B.4

The German-American satellite missions GRACE (Gravity Recovery and Climate Experiment, 2002-2017) and its successor GRACE-Follow-On (GRACE-FO, since 2018) observed terrestrial water storage (TWS) variations over the continents. With 20 years of data, we can now study interannual trends and variations in water storage beyond the strong declining trends of ice sheets or glaciers. Africa is the only continent which exhibits an overall positive trend in TWS for the GRACE/GRACE-FO period. In this contribution, we analyse the interannual TWS variations in Africa and focus on the East-African Rift region around Lake Victoria, Lake Tanganyika, and Lake Turkana, where the long-term TWS increase is most pronounced.

As TWS trends are not monotonous over time, a signal decomposition into linear trend and sinusoidal annual and semiannual seasonality is insufficient to investigate interannual variability. Hence, we employ the STL method (Seasonal Trend decomposition based on Loess) to separate the TWS signals into an interannual trend signal, which is not a linear trend, a seasonal signal, and residuals. These interannual trend signals are used in a subsequent cluster algorithm to identify regions with similar interannual variability. We found complex interannual TWS signals in East Africa and many African regions. In the East African Rift region, we can observe a decrease in TWS until around 2006, after which an increasing trend started. Finally, in the last few years, the trend has further accelerated.

To better understand the origin of the observed interannual signal, we compare the TWS time series with precipitation and evaporation data and SWS data derived from satellite altimetry. The interannual variations of precipitation are insufficient to explain the strong interannual variations visible in the TWS data in Eastern Africa. SWS variations, in contrast, are highly correlated with TWS, explaining nearly 50% of the TWS variations. Among the surface water bodies, we study the influence of Lake Victoria in particular, as it is the largest lake in the region, and its water balance is also governed by a dam at its outlet. SWS of this lake is heavily affected by man-made decisions and is visible in the GRACE/GRACE-FO observations.

How to cite: Boergens, E., Güntner, A., Sips, M., Schwatke, C., and Dobslaw, H.: Interannual TWS Trends in the East-African Rift Region, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-3, https://doi.org/10.5194/gstm2022-3, 2022.

Water mass changes at and below the surface of the Earth cause changes in the Earth’s gravity field which can be observed by at least three geodetic observation techniques: ground-based point measurements using terrestrial gravimeters, space-borne gravimetric satellite missions (GRACE and GRACE-FO) and geometrical deformations of the Earth’s crust observed by GNSS. Combining these techniques promises the opportunity to compute the most accurate (regional) water mass change time series with the highest possible spatial and temporal resolution, which is the goal of a joint project with the interdisciplinary DFG Collaborative Research Centre (SFB 1464) "TerraQ – Relativistic and Quantum-based Geodesy".

A method well suited for data combination of time-variable quantities is the Kalman filter algorithm, which sequentially updates water storage changes by combining a prediction step with observations from the next time step. As opposed to the standard way of describing gravity field variations by global spherical harmonics, we will introduce space-localizing radial basis functions as a more suitable parameterization of high-resolution regional water storage change. A closed-loop simulation environment has been set up to allow the testing of the setup and the tuning of the algorithm. In a first step only simulated GRACE data together with realistic correlated observation errors will be used in the Kalman filter to sequentially update the parameters of a regional gravity field model. However, the implementation was designed to flexibly include further observation techniques (GNSS, terrestrial gravimetry) at a later stage. This presentation will outline the Kalman filter framework, introduce the regional parameterization approach, and address challenges related to, e.g., ill-conditioned matrices and the proper choice of the radial basis function parameterization.

How to cite: Wöhnke, V., Eicker, A., and Weigelt, M.: Regional modeling of water storage variations in a Kalman filter framework, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-9, https://doi.org/10.5194/gstm2022-9, 2022.

The Grand Ethiopian Renaissance Dam (GERD) was built over the Pan African highlands in western Ethiopia and upon completion, its reservoir will cover an area of 1,904 km2 and impound 74 km3. The reservoir encroaches over Precambrian metamorphic rock, syntectonic intrusives, late and post tectonic granitic intrusives, calcite, dolomite, and Tertiary basalts. The basement rocks are characterized by deep regolith (up to 60 m) and highly deformed texture, related to NE–SW extension associated with the breakup of Gondwana and NW–SE-directed extension and opening of the Main Ethiopian Rift. The extended and recurrent tectonic activities left behind a highly deformed landscape in western Ethiopia. The static (Copernicus Digital Elevation Model), and temporal satellite data analysis (GRACE and GRACE-FO, Sentinel-1, 2, MODIS, Radar altimetry) were used to monitor and quantify seepage from the GERD reservoir. Findings reveal the following. (1) using Sentinel-1 images that capture the variations in the reservoir storage, the impounded water in GERD in the first (2020) and second (2021) fillings were found to decline relative to the Initial Storage (IS Filling I: 3.7 Km2, IS Filling II: 9.3 Km2) by 29 and 37%, respectively. (2) using GRACE data that measures the water in the GERD Reservoir and its surroundings, we found that the GRACETWS values exceed the reservoir storage detected from Sentinel-1 data and is more comparable to the targeted filling volumes. (3) One interpretation of the discrepancies between the GRACE and Sentinel-1 data is losses to evaporation and infiltration.  The evaporation remained insignificant during the filling process (<0.5 Km3), whereas losses to infiltration along the highly fractured (faults, shear zones) and within the weathered basement rocks remain a plausible explanation that merits additional investgations.

How to cite: Abdelmohsen, K., Sultan, M., Yan, E., and Save, H.: Assessment of Seepage from the Grand Ethiopean Renaissance Dam: A Remote Sensing-Based Application, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-68, https://doi.org/10.5194/gstm2022-68, 2022.

The Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) have enabled understanding and monitoring of the impacts of climate change on many of the major world’s watersheds. The Tigris Euphrates watershed covers an area of 1×106 km2 with two main rivers, the Euphrates River (length: 2800 km) and the Tigris River (length: 1900 km) originate from the highlands of Turkey, Iran, and Syria and flow downstream towards Iraq. Our analysis of multiple satellite missions (e.g., GRACE, GRACE Follow-On, Landsat 5,7,8, and satellite radar altimetry) and global land surface models over the highly engineered Tigris Euphrates watershed (30 dams) showed an impressive recovery of the system following a prolonged drought (2007–2018; Average Annual Precipitation [AAP]: ~400 km3) by an extreme precipitation event in 2019 (726 km3) with no parallels in the past 100 years. This recovery (113±11 km3) compensated for 50% of the losses endured during drought by impounding a large portion of the runoff within the reservoirs (capacity: 250 km3). In basins lacking artificial reservoirs a different response to extreme precipitation events is observed from temporal GRACE solutions.  Extreme precipitation events (2011-2022) over northern Arabia (PPT: Hail: 8.43 km3; Ad-Dahna: 2.22 km3 and Medina: 3.71 km3) and central Arabia (PPT: Riyadh: 4.66 km3 and Mecca: 0.21 km3) produced an increase in GRACETWS that lasted for a few months only. Similarly, cyclones over Oman (2011and 2015; PPT: 6 and 6.6 km3, respectively) produced a similar effect, where most of the precipitation ends up as losses from the water budget to evaporation or runoff. This is apparently the case for many of the nonengineered hydrologic systems that have no storage capacity to capture the runoff.  Our findings demonstrate the role of dams in drought mitigation and sustenance of water supplies through storage and controlled distribution, and suggest that highly engineered watersheds are better prepared to deal with the projected increase in the frequency and intensity of extreme rainfall and drought events in the 21st century. 

How to cite: Sultan, M., abdelmohsen, K., Karimi, H., Saleh, H., and Save, H.: GRACE Response to Climate Change over Engineered and Nonengineered Basins, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-70, https://doi.org/10.5194/gstm2022-70, 2022.

Changes in evapotranspiration (ET) substantially affect water availability and ecosystem health. Higher evaporative demand during drought acts to increase ET, but droughts also reduce the moisture supply necessary for ET. These competing factors limit straightforward prediction of even the sign of ET anomalies during droughts.  Drought-driven increases in evapotranspiration (ET+) are of particular concern because they quickly deplete water resources, causing flash droughts and acute stress on ecosystems. Here, we used GRACE/GRACE-FO and a water balance approach to show that ET+ is globally widespread, occurring in 44.4% of drought months. The sign of ET’s drought response depends most on the magnitude of precipitation and of total water storage anomalies, rather than its location. CMIP6 Earth system models underestimate the ET+ probability by nearly half, and more so in drier regions, primarily due to missing representations of soil structure effects on soil evaporation, and incorrectly parametrized plant and soil traits. These processes should be prioritized to reduce model uncertainties in the water-energy-food nexus. 

How to cite: Zhao, M., A, G., Liu, Y., and Konings, A.: Evapotranspiration frequently increases during droughts, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-35, https://doi.org/10.5194/gstm2022-35, 2022.

Over the last two decades, California, USA has undergone three multi-year intense periods of drought, with the most recent still ongoing as of August 2022. Low precipitation and unusually warm temperatures, yielding high evapotranspiration rates, low snowpack, and early snowmelt, have led to record low surface water levels in reservoirs across the state and an increased reliance on groundwater resources, resulting in widespread groundwater overdraft. Extreme states in the water cycle such as drought can be measured directly and indirectly using several geodetic remote sensing tools, including observations from the GRACE and GRACE-FO satellites and deformation recorded by Global Navigation Satellite System (GNSS) stations measuring the elastic response of Earth’s crust to changes in mass loading. In California’s Central Valley, a large, elongate sedimentary basin between the Coastal Ranges and Sierra Nevada Mountains, groundwater overdraft during drought has caused widespread subsidence. This subsidence is a poroelastic response to aquifer overdraft and has been observed using interferometric synthetic aperture radar (InSAR) and GNSS station displacements. This presentation proposes a unified geophysical model incorporating observations from GRACE, which provides regional closure of the water budget at impressive accuracy but a low spatial resolution, and indirect measurements of poroelastic and elastic deformation from GNSS and InSAR, to derive improved estimates of terrestrial water storage change (∆TWS) at a higher spatial resolution. Using a joint inversion framework that combines GRACE-FO ∆TWS with elastic deformation from GNSS station displacements and InSAR-derived vertical land motion showing poroelastic aquifer deformation over the Central Valley aquifer, we produce high-resolution maps of ∆TWS over California and Nevada and groundwater loss over the Central Valley during the 2020-2021 drought period. We find that the largest water loss occurs over the southern Central Valley, with groundwater loss of more than two meters equivalent water height. Outside of the southern Central Valley, we find that the largest TWS declines occur in the northern Sierra Nevada Mountains and northern Central Valley, with ∆TWS of ~0.5-1 meter equivalent water height. We also show that groundwater loss estimates determined using our joint inversion framework are larger, but mainly in agreement with GRACE-derived groundwater loss estimates when considering underlying processes and uncertainties.

How to cite: Carlson, G., Werth, S., and Shirzaei, M.: Improving groundwater loss estimates using a combination of GNSS, GRACE-FO, and InSAR: Case study of California’s recent 2020-2021 drought, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-2, https://doi.org/10.5194/gstm2022-2, 2022.

Multiple projects now assimilate GRACE and GRACE-FO data into land surface models in order to constrain the terrestrial water storage state variables (e.g., groundwater, soil moisture, snow) in those models.  This approach has been employed successfully to generate weekly, global drought and wetness indicators, among other products. However, in some cases GRACE/FO data assimilation can actually degrade the model output due to the spatial and temporal smoothing of localized mass change signals.  We focus on the state of Colorado as an example, where mass changes associated with seasonal snowpack in the mountainous western half of the state become damped by leakage of the weaker mass change signal in the dry eastern half of the state.  The effect is more acute at high elevations where the seasonal snowpack is deepest.  Innovative new GRACE/FO products and/or data assimilation schemes are needed to overcome such issues.

How to cite: Rodell, M.: GRACE-FO Data Assimilation Applications and Issues, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-18, https://doi.org/10.5194/gstm2022-18, 2022.

We describe the new Global Land Water Storage data set (GLWS2.0), which contains total water storage anomalies (TWSA) over the global land with a spatial resolution of 0.5°, covering the time frame 2003 to 2019 without gaps, and including an uncertainty quantification.

GLWS is produced by assimilating 4° gridded GRACE and GRACE-FO-derived TWSA into the WaterGAP global hydrological model using the Parallel Data Assimilation Framework (PDAF). The resulting data set represents thus an optimal synthesis of GRACE data, the hydrological model and implicitly all data sets that went into the model. This synthesis seeks to fit GRACE and GRACE-FO TWSA grids within error bars (from propagating full level-2 error variance covariance matrices), and at the same time it solves the horizontal and vertical water balances as represented in the hydrological model, again within error bars. To this end, the uncertainty of the hydrological model simulation is represented via a 32-member ensemble, where we account for the uncertainty of the precipitation and temperature data and for the uncertainty of some model calibration parameters. As a result, when no GRACE (-FO) data is available, GLWS represents the mean of an ensemble where each member is dynamically consistent with the model. It is important to understand that this mean depends on the ensemble and the previous GRACE estimates, and thus differs from published WaterGAP standard runs even if there is no GRACE data within a particular month. Due to the dynamical constraints, the assimilation-derived GLWS data set does not represent a simple downscaling of the GRACE data, i.e. spatial smoothing of GLWS does not necessarily correspond to GRACE (-FO) TWSA. GLWS indeed contains all water storages that are represented in WaterGAP (e.g. groundwater), but here we will focus only on TWSA.

The main updates with respect to the release 1 were the use of an updated version of WaterGAP as well as minor bug fixes in the assimilation. GLWS1.0 and GLWS2.0 have already been provided to several research groups within the DFG GlobalCDA research unit and beyond for evaluation purposes.

In this presentation we describe the methods and data sets that went into GLWS2.0, and the validation of the resulting 0.5° TWSA grids from a geodesy applications perspective, including comparisons to GRACE and GRACE-FO data and to GNSS-derived site displacements. We will also show some extended experiments with jointly assimilating river discharge data, model parameter estimation, and the integration of machine-learning based model prediction in our assimilation approach.

How to cite: Kusche, J., Gerdener, H., Schulze, K., Fupeng, L., Döll, P., Ackermann, S., Müller Schmied, H., Hosseini Moghari, S. M., van Dam, T., and klos, A.: The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into the WaterGAP global hydrological model, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-37, https://doi.org/10.5194/gstm2022-37, 2022.

Land surface models (LSMs) are useful for estimating land surface states and fluxes such as snow water equivalent, soil moisture content, vegetation, and river discharge. Although estimates are continuous in time and space, LSMs are flawed since they lack comprehensive representation of process physics and are dependent on uncertain boundary conditions. One way to improve LSM performance is by conditioning model states on space-borne retrievals using an ensemble Kalman filter framework.

In this study, the Noah-MP version 4.0.1 LSM without conditioning (a.k.a., Open Loop; OL) is compared against the same model with conditioning (a.k.a., Data Assimilation; DA). Two different univariate DA experiments are conducted:  1) assimilation using terrestrial water storage (TWS) anomalies from GRACE / GRACE-FO, and 2) assimilation using leaf area index (LAI) retrievals from MODIS. Not only do the experiments assimilate different types of retrievals (i.e., TWS versus LAI), but also, they assimilate products of different spatial (~3° versus ~0.005°) and temporal (~monthly versus ~weekly) resolutions. Experiments are conducted across different watersheds in North America with a particular focus on basins with irrigated agriculture. Modeled states and fluxes from the OL and DA are then compared against independent, ground-based measurement networks including U.S. SNOTEL, Canadian CanSWE product, U.S. SCAN for soil moisture, and USGS measurement gauges for river discharge. Statistical analyses, including bias, RMSE, and normalized information content (NIC) are computed to quantify the marginal improvements via each assimilation experiment. Results provide a basis to better understand the coupling between different state variables (i.e., snow mass, soil moisture, and groundwater) as well as the utility of using coarse-scale and fine-scale retrievals in land data assimilation.

How to cite: Moghaddasi, A. and Forman, B.: Comparing Land Surface Model Performance between Fine-scale and Coarse-scale Assimilation: Leaf Area Index Retrievals versus GRACE / GRACE-FO Retrievals, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-57, https://doi.org/10.5194/gstm2022-57, 2022.

Existing approaches for seasonal forecasts of land water storage via land surface models use meteorological forecast products as forcing data. Yet, such meteorological forecast data contain large uncertainties, which inevitably map into highly uncertain land water storage predictions. As a result, current seasonal forecasting of land water storage contains extensive uncertainties. The Gravity Recovery and Climate Experiment (GRACE) satellite mission greatly contributed to monitoring historical land water storage change (TWSC). But many applications like seasonal forecasting of land water storage using GRACE data remains underexplored. Here we analyze the lag relationship between hydrometeorological variables - e.g., precipitation, sea surface temperature, or runoff - and GRACE-derived total water storage change (TWSC). We find that TWSC detected by GRACE lags behind all considered hydrometeorological variables by a few months after removing seasonal effects. By using this lag relationship we forecast the nonseasonal TWSC fields up to one year lead. The prediction approach developed here is based on mostly observational inputs and the validation by GRACE-FO observations suggests it can provide more reliable prediction of global land water storage as compared to model simulations.

How to cite: Li, F., Kusche, J., Sneeuw, N., Siebert, S., Gerdener, H., Wang, Z., Chao, N., Chen, G., and Tian, K.: Forecasting Global Land Water Storage using GRACE data, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-19, https://doi.org/10.5194/gstm2022-19, 2022.

Understanding regional terrestrial water storage (TWS) is essential for efficiently managing water resources in a warming climate. The nearly twenty years of observations from GRACE/GRACE-FO missions have enabled unprecedented investigation into the spatiotemporal variability of TWS. However, it remains unclear to what extent regional TWS trends are persistent fingerprints of anthropogenic warming or natural variability in the Earth system. In particular, ambiguity arises in the potential aliasing of quasi-periodic processes operating on interannual to decadal timescales into observed decadal trends.

To alleviate this ambiguity, we leverage additional datasets that extend the TWS record in time: two statistical reconstructions of GRACE/GRACE-FO that extend the observed time-series into the past, and the suite of modeling results contained in the Community Earth System Model 2 Large Ensemble (LENS) Project. After establishing that these additional data reflect the observed variability, we assess the impact of record length on regional trends. Specifically, we address two questions:

1) Is the magnitude of observed trends anomalous relative to longer record lengths?

2) How does the dominant timescale of variability change as a function of record length and dataset?

Preliminary findings suggest that in most locations around the world, the magnitude of regional trends observed by GRACE/GRACE-FO are high compared to the reconstructions. In much of the Amazon, Central Africa, Southeast Asia, and India, the LENS dataset also shows lower variability than the observations. With spectral analysis, we find that much of these regions are dominated by interannual periodicities. Regression against climate indices gives further evidence that interannual periodicities are more important than decadal in explaining TWS variability. Such results suggest that the strong ENSO events in 2010-2011 and 2015-2016 were an important component of observed regional trends and that further observations are needed to disentangle climate variability from secular change.

© 2022. California Institute of Technology. Government sponsorship acknowledged.

How to cite: Buzzanga, B., Hamlington, B., and Fasullo, J.: Disentangling timescales of terrestrial water storage variability, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-61, https://doi.org/10.5194/gstm2022-61, 2022.

Information on water storage changes in the soil can be obtained on a global scale from different types of satellite observations. While active or passive microwave remote sensing is limited to investigating the upper few centimeters of the soil, satellite gravimetry can detect changes in the full column of terrestrial water storage (TWS) but cannot distinguish between storage variations occurring in different soil depths. Jointly analyzing both data types promises interesting insights into the underlying hydrological dynamics and may enable a better process understanding of water storage change in the subsurface.

In this study, we aim to investigate the global relationship of (1) several satellite soil moisture (SM) products and (2) non-standard daily TWS data from the GRACE and GRACE-FO satellite gravimetry missions on different time scales. The six SM products analyzed in this study can be categorized based on their degree of post-processing and the observed soil depth. Original level-3 surface SM data sets of SMAP and SMOS are compared to post-processed level-4 data products (surface and root zone SM) and a multi-satellite product provided by the ESA CCI.

We decompose the signal into seasonal to sub-monthly frequencies and carry out the comparison with respect to spatial patterns and temporal variability. An additional time-shift analysis will indicate differences in the temporal dynamics of soil moisture storage change in varying depth layers. We will apply tailored temporal masking of the time series to focus on time spans with favorable signal-to-noise ratio and to exclude time spans of snow cover or frozen soil. Additional data sets, such as precipitation events, will be added to the analysis to enhance the interpretation of the comparison of soil moisture and water storage variations.

How to cite: Blank, D., Eicker, A., Bochynski, F., and Güntner, A.: A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-8, https://doi.org/10.5194/gstm2022-8, 2022.

GRACE and GRACE Follow-On (GRACE-FO) missions provide unique information on the wetness state of a river basin with regard to its flood generation potential. However, the long latency of the standard monthly GRACE products has limited their direct applications in operational flood early warning.  The Center for Space Research (CSR) at The University of Texas is developing a new 5-day mascon product (CSR.5d) using GRACE measurements only. The 5-day solution represents the latest global mass changes and may thus be useful for detecting and predicting hydroclimate events (e.g., flooding) at the sub-monthly scale. In this work, we assessed the predictability of GRACE-like, short-term total water storage anomalies (TWSA) by using the experimental CSR.5d product as training samples. Specifically, a probabilistic deep learning model was used to learn the state-transition model underlying the CSR.5d TWSA, by using the antecedent TWSA and hydroclimatic variables (e.g., precipitation and air temperature) as predictors. By design, our method generates both predicted mean and uncertainty intervals at the same time. Performance metrics, obtained at the grid- and basin scales, suggest that the inherent dynamics of the TWSA variations can be learned well using the machine learning method, thus providing important insights on the operational use of the 5-day mascon product.

How to cite: Sun, A., Rateb, A., Save, H., Scanlon, B., and Hasan, E.: Predicting Sub-Monthly Total Water Storage Variations Using a New 5-Day Mascon Product and Deep Learning, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-88, https://doi.org/10.5194/gstm2022-88, 2022.

The sub-monthly gravity solutions derived from the Gravity Recovery and Climate Experiment (GRACE), and its Follow-On (FO) missions at daily and five-day interval represent a new opportunity to investigate rapid sub-monthly changes in terrestrial hydrology and help to map and understand the progression of hydrological extremes. In this research, we report on a comparison of the five-day mascon solution developed by the Center for Space Research (CSRm-5d), University of Texas at Austin, and the daily spherical harmonics solution from the Institute of Geodesy, Graz University of Technology (ITSG-2018). We compare the two GRACE/GRACE-FO products with atmospheric reanalysis data for the net meteorological changes in the water balance over grid and hydrological basin scales using an independent NASA-Modern Era Retrospective-analysis for Research and Applications (MERRA) and fifth generation ECMWF atmospheric reanalysis of the global climate  (ERA -5) data covering the period 2004-2010. Water fluxes and net changes in water balance at a 5-day rate, and 1-degree grid were derived, and the signal at sub-monthly timescale was isolated using one-dimensional inverse wavelet transform. Statistical matrices were used to evaluate the phase, amplitudes, and variability of the rapid signal. Results show that both GRACE/GRACE-FO data uncover valuable information of net meteorological changes for time scale as short as 11-day. Higher variability and stronger amplitude of the signal are predominantly in areas where the change in the meteorological fluxes is high (e.g., North India, South Africa, and Eastern U.S). While the ITSG-2018 solutions are statistically and process model-based, the CSRm-5d solutions are purely based on gravity fields. Both datasets show consistent agreement relative to the MERRA-2 over the globe. Rapid sampling of GRACE/GRACE FO gravity fields provides a new avenue to infer and understand rapid global and local geophysical processes.   

How to cite: Rateb, A., Sun, A., Save, H., Scanlon, B. R., and Hasan, E.: Synoptic meteorological signal in daily and five-day GRACE/GRACE-FO solutions, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-89, https://doi.org/10.5194/gstm2022-89, 2022.

The observations of total water storage variations (TWSV) from GRACE satellites have a coarse spatial resolution of ~300 km. Hydrological and land surface models, providing TWSV with higher spatial resolutions like 50 km, offer an opportunity to improve the coarse spatial resolution of GRACE data. We present a fusion approach based on wavelet multiresolution analysis to combine TWSV data from GRACE and GLDAS Noah model. We first decompose the TWSV maps from GRACE and GLDAS into their building blocks at various spatial scales, examine their signal characteristics, and then combine complementary spatial features at the wavelet coefficient level. The spectral nature of our approach enables us to easily combine the large-scale components from GRACE with small-scales information from GLDAS Noah and produce a unified, multi-scale TWVS dataset which has the advantages of both input datasets. We show that the spatial signal frequency spectrum of the fusion dataset matches that of GRACE data at low spatial frequencies, but that of GLDAS data at high spatial frequencies, indicating that the algorithm provides a shrinkage free combination of both input datasets. For a case study in contiguous United States, we inspect fused TWSV dataset in the spatial domain. Despite having detailed features originating from GLDAS, the fused dataset accurately quantifies the water budget and its long-term trend, when averaged over large basins during 2003–2015 similar to GRACE. We use the high-resolution surface soil moisture from SMOS satellite and snow water equivalent from SNODAS to demonstrate the improvement in representation of small-scale features associated with soil moisture and snow (i.e., the two water storage components that GLDAS Noah TWS is comprised of) in our fused dataset. In both cases, our fused TWSV dataset shows notable higher correlations with these independent data compared to GLDAS.

How to cite: Ghobadi-Far, K., Werth, S., and Shirzaei, M.: Multi-scale total water storage variations from fusion of GRACE and GLDAS Noah data using wavelet analysis, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-81, https://doi.org/10.5194/gstm2022-81, 2022.

We will give an insight into the atmospheric physics contribution to the New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV) research group, which is funded by the German Research Foundation (DFG). Since May 2019, NEROGRAV develops new analysis methods and modeling approaches to improve data analysis of the GRACE and GRACE-FO mission. Our contribution to this research unit is the High-Resolution Atmospheric-hydrological Background Modelling for GRACE/GRACE-FO – regional refinement and validation (HIRABAM). One of our goals in this individual project (IP) is the development of improved Atmospheric Ocean Dealiasing (AOD) products by integrating high-resolution regional atmospheric modeling. Furthermore, we are interested in whether and how extreme hydrometeorological events map into GRACE L1/2 data.

The 1995-2019 COSMO-REA6 regional reanalysis and the July 2021 ICON-EU/D2 analysis are non-hydrostatic regional atmospheric models with 3D fields on grid sizes below 10 km, with mass densities of dry air and all water phases (gaseous, liquid clouds and rain, icy snow, sleet, and hail) available in each of their 40-50 vertical layers. By integrating over all layers, the total local and column-by-column atmospheric mass density can be calculated without using the hydrostatic assumption. Unfortunately, such high-resolution atmospheric model data is currently only available within the EURO-CORDEX domain, which covers the European continent. Nevertheless, to compute global spherical coefficients for our AOD products that benefit from the high resolution of the regional models, we nested COSMO-REA6 in a lower-resolution but global model. Here we want to answer the question: does high-resolution mass variability leaves a significant fingerprint in the global loading coefficients? The heavy rainfall observed in the Ruhr/Ahr/Erft/Maas basin in July 2021 are clearly visible in ICON-EU/D2 atmospheric data. The question to be answered here is: are mass variations of such extreme weather events visible in GRACE/GRACE-FO data and do they exceed typical uncertainties?

How to cite: Mielke, C., Dixit, S., Kracheletz, M., Friederichs, P., Kusche, J., Springer, A., and Hense, A.: High-Resolution Atmospheric-hydrological Background Modelling for GRACE/GRACE-FO – regional refinement and validation, GRACE/GRACE-FO Science Team Meeting 2022, Potsdam, Germany, 18–20 Oct 2022, GSTM2022-62, https://doi.org/10.5194/gstm2022-62, 2022.