for scientific applications and operational services in hydrology, ocean sciences, glaciology, climate sciences, solid earth sciences, and geodesy. Using a closed-loop simulator with a comprehensive description of instrumental, ocean tide, dealiasing and toning errors, synthetic observations of the gravity field have been generated to assess the observability of mass changes occurring in the atmosphere, ocean, hydrosphere, cryosphere, and solid earth for different mission configurations, including GRACE-C-like (single polar pair), NGGM (single inclined pair), and MAGIC (double pair). 

The synthetic gravity observations have first been used to assess the closure of the sea level budget. With historical GRACE, altimetry, and Argo data, global sea level budget closure is achieved with an accuracy of 0.3–0.4 mm/yr (2003–2015). Using VADER-filtered simulations, all three configurations contribute <0.1 mm/yr to the global mean sea level error. NGGM and MAGIC maintain this accuracy even without filtering, unlike GRACE-C. At regional scales, NGGM and MAGIC notably improve significantly the sea level budget closure, especially at seasonal and interannual timescales, though gains for decadal trends remain modest. 

The synthetic gravity observations were also used to assess the closure of the global energy budget. Historical gravimetry, altimetry, and Argo data yield global mean ocean heat uptake (GOHU) accuracy of 0.2–0.3 W/m² (2003-2015). With VADER-filtered simulations, GRACE-C-like missions contribute up to 0.19 W/m² uncertainty, while NGGM and MAGIC improve this by 30–40%, achieving ~0.12–0.13 W/m² accuracy. They also enhance the stability and temporal consistency of GOHU retrievals. Regionally, NGGM and MAGIC outperform GRACE-C by up to 80% in recovering ocean heat content changes at mid-latitudes (30–60° N/S). Slightly better results are obtained with NGGM due to the use of mission error covariance information in the VADER filter. NGGM and MAGIC recover mean and temporal variations in ocean heat uptake at regional scales with up to 50% higher accuracy than GRACE-C.
The NGGM and MAGIC missions will substantially enhance the accuracy, spatial and temporal resolution of gravity-based observations of sea level changes and its drivers. These improvements strengthen global climate assessments, support the evaluation of mitigation policies, and improve climate model validation. In particular, sustained and redundant monitoring of ocean heat uptake would provide an early and robust indicator of changes in radiative forcing, preceding detectable stabilization of global temperatures by several decades. Improved characterization of regional heat-uptake pathways also enhances projections of sea level rise, marine heat extremes, and ocean circulation changes, supporting climate risk management across coastal, marine, and ecosystem applications.

How to cite: Ferrari, R., Pfeffer, J., Bouih, M., Meyssignac, B., Blazquez, A., and Daras, I.: Impact of NGGM and MAGIC on Sea Level and Energy Budgets Closure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18337, https://doi.org/10.5194/egusphere-egu26-18337, 2026.