Insights into hydrologically-induced deformations of the Earth surface, and particularly of aquifers, are crucial for a better understanding of water cycle dynamics and its interaction with solid earth processes and to provide useful information for the sustainable management of water resources. The high spatio-temporal resolution and millimeter/centimeter-scale accuracy of surface deformation data from satellite geodesy techniques such as Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) make it possible to measure and identify signals related to hydrological forcing. Elastic loading response has been primarily investigated using GNSS surface displacement to infer TWS variation at regional scales. The higher spatial resolution of InSAR measurements has made it possible to identify surface deformation patterns associated with the groundwater storage (GWS) variation of local aquifer systems.

In this work we leveraged Sentinel-1A Multi-Temporal InSAR observations from the European Ground Motion Service (EGMS) to analyse the deformation occurring in an area in North-Western Italy. This region hosts the Po valley, a large alluvial plain in northern Italy characterized by abundance of both surface and groundwater bodies, which are extensively exploited for farming and industrial activities. Recently, changing climatic conditions have imposed additional stress on water resources, culminating into a severe drought in 2022. GNSS data revealed an elastic response to the TWS variation associated to this drought at entire Po basin scale (Pintori & Serpelloni 2023).

We analysed InSAR time series (2018-2022) focusing on an area spanning from the low Lombardian plain to the foothills of the Alps, encompassing terrain that transitions from fine alluvial deposits in the south to coarser fan and glacial deposits in the north and including some main cities and two of the largest Italian lakes. We applied decomposition and clustering techniques in order to extract the signals contributing to the observed deformation and their spatio-temporal features. To identify the possible physical drivers, we compared our results with publicly available precipitation, rivers discharge and water head table piezometric data, and hydro-geological information. We found that different areas respond with different mechanical behaviours to the same forcing. We highlighted localized areas on the piedmont belt which are mainly characterized by a transient multiyear signal of up to 15 mm which results to be strongly correlated with precipitation, uplifting in wet periods and subsiding during drought periods. This is consistent with a poroelastic response which could be attributed to the higher localized concentration of coarse-grained material like gravel and sand in the piedmont belt. We applied models of poroelastic deformation, including, where available, hydraulic head data, to relate the identified poroelastic surface deformation to GWS variation, and characterize the aquifers properties. Outside these areas, the multiyear deformation pattern has a lower amplitude (up to 2mm) and is anticorrelated in time with precipitation, consistently with an elastic loading response. We computed the elastic deformation due to the estimated TWS variation from Pintori & Serpelloni (2023) and found agreement in order of magnitude and temporal trends with InSAR data.

How to cite: Guidi, D., Silverii, F., Polcari, M., and Rivalta, E.: InSAR for the characterization of climate-related processes in Northwest Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6683, https://doi.org/10.5194/egusphere-egu25-6683, 2025.

Global warming and its associated impacts on sea level rise pose increasing risks to coastal regions. However, regional sea level changes are influenced by local factors, including land subsidence and localized climatic phenomena, which can exhibit significant variations that exceed the global average. As the world's largest inland sea, the Black Sea water level changes are driven not only by global climate processes but also significantly influenced by river runoff, with almost one-third of the entire land area of continental Europe draining into it, making it a critical factor in sea level variations. This study investigates long-term and seasonal variations in Black Sea water level and basin runoff by integrating satellite altimetry data with in situ hydrological observations, spanning 1993-2024. The results indicate a long-term sea level rise of 3.7 ± 0.38 mm/year for the Black Sea, with the winter season showing a notably higher trend of 3.89 ± 0.38 mm/year compared to other seasons. By investigating the relative contributions of steric (thermal expansion and salinity changes) and mass-related sea level changes, corrected for surface loading deformation, this study provides insights into the mechanisms driving regional sea level variability and the broader hydrological responses of Black Sea surrounding basins.

Keywords: the Black Sea; steric sea level rise; river discharge; Altimetry; loading deformation

How to cite: Wen, Z., Saemian, P., Sun, W., and Tourian, M. J.: Assessing Long-Term and Seasonal Drivers of Black Sea Level Rise: Runoff and Loading Deformation (1993–2024), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4691, https://doi.org/10.5194/egusphere-egu25-4691, 2025.

Global warming and other climate change influences are leading to major changes in the global hydrological cycle. The response of the Solid Earth to water mass transfers causes crustal deformations and gravity field temporal variations that can be monitored by space geodesy. It is challenging to identify the climate change signature contained in the time series and to separate the different contributions from various spatial and temporal scales. In this study, we use more than 20 years of GNSS and GRACE time series to analyse hydrological loading signal in two different areas that are highly sensitive to climate change. The Svalbard archipelago in the Arctic is one of the fastest warming locations in the world. We use seasonal analysis and comparison with satellite altimetry and in-situ datasets to distinguish current ice melting from the solid Earth’s response to past events (GIA, LIA). South America and the Amazon basin, home to some of the world’s largest rivers, have recently experienced severe drought and extreme floods. The hydrological loading shows huge annual variations superimposed on interannual variations linked to extreme events. It is therefore essential to use high-performance analysis methods to separate the part of the observed signals associated with climate change from the well-known seasonal trends. To assess their reliability and interpretation, the results are discussed in relation to complementary datasets and models.

How to cite: Nicolas, J., Tafflet, A., Boy, J.-P., Baltzer, A., and Verdun, J.: Solid Earth response to climate change in Svalbard and South America using geodetic observations of hydrological loading, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10069, https://doi.org/10.5194/egusphere-egu25-10069, 2025.

The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GFO) gravity observations have significantly improved models of the terrestrial water cycle globally. However, GRACE-assimilated models of terrestrial water storage still show differences amongst the models, and studies to determine their ability to predict the state of terrestrial water storage in different regions are ongoing. This paper uses Global Positioning System (GPS) data to assess two global GRACE-assimilated datasets: GLWS2.0 and CLSM-DA. From 2004 to 2019, the mean annual amplitude of water thickness of these datasets differs by more than 25 mm over 40% of the land area. Additionally, the models predict the timing of maximum water storage with difference in phase of 30-days across 50% of their domain. We compare the modeled hydrological loading vertical displacements predicted from these models with GPS uplift data as a measure of the model quality. We cluster 5,983 global GPS stations, each with at least three years of daily data, based on river basin borders. This segmentation allows for better detection of how hydrological conditions, e.g. precipitation patterns, soil characteristics, etc., and model calibration (applied in each river basin) influence the model-GPS agreement.    

Our comparison demonstrates that compared to GLWS2.0, CLSM-DA generally agrees better with GPS and GRACE data across more river basins. We find that the 100-300 mm larger annual water variation of CLSM-DA to GLWS2.0 accounts for CLSM-DA’s better agreement with GPS in Africa, Southeast Asia, and some parts of South America. For regions like the Western United States and Eastern Europe, where the two models propose a similar range of annual water variation, the 30-60 days phase delay of CLSM-DA improves its alignment with GPS. Our findings highlight the need for regional improvement in these models, particularly in areas where they significantly deviate from GPS observations of the terrestrial water variation.

How to cite: Abbaszadeh, M. and van Dam, T.: Assessment of two GRACE-assimilated terrestrial water storage datasets across 44 river basins using GPS observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18131, https://doi.org/10.5194/egusphere-egu25-18131, 2025.

While geodetic observations are now commonly used to retrieve terrestrial water storage changes at regional or watershed scales, their application at the local scale, such as individual lakes, remains limited due to spatial resolution constraints and the lack of onsite observations, especially in remote areas. This study investigated the deformation field and water storage changes at Qinghai Lake, China from January 2016 to December 2022 by integrating data from five Global Navigation Satellite System (GNSS) stations and Interferometric Synthetic Aperture Radar (InSAR) images. We observed that the area surrounding Qinghai Lake exhibited an overall subsidence trend with rates ranging from -2.89 to -0.30 mm/yr between January 2016 and August 2019. However, from September 2019 to December 2022, this trend reversed to an uplift with rates ranging from 2.20 to 4.89 mm/yr. This shift in deformation direction is largely attributed to changed precipitation influenced by large-scale atmospheric circulation. Furthermore, independent component (IC) analysis of the deformation field shows that the first two ICs accounted for 77.36% and 16.67% of the data variance, representing loading signals due to regional background hydrological loading and lake water storage gains, respectively. We then reconstructed the loading deformation associated with lake dynamics and inverted the lake water storage changes, which demonstrated high consistency (r=0.86) with lake volume changes estimated from satellite water level measurements, indicating that increases in lake surface water constitute a significant portion of the water storage increases.

How to cite: Zhu, H., Chen, K., Li, M., Hu, S., Zhang, G., Kuang, X., Cui, W., and Zhang, S.: Terrestrial water storage changes of Qinghai Lake on the Tibetan Plateau from joint inversion of GNSS and InSAR data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4027, https://doi.org/10.5194/egusphere-egu25-4027, 2025.

Terrestrial Water Storage (TWS) is a vital component of the Earth's hydrological and climate systems, influencing water resource management and ecosystem dynamics. However, current TWS estimation techniques, such as those derived from global spherical harmonics, suffer from low spatial and temporal resolutions, limiting their application for regional studies. To address this issue, this study proposes a framework for regional TWS estimation based on Spherical Cap Harmonic Analysis (SCHA) applied to GNSS-derived vertical crustal displacements. The proposed methodology employs the remove-restore strategy to isolate the hydrological load within the cap. First, mass redistribution signals from outside the cap are removed using GRACE (Gravity Recovery and Climate Experiment) data. The GNSS-derived residual vertical displacements are then expanded into SCHA coefficients, incorporating modified load Love numbers that account for the spherical cap geometry. The modified load Love numbers ensure a physically consistent representation of the Earth's elastic response within the cap boundary. The estimated coefficients (residual) are used to recover residual TWS variations, after which the removed external contributions are restored. The proposed approach provides enhanced spatial resolution and accuracy compared to traditional global spherical harmonics by tailoring the analysis to the geometry of a spherical cap.

Both simulated and observed GNSS data from a network of stations across Brazil, covering diverse hydrological regimes—from the Amazon Basin to the semi-arid Northeast—are analyzed to validate this approach. The results reveal spatial and temporal patterns of TWS changes, demonstrating agreement with independent GRACE estimates and hydrological models. These findings emphasize the ability of SCHA-based regional analysis to capture local-scale hydrological processes with higher precision than global methods. Furthermore, this study highlights the potential of SCHA to complement GRACE datasets in regions with dense GNSS observational coverage and advances geodetic techniques for hydrological monitoring.

How to cite: Ferreira, V.: Regional Terrestrial Water Storage Recovery Using Spherical Cap Harmonics from GNSS-Derived Vertical Displacements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7627, https://doi.org/10.5194/egusphere-egu25-7627, 2025.

Aquifer classification plays a pivotal role in understanding groundwater dynamics and informing sustainable water resource management, especially in regions under significant stress from over-extraction. This study presents a novel remote sensing-based methodology for classifying aquifers represented by monitoring wells within the study area. The approach integrates stress-strain analysis, incorporating deformation data derived from Interferometric Synthetic Aperture Radar (InSAR) and groundwater head measurements from monitoring wells, utilizing advanced deep-learning techniques. Groundwater data from piezometric wells are utilized to create image-based representations of hysteresis loops derived from stress-strain diagrams, capturing aquifer deformation under varying drawdown and recovery cycles. A convolutional neural network is applied to extract high-dimensional features characterizing aquifer response dynamics. Principal component analysis is then employed to reduce dimensionality, highlighting the most significant features driving classification. Finally, unsupervised clustering methods are used to group piezometric wells, revealing distinct aquifer types and deformation patterns. The proposed methodology is tested in three hydrologically and geologically diverse regions of Iran: Shabestar, Urmia, and Neyshabur Plains. In the Shabestar and Urmia Plains, located near the hypersaline Lake Urmia, intensive groundwater extraction has severely strained local hydrological and ecological systems, contributing to declining lake levels and increased stress on water resources. Similarly, in the Neyshabur Plain in northeastern Iran, characterized by its arid to semi-arid environment and intricate geological features, excessive groundwater use has led to significant aquifer depletion and land subsidence. The proposed approach effectively identifies different aquifer types, analyzes the balance between elastic and inelastic deformation, and determines aquifer responses to varying degrees of groundwater extraction. By integrating InSAR-based deformation monitoring of ground surface with advanced deep learning techniques, the study provides a comprehensive framework for aquifer system characterization. The findings are particularly valuable for regions with scarce geological and hydrological data, offering insights to guide sustainable groundwater management practices, mitigate environmental degradation, and support effective decision-making. 

How to cite: Khodaei, B., Hashemi, H., and Kompanizare, M.: A Novel Approach to Aquifer Classification Using Hysteresis Loop Analysis and Deep Learning for Sustainable Groundwater Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6896, https://doi.org/10.5194/egusphere-egu25-6896, 2025.

The Earth's surface undergoes deformations due to temporal variations in the distribution of atmospheric, hydrological and oceanic mass loads on the lithosphere. These deformations can be observed using Global Navigation Satellite System (GNSS) data where seasonal variations are particularly prominent in GNSS height time series.

While continental-scale water mass redistributions can be captured by Gravity Recovery and Climate Experiment/Follow On (GRACE/FO) or global hydrological models, surface water storage changes e.g. those caused by rivers and lakes cannot be resolved. However, GNSS timeseries may contain these small-scale surface water loading signals especially when located close to water bodies. Correctly representing such close-range, subgrid-scale loading signals is important for interpreting GNSS displacements, in particular when the goal is validating hydrological models.

In this study, we compiled daily time series from 326 GNSS stations jointly with water level observations along the Rhine river in the the Eifel area, North West Europe. The GNSS time series underwent careful post-processing including offset corrections and outlier removal. We identified a statistical relationship between the annual GNSS amplitudes and the stations' distance from the Rhine River. After applying blind source separation techniques, including Singular Spectrum Analysis (SSA) and hydrological model-based corrections (using the Community Land Model version 5, CLM5, at daily resolution) to isolate large-scale common mode signals from the GNSS observations, the correlation between the residual GNSS signals and Rhine river level variations improved. We further inverted for regional elastic Earth parameters based on a half-space infinite elastic Earth model to estimate the surface water induced vertical displacements. The results demonstrated that surface water loading could account for a considerable fraction of the vertical displacement observed at GNSS stations near the riverbanks on daily to monthly timescales.

How to cite: Zhang, L., Karegar, M., and Kusche, J.: GNSS observations of the surface water storage-induced displacements in the Eifel area, NW Europe: the influence of the Rhine river, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10956, https://doi.org/10.5194/egusphere-egu25-10956, 2025.

Precise gravity measurements have been consistently collected in Fennoscandia and Canada since the 1960s and 1990s, respectively, using relative gravimeters and later employing absolute gravimeters (e.g., FG5 and A10 absolute gravimeters) to establish gravity reference system and study temporal changes in gravity, e.g. associated with ongoing glacial isostatic adjustment (GIA). In this study, we utilized monthly data from GRACE and GRACE Follow-on, spanning 2003 to 2023, to estimate temporal variations in surface gravity changes, their relationship with land uplift rates, and to determine the upper mantle density associated with viscous mass flow in the mantle. The main focus of this paper is Canada; however, the results will be compared with our previous studies in Fennoscandia. We used the ICE-6G_D land uplift model for Canada and the NKG2016LU regional land uplift model for Fennoscandia for this purpose. The satellite gravimetry results were compared with terrestrial absolute gravity observations collected at 43 stations across Canada and Fennoscandia, respectively.

The results derived from GRACE and GRACE Follow-on data show that the ratio between surface gravity and height changes is −0.152 ± 0.010 μGal/mm in Canada and −0.156 ± 0.016 μGal/mm in Fennoscandia aligning closely with findings from terrestrial gravity observations. These values correspond to upper mantle densities of approximately 3736 ± 239 kg/m³ and 3641 ± 382 kg/m³ in Canada and Fennoscandia, respectively. In addition, the results were combined with terrestrial absolute gravimetry results. These findings highlight the importance of satellite gravimetry data and are crucial for GIA modeling and the Earth’s interior parameters.

How to cite: Bagherbandi, M., Sjöberg, L. E., Foroughi, I., and Abd El-Gelil, M.: Investigating Surface Gravity and Height Variations due to Glacial Isostatic Adjustment: Insights from GRACE and GRACE-FO Data in Fennoscandia and Canada, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2238, https://doi.org/10.5194/egusphere-egu25-2238, 2025.

The Greenland ice sheet is melting at an increasing rate and is predicted to have a large contribution to sea level change by 2100. Future climate over Greenland, which determines the ice sheet’s surface melt and marine-terminating glacier retreat, represents a major source of uncertainty for Greenland ice sheet evolution (ISMIP6). In this study, we explore the Greenland ice sheet contribution to sea level change from 1960 to 2100 and quantify how uncertainties in projected climate change and Earth rheological structure shape global and local sea level changes and their spatio-temporal variability.

Ice load history is provided by simulations following the ISMIP6 protocol. To project regional sea level changes, we employ two different gravitationally self-consistent sea level models. We use the pseudo-spectral sea level model described in Gomez et al. (2010). To test the sensitivity of projections to surface resolution and Earth structure, the experiments are repeated with the finite volume sea level model SEAKON (Latychev 2005) that includes 3D variations in Earth structure and grid refinement capabilities to reach ~5 km surface resolution over Greenland.

Results highlight the spatial variability of projected sea level for communities along the Greenlandic coastline, and contrast local changes to farfield sea level rise for Pacific Islands. With a spread of -1.00m to -2.96m sea level change by 2100 around Ilulissat, West Greenland, our results are up to three times the value provided by the NASA IPCC sea level tool (-0.8m) and emphasize the need for more studies addressing local sea level changes.

How to cite: Haubner, K., Gomez, N., Lucas, E., Rahlves, C., Richter, K., Nisancioglu, K. H., and Born, A.: Local sea level changes due to Greenland ice sheet mass changes from 1970 to 2100, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8675, https://doi.org/10.5194/egusphere-egu25-8675, 2025.

Understanding the past evolution of the Greenland ice sheet (GrIS) is important for accurately simulating its future behavior and thus its contribution to global mean sea level rise. Data and models related to glacial isostatic adjustment (GIA) have provided critical constraints on past GrIS evolution. These models are necessary to interpret a variety of data, including past sea-level changes and geodetic observations of current land motion and gravity changes. In all studies to date, paleo sea level data from southern Greenland have presented the greatest challenge to GIA models. Poor data-model fits in this region have led to the hypothesis of glacially-induced faulting  during periods of rapid ice loss (with associated tsunami hazard).

In this study, we seek to determine if quality fits to the southern Greenland relative sea level (RSL) data can be obtained by improving the GIA model and exploring the parameter space more fully than past efforts. Specifically, we consider two recent advancements in model development: new 3-D models of earth viscosity structure based on the joint inversion of regional geophysical datasets, and GrIS reconstructions output from a leading glacial systems model. The improved 3-D earth models result in a larger RSL fall compared to past 1-D earth modelling and so that amplitude of the measured signal can be accurately simulated at most sites in southern Greenland. However, the rate and timing of RSL fall are generally too late and too slow to match many of the mid-Holocene sea-level index points. We seek to improve this aspect of the model fits by varying the ice history model. A two-step approach is used: (1) manually adjust the timing and rate of ice retreat in a chosen model to identify if plausible variations in these aspects can capture RSL data, and (2) assuming (1) is satisfied, seek to produce a glaciologically consistent ice history by varying parameters within the glacial systems model (e.g., climate forcing). In this presentation, we will provide an update on the status of our sensitivity analysis and the implications for glacially-induced faulting and the ice sheet response to the Younger Dryas cold interval.

How to cite: Lepipas, A., Ajourlou, P., Milne, G., Tarasov, L., and Woodroffe, S.: Modelling sea-level reconstructions from southern Greenland: Implications for glacially-induced faulting and the response of the ice sheet to the Younger Dryas cold interval, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14180, https://doi.org/10.5194/egusphere-egu25-14180, 2025.

The Antarctic Peninsula is uplifting rapidly due to the isostatic response to ice sheet unloading since the Holocene. Understanding the timing and rate of this process is crucial for addressing several key research questions: (1) exploring the elastic interactions between the mantle and lithosphere to improve Glacial Isostatic Adjustment (GIA) models, (2) assessing the contribution of the Antarctic ice sheet to Holocene global sea level rise, and (3) investigating the modern response of Antarctic ice sheets to climate change, helping to identify high-impact research areas for polar science.
We focus on stepped coastal terrace staircases formed at the Horseshoe Island, Marguerite Bay, west Antarctic Peninsula. We used low altitude UAS aided SfM mapping to measure the horizontal and vertical geometry of stepped terraces and deployed absolute dating methods (luminescence and radiocarbon) to establish their formation timelines for the east (Gaul Cove, #6 dates), north (Sally Cove, #2) and west (Lystad Bay, #2) of the island.  
The field observations and achieved data explained the formation mechanisms and evolutionary steps of the terraces and pinpoint (1) the timing of deglaciation of the Island, (2) reconstruct a RSL curve for the Holocene and (3) variations in temporal and spatial vertical uplift rates. Our reconstructed RSL(s) fit the geometry of model curves proposed by Peltier (2004) and Whitehouse (2018) . However, there is an apparent discrepancy between our results and published estimations from coastal record of Antarctic Peninsula. This raises questions on the accuracy of dating or interpretation of the results for studies on stepped-coastal terraces. This presentation aims to represent analytical data to discuss these critical issues.
This study was carried under the auspices of Presidency of The Republic of Turkey, supported by the Ministry of Industry and Technology, and coordinated by TUBITAK MAM Polar Research Institute within the TAE-VIII expedition and supported by TUBITAK 122G261 grant.

How to cite: Erturaç, M. K., Şahiner, E., Kandemir, R., Okur, H., Salman, İ., Hasözbek, A., Öncel, M. S., Qin, J., and Akçar, N.: Timing of glacier retreat and spatio-temporal variations in the vertical deformation rate of the Horseshoe Island, Marguarite Bay, west Antarctic Peninsula, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6751, https://doi.org/10.5194/egusphere-egu25-6751, 2025.

Understanding the behaviour of polar ice sheets during past warm intervals provides critical constraints on their potential response to future warming scenarios. The Last Interglacial (LIG, ~125 ka) is a particularly valuable analogue, characterised by temperatures around 1-2°C above pre-industrial levels and global mean sea level 6-9 m higher than today. This study presents a comprehensive analysis integrating relative sea level (RSL) observations with numerical modelling to reconstruct ice volume fluctuations during this key interval.

A fundamental challenge in reconstructing past ice volumes from RSL records lies in deconvolving the spatially heterogeneous solid Earth deformation signals associated with Glacial Isostatic Adjustment (GIA) from the eustatic component. To address this, we have developed and implemented a high-resolution numerical model that explicitly accounts for GIA effects during the LIG. The integration of this model with a spatially extensive database of well-dated RSL indicators enables robust constraints on polar ice sheet volume changes.

This study utilises a GIA model, incorporating rotational effects, in order to predict variations in both space and time with respect to RSL during the LIG. The aim of this study is to evaluate the dependence of these predictions on penultimate glacial maximum ice geometries, by conducting a comparison with global RSL observations. The conclusions of this study serve to further the understanding of ice sheet response to warming, and thus inform future projections of sea level.

How to cite: Okuno, J. and Irie, Y.: Constraining Last Interglacial ice sheet volumes through GIA-corrected sea-level reconstructions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1903, https://doi.org/10.5194/egusphere-egu25-1903, 2025.