Under global warming, the intensification of the global water cycle has led to an increased frequency of alternating drought and flood events, making such extreme conditions seemingly the new normal. These frequent events are causing more pronounced fluctuations in terrestrial water storage anomalies (TWSA). In this study, we introduce a novel yet straightforward metric—the standard deviation of TWSA (STD_TWSA)—to quantify these fluctuations and gain insight into changes in the global water cycle. TWSA estimates from the Center for Space Research (CSR) are used to calculate STD_TWSA, and the results are compared with fluctuations in precipitation anomalies (PA) and temperature anomalies (TA) to explore their connections with TWSA fluctuations. Our results indicate that 55.7% of the global land grid cells show a significant increase in TWSA fluctuations, while only 2.2% exhibit a noticeable decrease. Among the 40 large global basins analyzed, 11 basins display significant upward trends in STD_TWSA, while only two basins show significant downward trends. Furthermore, 36 of the 40 basins show a positive correlation between STD_TWSA and PA fluctuations, with 17 basins exhibiting a statistically significant correlation. Additionally, 6 basins show a significant positive correlation between STD_TWSA and TA fluctuations, which corresponds with those basins that also have significant correlations between STD_TWSA and PA fluctuations. This study provides valuable insights into global TWSA fluctuations and enhances our understanding of the impacts of global climate change on the water cycle.

Figure 1. A simple schematic diagram demonstrates the large (red) and small (blue) fluctuations of TWSA in one basin.

Figure 2. Spatial distribution of global grid trends in 17 STD_TWSA series from 2002 to 2022. α < 0.05 is used to assess the significance of the estimated trend, and the grey shading indicates that the trend does not pass the significance test. It should be noted that the unit is expressed as 10^-1mm considering the small trend term.

Figure 3. Time series of STD_TWSA, STD_PA, and STD_TA in 40 global large basins. The detrended and deseasonalized TWSAs are used to derive STD_TWSA.

Figure 4. The histograms of STD_TWSA, STD_PA, and STD_TA estimates from 2002 to 2022 in global 40 large basins, and the scatter plots between STD_TWSA and STD_PA/STD_TA.

Figure 5. Basin-scale Pearson correlation coefficients between STD_TWSA-STD_PA (a) and STD_TWSA-STD_TA (b) from 2002 to 2022.

How to cite: Zhong, Y., Wang, Y., Kusche, J., and Wu, Y.: Global river basin water storage fluctuations and their connection to precipitation, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-37, https://doi.org/10.5194/gstm2024-37, 2024.

The Gravity Recovery And Climate Experiment (GRACE) satellite mission has significantly advanced the remote sensing of total water storage anomalies (TWSA) from regional to continental scales. Building on this foundation, the GRACE Follow-On (GRACE-FO) mission, launched on 22 May 2018, continues to provide valuable data. However, the combined observational period of GRACE and GRACE-FO is limited to two decades of monthly data, with a one-year gap between the missions. This relatively short record limits the ability to observe global and regional climate trends over the long term, which is essential for studies on drought characterization and long-term climate change patterns.

To overcome this limitation, using global hydrological, atmospheric, and reanalysis models, we have developed a new gridded TWSA dataset that expands the temporal coverage of GRACE(-FO) observations back to 1980. We employed various machine learning algorithms to combine these models and reconstruct TWSA, including Multivariate Linear Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) together with some ensemble methods. Comparisons were made with GRACE data during the GRACE period (Apr 2002 to Dec 2012) with a high-resolution Satellite Laser Ranging (SLR) TWSA product before the GRACE period (1992-2002).

Our findings demonstrate significant improvements compared to the basic approaches, highlighting the necessity for advanced and sophisticated methods to reconstruct TWSA accurately across diverse regions and climates. GPR and MLR exhibited superior performance among the tested methods, while SVM and DT displayed poorer performance in most basins. This research presents a new approach for reconstructing long-term total water storage anomaly fields prior to the GRACE period (i.e., before 2002). The newly developed dataset substantially extends the timeline of TWSA observations, providing valuable insights into long-term variations in Earth's water storage.

How to cite: Saemian, P., Tourian, M. J., Douch, K., and Sneeuw, N.: Developing a Long-Term Global Dataset of Water Storage Anomalies Using GRACE and Model-Based Estimations, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-65, https://doi.org/10.5194/gstm2024-65, 2024.

Data from both the Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) and displacements of Global Navigation Satellite Systems (GNSS) stations have been shown to provide a valuable record of variations in global terrestrial water storage. However, both satellite gravimetry and GNSS station displacements have trouble distinguishing between different components of water storage. The effective horizontal resolution of GRACE/GRACE-FO spreads concentrated surface water storage over a large area increasing the difficulty of determining the contribution of each component of water storage, and the displacement of any single GNSS station can be caused by a variety of physical phenomenon. To improve our understanding of water storage estimates from inversions of GRACE/GRACE-FO and GNSS station displacements here we use the monthly lake water storage anomalies of 287 natural lakes and 704 reservoirs, larger than 100 km², between October 1992 to October 2020 derived from satellite altimetry and Landsat imagery from the Global database of Lake Water Storage to predict the elastic deformation of the solid Earth and mass change observed by GRACE caused by these changes in lake water storage (Yao et al., 2023). We estimate the least-squares seasonal oscillation of each water body and combine it with a linear interpolation to construct a continuous monthly record across the study period. We estimate that our final product accounts for ≈68% and ≈66% global natural lake and reservoir storage respectively, with the worst coverage above 55°N where the temporal coverage of lake altimetry is poor. When removed from GRACE/GRACE-FO terrestrial water storage estimates the affected mascon’s WRMS is reduced by up to ≈38 cm equivalent water thickness (86%), with an average RMS reduction of 2 cm (11%). The elastic vertical displacements due to changes in lake and reservoir storage significantly affect 1885 GNSS stations and reduce the WRMS of vertical displacement timeseries by up to ≈28 mm (69%) with an average improvement of ≈1.2 mm (7%). GNSS stations experience a range of vertical displacement due to changes in lake water storage between 47 mm of uplift and 27 mm of subsidence with the largest variations found for stations around the Caspian Sea, the Great Lakes, and the Rift lakes of East Africa.

How to cite: Gaastra, K., Argus, D., Landerer, F., and Ellmer, M.: Elastic Displacement of the Solid Earth and GRACE/GRACE-FO response to variations in Global Lake and Reservoir Storage, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-31, https://doi.org/10.5194/gstm2024-31, 2024.

This study aimed to independently evaluate precipitation and terrestrial water storage (TWS) products by analyzing hydrological drought characteristics across various climate zones. Hydrological drought characteristics were estimated using two TWS and two precipitation products as the hydrological drought recovery time (DRT). The TWS data were obtained from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO). The Global Gravity-based Ground Product (G3P) spherical harmonic solution and JPL mascon solution (RL06) were utilized as GRACE/GRACE-FO TWS products. Precipitation data were obtained from the Global Precipitation Climatology Center (GPCC) Full Data Monthly Product version 2022 and the Global Precipitation Climatology Project (GPCP) version 3.2 monthly analysis product. Köppen-Geiger Climate Classifications were utilized in the presentation of the results of the study. Two methods were used to estimate DRT: (1) storage deficit, using the detrended TWS anomaly (dTWSA), and (2) required precipitation amount, using the detrended smoothed precipitation anomaly (cdPA) and the dTWSA. The storage deficit method is based on the calculation of the residuals of the dTWSA from its climatology. The required precipitation amount method is based on the linear relationship between the cdPA and dTWSA. The end of a hydrological drought was determined when the residuals of the dTWSA turned positive for the storage deficit method and when observed precipitation amount exceeded absolute required precipitation amount for the required precipitation amount method. Results indicated that the mean DRT estimations from GPCC and GPCP were similar. However, the mean DRT obtained from G3P was approximately three months shorter than that from the JPL mascon. For precipitation products, the results of the consistencies in mean DRT estimations between the two methods were similar to those of the mean DRT estimations, showing no significant difference between GPCC and GPCP. Conversely, the consistency in DRT estimations obtained from G3P was 5.0% higher than the consistency in DRT estimations obtained from JPL mascon. Among climate zones, the equatorial zone had the shortest DRT estimation (~10 months) and the highest consistency (~98%). In contrast, the polar zone had the most extended DRT estimation (~16 months) and the lowest consistency (~75%). Overall, findings demonstrate that GPCC and GPCP are closely aligned in terms of mean DRT estimations and consistency. Additionally, G3P exhibited slightly more consistent DRT estimations with precipitation products than did JPL mascon. By analyzing hydrological drought characteristics, this study provides a basis for a better understanding of meteorological and hydrological processes as well as assessing the accuracy of the precipitation and TWS products.

How to cite: Çakan, Ç., Yılmaz, M. T., Dobslaw, H., Ince, E. S., Evrendilek, F., Förste, C., and Yağcı, A. L.: Independent Evaluation of GPCC and GPCP Precipitation Products and G3P and JPL Mascon TWS Products, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-35, https://doi.org/10.5194/gstm2024-35, 2024.

We propose a status update on the Level-3 (L3) solution with uncertainties produced by the CNES. The L3 solution is designed to analyze the global water cycle consistently with the conservation of mass. In particular, the L3 solution has been used to estimate glacier mass loss in Arctic regions, Greenland and Antarctica, as well as water mass change in these areas. The ocean mass product and its uncertainty is designed in a consistent way with satellite altimetry products to estimate the Earth’s energy imbalance.

We assess the quality of the solution and its uncertainties with external independent datasets from satellite altimetry observations and in-situ measurements. Comparisons of the mass estimation on large lakes provide insights into glacial isostatic adjustment, showing that the ICE-6G_D model aligns better with the observed trends in North America water volume from altimetry than the mean value of Caron 2018 model. We observe that for most of the lakes the seasonal maximum of the surface water takes place 1 to 2 months before the whole land water observed by GRACE.

How to cite: Lecomte, H., Meyssignac, B., Blazquez, A., and Dhondt, C.: Level-3 CNES ensemble solution, assessment with lake altimetry, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-66, https://doi.org/10.5194/gstm2024-66, 2024.

GRACE and GRACE-FO measurements of terrestrial water storage (TWS) change can help with the management of water resources. This is particularly important for California because of its large population, extensive irrigated lands, limited freshwater resources, and the impact of climate change, such as more frequent and prolonged droughts. California’s TWS signal has a significant spatiotemporal variability related to, e.g., snow in Sierra Nevada, groundwater in Central Valley, and large differences in TWS change magnitude in its northern and southern parts. Thus, it is important to evaluate the ability of GRACE and GRACE-FO to measure TWS in this region accurately. We begin by inspecting differences between seasonal variations in GRACE/GRACE-FO TWS data from SDS L2 data and the JPL, GSFC, and CSR mascon solutions. Then, we use elastic deformation measurements from ~1000 permanent GNSS stations in California to validate the GRACE/GRACE-FO observations of TWS changes. We invert 16 years (2006-2021) of GNSS measurements to estimate a high-resolution (25 km) map of seasonal TWS change in California. We then convert the seasonal TWS map estimated from GNSS to spherical harmonic coefficients and truncate at degree 40-60, commensurate with GRACE/GRACE-FO effective spatial scale. We then compare all GRACE/GRACE-FO TWS solutions to the low-pass filtered GNSS TWS map. This analysis demonstrates that the seasonal TWS map from GRACE/GRACE-FO is precisely a low-pass filtered version of the one from GNSS, highlighting the usefulness of GNSS for evaluating GRACE data. Through this comparison, we discuss the (1) similarity and differences among GRACE/GRACE-FO TWS change solutions, (2) decay of TWS amplitude from L2 solutions due to filtering, and (3) spatial regions where each TWS change solution might suffer from errors. This study offers a unique tool for validation various GRACE/GRACE-FO TWS solutions.

How to cite: Ghobadi-Far, K., Werth, S., and Shirzaei, M.: Evaluation of GRACE and GRACE-FO terrestrial water storage change data in California using GNSS elastic deformation measurements , GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-45, https://doi.org/10.5194/gstm2024-45, 2024.

After enduring severe drought conditions from 2020 to 2022, California experienced an exceptionally wet 2023 water year, driven by a series of atmospheric rivers, unusually frequent tropical storms, and extreme snowfall during the winter months. Statewide precipitation levels significantly exceeded normal averages, with some areas reporting increases of over 100%. Notably, the Sierra Nevada mountains witnessed unprecedented snowfall, with averages reaching approximately 200%, leading to a surge in surface water levels above the average level statewide, potentially reducing the need for groundwater overdrafts for agricultural purposes and allowing for the recharge of aquifers. The Central Valley of California, a major agricultural hub responsible for producing more than a quarter of the food consumed in the US, heavily depends on groundwater from deep confined aquifers for the water used in farming. Years of excessive groundwater extraction have led to significant land subsidence, primarily impacting the San Joaquin Valley and Tulare Basin in the southern part of the Central Valley. Following the extremely wet water year of 2023, questions persist about whether the increased availability of surface water has positively impacted the deep confined aquifers of the Central Valley, which have suffered from severe overdraft for decades. To address this pressing question and decipher the complex recharge dynamics of the Central Valley's deep confined aquifers, we combine water storage datasets from GRACE-Follow On (GRACE-FO), Interferometric Synthetic Aperture Radar (InSAR) based vertical land motion (VLM), measurements of groundwater level, and surface hydrological data. We estimate the recharge volume of both shallow (unconfined) and deep (confined) aquifers during the wet water year of 2023 (Sep 2022 – Aug 2023). We observed a significant rise in groundwater head levels across most wells in the Central Valley, with approximately 599 wells (89%) recording positive rates. A seamless, high-resolution (~75m) spatiotemporal map of vertical land motion (VLM) for the Central Valley has been generated using a mosaic of three overlapping SAR frames from Sentinel-1 satellite images. The VLM map reveals maximum uplift and subsidence rates of 10.4 cm/year and -29.3 cm/year, respectively. We noted that about 58.83% of Central Valley experienced subsidence, while the rest (41.17%) uplifted. By integrating GRACE-FO and hydrological data, we have identified a net gain in groundwater storage amounting to approximately 19.4 ± 4.1 km³ during the 2023 water year. We use head level changes from well measurements and aquifer properties to quantify groundwater storage changes in Central Valley's unconfined (shallow) and confined (deep) aquifers. In addition, we apply VLM data in a 1D poroelastic model to independently estimate groundwater storage change in deep aquifers of Central Valley during the post-drought wet year and compare the results with those from GRACE-FO observations and groundwater well measurements. Our widely applicable approach combines multiple remote sensing technologies with ground-based observations to monitor groundwater dynamics accurately. The findings of this research enhance our understanding of how deep confined aquifers respond to transient, extreme weather phenomena like atmospheric rivers and unusually severe winter events, which are expected to become more common with climate change.

How to cite: Sadhasivam, N., Werth, S., Carlson, G., and Shirzaei, M.: Did the Record-Breaking Rains of 2023 Revive Central Valley's Deep Aquifers?, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-69, https://doi.org/10.5194/gstm2024-69, 2024.

GPS measurements of solid Earth's displacements are bringing a better understanding of the water cycle in the Pacific Mountain system of the western U.S.in particular on how water processes transfer water between storage reservoirs through the season.  In this study, we estimate change in water in the mountains of California, Oregon and Washington every 10 days with an accuracy of 0.1 mm and a spatial resolution of about 75 km.

In the 2023 rainy season, big storms dumped 1.5 m of snow and water on the Sierra Nevada.  During the 1st sequence of atmospheric rivers in Jan 2023, subsurface water increased by 0.2 m and 0.25 m of snowpack formed, accounting for most of 0.5 of water dumped. During the 2nd sequence of atmospheric rivers in Mar 2023, snowpack increased by 0.25 m but subsurface water held constant, because Earth's surface was either frozen or saturated. As the snow melted from Apr to Jun, subsurface water increased by 0.3 m.  During the 12 months of the water year from Oct 2022 to Sep 2023, subsurface water increased by 0.5 m, 1/3 of total precipitation for the year.

To estimate water change in the southern Central Valley, where there are few GPS sites recording elastic deformation, we added GRACE gravity data to execute a joint inversion.  GRACE gravity resolves water change with coarse spatial resolution, but GRACE resolves change in water in southern Central Valley given that GPS determines water change in the mountains surrounding the Valley.  We estimate that Central Valley groundwater increases slowly by 0.5 m from Jan 2022, the time of the 1st atmospheric river to Jul 2023, the time Sierra Nevada snow has melted.  We find that in the southern Central Valley, groundwater increase slightly exceeds cumulative precipitation. We postulate that this is because significant water is moving from the Sierra Nevada to the Central Valley deep underground (mountain block recharge).  It is believed to take decades to centuries for groundwater to flow from the Sierra Nevada to the Central Valley, but an increase in fluid pressure in the Sierra Nevada could move groundwater underground perhaps over tens of days.

In conclusion, GPS and GRACE data are characterizing spatial and temporal fluctuations in water storage and how the water cycle transfers water between different reservoirs.

How to cite: Argus, D., Matthew, S., Hilary, M., Borsa, A., Lau, N., Gaastra, K., Sarfaraz, A., Marin, G., David, B., Felix, L., Payton, G., and Reagar, J. T.: The Big Soak Part 2:California's Big 2023 Storms Replenish Groundwater in California's Central Valley, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-9, https://doi.org/10.5194/gstm2024-9, 2024.

Satellite observations of Earth's gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission offer a unique dataset for analysing terrestrial water storage and effectively closing the continental water balance. Comparing terrestrial water storage (TWS) anomalies from GRACE with in-situ groundwater level measurements is crucial for understanding how groundwater contributes to the overall water retention. In this study, monthly changes in TWS anomalies (ΔTWS) from GRACE are compared with changes in groundwater storage (ΔGWS) obtained from in-situ measurements. The analyses are performed for various aquifer systems and different hydrodynamic zones across Poland.

The analysis of correlations between GRACE-based ΔTWS and in-situ ΔGWS indicates that, while the magnitude of ΔTWS is greater than that of in-situ ΔGWS, a strong relationship exists between these two quantities in alluvial aquifers and in systems with rapid water exchange, such as fractured and karst aquifers. In contrast, the porous reservoirs in postglacial formations with a thick vadose zone - typical for a large part of the area of Poland - exhibit a weak correlation between ΔTWS and in-situ ΔGWS. This indicates that the standard method of calculating ΔGWS as the difference between ΔTWS-GRACE and ΔTWS from the GLDAS (Global Land Data Assimilation System) model should be revised to account for the complexities of these aquifer systems.

Since ΔSWS-GLDAS (Soil Water Storage) effectively captures changes in water content in the vadose zone (ΔVZ), and ΔVZ is a major component of ΔGWS in alluvial aquifers, calculating ΔGWS in these areas as a difference between ΔTWS-GRACE and ΔSWS-GLDAS might be inaccurate. Our results indicate that ΔGWS for alluvial systems should be estimated based solely on ΔTWS-GRACE data. In turn, for aquifer systems with a thick vadose zone (above approximately 2 meters), the ΔGWS should be estimated using the difference between ΔTWS-GRACE and ΔSWS-GLDAS.

We also use a balance approach to determine ΔTWS, employing precipitation data (P) from the European E-OBS database, evapotranspiration (ET) from the Simplified Surface Energy Balance database, and river discharge (Q) from in-situ measurements, by calculating ΔTWS = P − ET − Q. The results demonstrate that from 2009 to 2023, there has been a general decrease in GWS in Poland, with an average rate of -1.4 mm/year. The highest downward trend is observed in the lowland area in central Poland and in the southeast part of the country, with a rate of -1.7 to -2.2 mm/year. In the coastal zone and parts of northeastern Poland, the GWS decline is smallest (from 0 to -1 mm/year). The results of this study suggest that for deeper aquifer systems, GWS accounts for about 76% of the total TWS, whereas for alluvial systems, it is nearly 100%.

Satellite gravimetry complements in-situ observations and model data by independently measuring changes in GWS and offering a continuous spatial view of these variations. Combining remote sensing data with the water balance method promises high-resolution estimates of GWS changes, essential for effective groundwater resource management.

Key words: GRACE, groundwater, terrestrial water storage

How to cite: Solovey, T., Śliwińska-Bronowicz, J., Janica, R., and Brzezińska, A.: Assessment of the effectiveness of GRACE observations in monitoring groundwater in Poland, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-5, https://doi.org/10.5194/gstm2024-5, 2024.

Extreme precipitation and flooding events are are becoming more common in many places worldwide necessitating developing a thorough understanding of this phenomena and implementing sustainable management scenarios of these added water resources. One of those settings is the source areas of the Nile River. Twice (1998-2003; 2019-2022) in the last two decades, high precipitation over the Nile River source areas has led to flooding in northern and central Sudan, the filling of Lake Nasser (LN), and the diversion of the overflow to depressions in the plateau bounding the Nile River from the west, where it is lost to evaporation. We adopted an integrated approach using temporal GRACE and GRACE-FO data, precipitation, and continuous rainfall-runoff models (Soil Water Assessment Tool; SWAT) and global circulation model (GCMs;   CCSM4, HadGEM3, and GFDL-CM4.0) projections to investigate the primary source area contributing to the observed increased in runoff that reached LN in the past two decades, and assessed the impact of climate change on the LN’s runoff throughout the 21^st century. Findings include: (1) the primary contributor to increased downstream runoff reaching LN is the Blue Nile subbasin (BNS), (2) we simulated the BNS runoff  in the 21st century using a calibrated (calibration period: 1965-1992) rainfall-runoff model with CCSM4, HadGEM3, and GFDL-CM4.0 projections as model inputs, (3) the extreme value analysis that we conducted for the projected runoff driven by GCMs’ output indicates extreme floods are more severe in the 21^st-century, (4) based on the predicted median values of stream flows for the 21st century, the stream flows are expected to increase by 2%, 5%, 9%, and 13%, corresponding to 25-, 50-, 100-, and 200-year events, respectively, under RCP 4.5 and 2%, 7%, 11%, 15% and 20% for 10-, 25-, 50-, 100-, and 200-year events, respectively, under RCP 8.5, and (5) one adaptation for the projected 21^st-century increase in precipitation (25%-39%) and flood (2%-20%) extremes is to recharge Egypt’s fossil aquifers during high flood years that are being depleted (2002-2023) by 0.98 km³/yr.

How to cite: Sultan, M., Badawy, A., Yan, E., Abdelmohsen, K., and Torres-Uribe, H.: Use of GRACE, Rainfall-Runoff and Global Circulation Models to Assess the Primary Source(s) of the Nile Floods and their Intensity in the 21st Century, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-30, https://doi.org/10.5194/gstm2024-30, 2024.

The Grand Ethiopian Renaissance Dam (GERD) has become a key focus for hydrological research due to its influence on the Nile Basin. Previous studies have revealed that seepage losses from the GERD reservoir, facilitated by fault networks in the fractured terrain, are underestimated. This study builds on earlier findings by analyzing seepage from the GERD's fourth filling, completed on September 28, 2023, reaching 39.8 km³. By June 18, 2024, the reservoir's volume decreased to 30.2 km³, with a total seepage of 9.6 km³. The analysis suggests that as the reservoir area expands, more fractures and faults are accessed, increasing seepage. These results highlight the necessity of integrating seepage analysis into hydrological models to support sustainable water management and informed decision-making in the Nile Basin. Failure to address these factors could lead to inaccurate hydrological predictions and impede regional cooperation.

How to cite: Abdelmohsen, K., Sultan, M., Yan, E., Save, H., Emil, M., and Famiglietti, J. S.: Assessing Seepage Dynamics of the GERD for Enhanced Nile Basin Water Management, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-86, https://doi.org/10.5194/gstm2024-86, 2024.

Since the deployment of the GRACE mission in 2002, data products from the mission have found multiple applications in studies of the physical earth. Some of the greatest successes amongst these applications have been achieved in the tracking of groundwater dynamics over large terrestrial regions, and to some extent over smaller regions where the underlying geomorphology is reasonably homogenous. Studies over smaller regions with mixed bedrock, soil and aquifer types have encountered challenges, mostly due to the limitations of the spatial resolution of the GRACE data sets. The current literature describes several different schemes to overcome this spatial resolution challenge, many of which employ machine learning models.

This study aims to improve the spatial resolution of GRACE measurements over Ireland by incorporating a number of additional data sets into a deep learning model. In the context of groundwater dynamics monitoring from orbit, the Irish land mass presents a number of unique challenges. The island of Ireland is entirely covered by about eleven GRACE one-degree pixels, most of which straddle a land-sea interface. In addition, the underlying bedrock and aquifer types are highly inhomogeneous with large regions, particularly in the west of the country, consisting of porous limestone aquifers that  have characteristically different groundwater flow dynamics from the gravel aquifers that underly much of the rest of the country.

The geological features of Ireland are well mapped by the Geological Survey of Ireland (GSI) so that the locations of the different aquifer types is reasonably well known. The Irish Environmental Protection Agency (EPA) operates a network of automated wells that record groundwater levels at fifteen-minute intervals.  Both of these primary data sets formed valuable inputs and validation for our models. Other input data sets included meteorological data, MODIS NDVI and LST, GLDAS climate variables, GPM precipitation and SRTM DEM.

The groundwater depth time series from the EPA wells were clustered using a k-means clustering algorithm to classify wells that behaved similarly in response to the natural precipitation and runoff cycles. These classifications aligned well spatially with the aquifer type maps and helped to identify the regions consisting of primarily gravel aquifer types. All data sets were resampled to 0.25-degree resolution using a ConvLSTM deep learning model.  The model was trained on GLDAS climate variables, MODIS NDVI and LST, GPM precipitation, SRTM DEM, EPA well maps and GSI aquifer type maps. The resulting predictions were validated with the EPA well data and as expected, the results exhibited heavily location-dependent correlations with the best agreement occurring in areas with predominantly gravel aquifer types. In these locations, the results suggest that this model can improve the resolution of GRACE measurements to 0.25-degree resolution quite well by capturing the spatiotemporal dynamics of this unusually varied hydrological system. At this resolution, GRACE measurements could form a key component of a more complete groundwater model for Ireland. 

How to cite: Geever, M., O'Farrell, J., Golden, A., O'Fionnagain, D., and Murphy, P.: Improving GRACE spatial resolution to small river basin scale in Ireland using a deep learning model - a twenty-year study., GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-79, https://doi.org/10.5194/gstm2024-79, 2024.

Data Assimilation (DA) of time-variable satellite gravity observations, e.g., from the Gravity Recovery and Climate Experiment (GRACE), GRACE-Follow On (GRACE-FO) and future gravity missions, can be applied to constrain the vertical sum of water storage simulations of Global Hydrological Models (GHMs). However, the state-of-the-art DA of these measured Terrestrial Water Storage (TWS) changes into models is often performed regionally, and if globally, at low spatial resolution. To perform a reliable global DA system with GRACE(-FO), several major challenges must be addressed, (1) what’s the accuracy of GRACE(-FO), (2) what’s the spatial correlation of GRACE(-FO) grids, (3) how to resolve the numerical instability and inefficiency of global DA. In this study, we present a detailed analysis of GRACE(-FO)’s accuracy and spatial resolution from a new perspective, and reveal their dynamic change over space and time. Then, we develop a Python-based open-source PyGLDA system that allows performing DA globally at a fine scale with high numerical efficiency. Case studies will be demonstrated at Danube River Basin and at a global scale, using the monthly TWS fields of GRACE (2002-2010) and the W3RA water balance model at 0.1-degree/daily spatial-temporal resolution.

How to cite: Yang, F., Schumacher, M., and Forootan, E.: Quantifying accuracy and spatial resolution of GRACE(-FO) products and their impact on global data assimilation studies, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-63, https://doi.org/10.5194/gstm2024-63, 2024.

Due to the hidden nature of groundwater resources and the geologic complexity of aquifers, an accurate assessment of the groundwater storage change is challenging. Over the last decades, remote sensing technologies have provided novel insights into groundwater dynamics. One example is the Satellite Gravity and Climate Experiment and its Follow-on (GRACE/FO) missions, which are sensitive to water mass changes in regionally sized aquifers. They have brought to light a global phenomenon of water bankruptcy in arid to semi-arid regions with large populations and agricultural activities like the Southwest USA, the Middle East, or northern India. Other essential technologies include those that provide surface deformation measurements and, most importantly, vertical land motion (VLM), caused by changes in water resources. High-resolution VLM maps provided by interferometric synthetic aperture radar (InSAR) acquisitions have helped quantify aquifer mechanics and groundwater dynamics worldwide at management-relevant resolutions. Despite their fine spatial resolution, continuous coverage, and complementary nature, a seamless and physically consistent combination of VLM maps from InSAR with global GRACE TWS change observations has been lacking. This presentation first reviews early advances in GRACE-InSAR combinations and their outcomes. Then, it presents a unifying approach combining GRACE and InSAR observations through a multi-physics joint inversion for quantifying groundwater storage changes (GWS) by considering the underlying physical processes driving each observation. Next, we apply this approach in a case study to estimate GWS for Central Valley California during the 2020-2021 drought. We compare the results to those from other studies. Lastly, we highlight the technological and scientific advances required to expand the approach from drought to non-drought periods and regional to continental or global spatial coverage. Accurate observation of GWS with high spatial resolution will improve our understanding of groundwater recharge processes, for example, by better enabling the integration of the geodetic data products into accurate groundwater hydrological models. This can further support the assessment of climate change's impact on groundwater resources and water management approaches, such as assessing the success of managed aquifer recharge.

How to cite: Werth, S., Manoochehr, S., Grace, C., Manuela, G., and Nitheshnirmal, S.: Unlocking Insight: Past, Present, and Future of Groundwater Storage Change Digital Twins via GRACE and InSAR Integration, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-42, https://doi.org/10.5194/gstm2024-42, 2024.

The planned MAGIC mission, a collaboration between ESA and NASA, is expected to deliver 
an extended record of the global mass transport time series with improved accuracy, 
temporal and spatial resolutions. ESA’s involvement in MAGIC is through its Next
Generation Gravity Mission (NGGM) and NASA contributes with its GRACE-C mission. One 
of the key deliverables is terrestrial water storage (TWS), which is vital for assessing 
changes in climate and managing water resources efficiently. As an essential climate 
variable, TWS plays an important role in providing information regarding extreme events. 
While the GRACE and its Follow-On mission distribute global TWS anomalies (TWSA), their 
coarse spatial resolution (around 150,000 km²) constrains detailed analysis of smaller 
basins. Given that freshwater is often sourced from localized aquifer systems, enhancing 
spatial resolution is necessary for effective local water management. Furthermore, 
improvements in spatio-temporal resolution would allow advances in early flood warning 
applications. To overcome these limitations, data assimilation (DA) techniques have been 
developed to combine GRACE observations with land surface models (LSMs), making it 
possible to downscale and disaggregate TWSA information into its individual components.

This study evaluates the performance of data assimilation utilising GRACE-type and MAGIC 
error information within the LSMs NOAH-MP and CLSM, with a focus on two regions in 
South America and Europe. The model runs cover the period from January 2003 to 
December 2006, using data generated during the ESA Science Support study for MAGIC 
Phase A. The data is based on closed-loop simulations with a 30 day repeat orbit, containing 
the hydrology, ice and solid Earth (HIS) signal along with atmosphere-ocean errors, ocean 
tide errors and instrument noise. In total 12 years of monthly data were produced spanning 
from January 1995 to December 2006 with spherical harmonic coefficients up to degree and 
order 90. A reference HIS signal, acquired from the ESA ESM over the same period is used 
to compute retrieval errors. 

The results demonstrate that MAGIC data assimilation offers advantages across climatically 
different regions independent of LSMs chosen. Furthermore, this study indicates that unlike 
previous data assimilation studies, it will be possible to assimilate MAGIC into smaller basins 
sizes, seen by the relative improvements of MAGIC DA over GRACE-type DA. Lastly, it is 
shown that in case of MAGIC DA post-processing can be considerably reduced, such as 
removing the need of filtering up to degree and order 60. Thus, leakage quantification issues 
due to the applied filter would be alleviated achieving more straightforward uncertainty 
quantification.

Overall, MAGIC data assimilation offers substantial improvements in TWS estimation and 
trend correction compared to GRACE-type data assimilation, demonstrating its potential for 
improving hydrological applications

How to cite: Nitschke, A. and Daras, I.: Expected performance of future MAGIC data-assimilated Terrestrial Water Storage (TWS) products, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-68, https://doi.org/10.5194/gstm2024-68, 2024.