Displays

The establishment of the Inter-Commission Committee on "Geodesy for Climate Research" (ICCC) of the International Association of Geodesy (IAG) emphasizes on the usefulness of geodetic sensors for estimating high-resolution water mass variation, which is due to broad applications of geodetic tools ranging from water cycle studies to water resources management. As such, data from both GRACE missions continue to provide insight into the alarming rates of groundwater depletion in large aquifers worldwide. Observations of vertical land motion (VLM) from GPS and InSAR may reflect elastic responses of the Earth's crust to changes in mass load, including those in aquifers. However, above confined aquifers, VLM observations are dominated by poroelastic deformation processes. In previous works, Ojha et al. 2018 and 2019 show that GRACE-based estimates of groundwater storage change in the Central Valley, California, are consistent with those obtained by utilizing measurements of surface deformation. These studies also show that annual variations in VLM correlate well in time with groundwater levels.

Here, we investigate seasonal variations in groundwater storage by identifying how their effect is manifested in geodetic and hydrological datasets. Groundwater well observations in the Central Valley indicate maximum groundwater levels at the beginning of the year between February to April and lowest water levels in the middle of the year about July to October. Meanwhile, GRACE groundwater storage estimates peak about four months later. To get insight into the mechanisms leading to this discrepancy, we perform a Wavelet multi-resolution analysis of GRACE TWS variations and complementary groundwater, snowcap, soil moisture, and reservoir level variations. We show that the majority of the differences between wavelet spectrums at seasonal frequencies occur during drought periods when there is no supply of precipitation in the high elevations. We employ a 1D diffusion model to demonstrate that the variations in groundwater levels across the Central Valley are due to the propagation of the pressure front at recharge sites due to gradual snowmelt. Such a model could explain the different timing of peaks in groundwater time series based on satellite gravimetry compared to deformation and well observations. We also discuss that winter rains are not able to directly contribute to recharging deep aquifers in the Central Valley, whereas most of the recharge must source from lateral flow caused by differential pressure at the sites of snow-melt in the Sierra Nevada as well as from agricultural return flows.

This analysis addresses the question of how well the different geodetic signals that reflect groundwater discharge and recharge processes agree with one another and what are the possible causes of disagreements. We emphasize the need for interdisciplinary efforts for the successful integration of available geodetic and hydrological datasets to improve our ability to utilizing geodetic sensors for climate research and water resources management.

References:

Ojha, C., Werth, S., & Shirzaei, M. (2019). JGR, https://doi.org/10.1029/2018JB016083.

Ojha, C., M. Shirzaei, S. Werth, D. F. Argus, and T. G. Farr (2018), WRR, https://doi.org/10.1029/2017WR022250.

How to cite: Werth, S. and Shirzaei, M.: Seasonal variation in water storage, vertical land motion and well levels: Implications for groundwater storage change in Central Valley, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10555, https://doi.org/10.5194/egusphere-egu2020-10555, 2020.

Climatic changes in precipitation intensity across the United States (USA) may also affect the frequency and magnitude of drought and flooding events, with potentially serious consequences for water supply across this country. Reliable estimation of water storage changes in the soil root zone and groundwater aquifers is important for predicting future water availability, drought and flood monitoring and weather prediction. In this study, we assimilate Terrestrial Water Storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into a water balance model with 12.5-km spatial resolution. Our goal is to explore meso-scale surface and deep-level soil water storage, as well as groundwater changes within the USA covering the period 2003-2017. A new Bayesian approach is formulated and implemented in this study, which provides a dynamic solution for a state-space equation between hydrological model outputs and TWS observations, while considering their error structures. The unknown state parameters and temporal dependency between them are estimated through a combination of forward/backward Kalman Filtering and Markov Chain Monto Carlo (MCMC) methods.

The outputs of this methodological approach are evaluated using in situ data from historical USGS groundwater data (over 6600 wells) and the ESA CCI surface soil moisture data. The results indicate that our GRACE data assimilation generally improves the simulation of groundwater and soil moisture across the USA. For example, the long-term linear trend fitted to the Bayesian-derived groundwater and soil water storage are in a same direction as those of in situ data in 63% and 58% of regions studied across the USA, respectively. However, this vale is estimated less than 51% for both water storage estimates derived from the original water balance model, which suggesting that the data assimilation modulates the hydrological models to perform more realistically. The biggest improvements are observed in the southeast USA with considerably large inter-annual variability in precipitation, where modelled groundwater apparently responded too strongly to the changes in atmospheric forcing. The Bayesian data assimilation method also improves the temporal correlation coefficients between the in situ USGS and ESA CCI data and model outputs after merging with GRACE TWS estimates. For instance, the correlation coefficient between groundwater storage and USGS observation increased from -0.52 to 0.48 and from -0.28 to 0.25 in southeast and southwest of USA, respectively. Finally, we will explore changes in Bayesian-derived groundwater and soil water storage within the Florida, California and South of Mississippi regions and interpret their relations with climate-induced factors such as precipitation and ENSO index.

Keywords: USA; Data Assimilation; Bayesian Method; Kalman Filtering; MCMC; GRACE; W3RA; groundwater storage; soil water storage; USGS; ESA CCI.

How to cite: Mehrnegar, N., Jones, O., Singer, M. B., Schumacher, M., Jagdhuber, T., Scanlon, B. R., and Forootan, E.: Exploring meso-scale soil water and groundwater storage changes within the USA through a Bayesian combination of GRACE data with monthly 12.5 km model simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19070, https://doi.org/10.5194/egusphere-egu2020-19070, 2020.

In this study we investigate the ability of GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) to quantify the two consecutive summer droughts of 2018 and 2019 in Central Europe. The GRACE-FO mission was launched in May 2018 as the successor of GRACE (2002-2017) and thus, allows us to relate the droughts of the last two years to former droughts in 2003 and 2015.

The water mass deficit in 2018 was 90±18.5 Gt and in 2019 even 116±18 Gt compared to the long term climatology. These deficits are 60% and 76% of the annual mean variations which is so severe that a fast recovery of the water storage cannot be expected within one year. The drought of summer 2019 was more severe than the European-wide drought of 2003 with a water deficit of 85±16 Gt and had the largest water deficit in the whole GRACE and GRACE-FO time span.

GRACE-FO total water storage data also allows the analysis of the spatio-temporal drought patterns. The largest water mass deficit in 2018 was detected in October and centred in South-Western Germany and neighbouring countries. However, the exact onset of the 2018 drought is not determinable due to missing data between July and October. The drought 2019 reached its largest deficit in July and was more evenly spread across Central Europe than the 2018 drought.

From the GRACE and GRACE-FO mass anomalies, we derive a drought index which is compared to an external soil-moisture drought index. Over the whole time series between 2002 and 2019 both indices show a high congruence. However, as the two indices do not describe the same hydrological compartments a time lag and a memory effect of TWS relative to soil-moisture is visible in the comparison.

Overall, the presented study proves the successful continuation of GRACE with GRACE-FO and thus the reliability of the observed Central European summer drought of 2019 as the most extreme water scarcity event since 2002.

How to cite: Boergens, E., Güntner, A., Dobslaw, H., and Dahle, C.: The Central European droughts of 2018 and 2019 observed with GRACE-Follow-On, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6550, https://doi.org/10.5194/egusphere-egu2020-6550, 2020.

With the climate change, drought events likely become more frequent and severe in Australia, where the worst droughts were recorded during the 21st century. Particularly, in the South-East of the country, the so called "Millennium Drought" showed below average annual precipitation for an entire decade. The precipitation record was then increased by extreme precipitation events generated from the La Niña events in 2010 and 2011. Afterwards, dry conditions began again to develop. The climate-driven events and anthropogenic adaptions to the circumstances resulted in strong impacts on the hydrological resources and agricultural production. In fact, simulating hydrological processes within the (semi-)arid region of South-East Australia is very challenging especially during extreme events. In previous studies, we found a strong underestimation of the decline of total terrestrial water storage (TWS) and of groundwater in comparison to remote sensing data and in-situ station networks. Thus, we successfully calibrated the W3RA water balance model and simultaneously assimilated TWS anomalies obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission to improve the model's skill during extreme meteorological conditions. In this presentation, we focus on the comparison of remote sensing observations and W3RA simulations after implementing the calibration and data assimilation with existing data records on anthropogenic intervention into the water cycle, as well as on agricultural production. Our results indicate high correlations between meteorological, hydrological and agricultural variables, and we observe strong similarities in the long-term trends and break points.

How to cite: Schumacher, M., Forootan, E., Crosbie, R., Mallschützke, T., and Rothermel, J.: Monitoring climate-driven and anthropogenic impacts on hydrology and agriculture in South-Eastern Australia in the 21st century, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20224, https://doi.org/10.5194/egusphere-egu2020-20224, 2020.

The three gorges dam (TGD) is always thought to have a significant impact on hydrological and climatic change in the middle-lower reaches of the Yangtze River basin (MLYRB), which can be regarded as human driven factor. The El Nino/Southern Oscillation (ENSO) events are also considered have large effect in the MLYRB, which can be regarded as climate driven factor. In the study, using terrestrial water storage change anomalies (TWSA) from Gravity Recovery and Climate Experiment (GRACE) mission and hydrological data, we investigate the effect of TGD and ENSO on the TWSA in MLYRB and its sub-basins. From the routinely impoundment of TGD since October 2010, the TWSA and ENSO show high correlation greater than 0.75 with a 5-month time lag, except for the upper Han River basin which is large affected by the Danjiangkou reservoir, and during two extreme flood and drought events, the TWSA and ENSO are almost consistent. It is concluded that the TWSA in the MLYRB is mainly affected by the climate driven factor, but the impoundment of TGD has limited effect. Since the relationship between TWSA and ENSO is stable during the routinely impoundment of TGD, the extreme events occurred in the MLYRB can be early warned by the ENSO index. 

How to cite: Jin, T., Li, X., and Cai, Z.: The water storage change anomaly and its causes in the middle-lower reaches of Yangtze River basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20937, https://doi.org/10.5194/egusphere-egu2020-20937, 2020.

Both the IPCC and the 2017 US Decadal Survey for Earth Science and Applications have recognized atmospheric profiling as an immediate priority, as proper representation of the Earth’s vertical atmosphere is imperative to close gaps in our understanding of processes that impact severe weather, air quality, and climate change. Radio Occultation (RO) techniques have been recognized for their uniqueness to provide reference datasets, triggering a growing interest in using RO for Climate and Weather applications.

 At the NASA Jet Propulsion Laboratory (JPL), physical parameters such as refractivity and derived atmospheric products (temperature, pressure, moisture) are obtained by applying inversion methodologies on the atmospheric delay induced on the occulted signal. Such multi-mission retrieval system has generated nearly two decades of observations and allowed the generation of Global Navigation Satellite Systems Radio Occultation (GNSS-RO) monthly gridded data for climate model evaluation and other applications (Obs4MIPS). 

We present an overview of data and methodology involved in producing Obs4MIPS GNSS-RO data, and show current improvements in the legacy products by comparison against the next generation of JPL’s monthly gridded data (Level 3) products.  Also, we evaluate the performance of the products against reanalysis datasets, and demonstrate its capability to detect climate signals and to improve our understanding of weather processes. Additionally, we will discuss ongoing activities associated with the incorporation of the recently launched COSMIC-2 data into our system.

How to cite: Oyola, M., Ao, C., Verkhoglyadova, O., and Mannucci, A.: Multi-Mission GNSS Radio Occultation Climate Data Records at the Jet Propulsion Laboratory, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21456, https://doi.org/10.5194/egusphere-egu2020-21456, 2020.

Spatial filtering is an effective way to identify and reduce the so-called common mode error (CME) from the regional GPS networks measurements, which could improve GPS positioning accuracy and precision for detection of subtle crustal deformation signals. In this work, we decompose GPS coordinate time series into a set of temporally varying modes with the widely used principal component analysis (PCA) on minizine the misfit calculated using a L2 norm(x2). The results show that the decomposed components from PCA are not statically independent to each other. It is difficult to reveal the original geophysical mechanisms for the related signals only on the PCA results. To work around the problems, we reanalysis the output from PCA to recovery and separate the original signals from mixed observations with the independent component analysis (ICA). Here, we firstly apply the PCA methods on the GPS position time series in Sichuan_Yunnan region of China to evaluate the ability in discerning and charactering different source of crust deformation in the space and time domains. Using the PCA decomposed first 6 PCs, we find that the spatially and temporally correlated CME can be decomposed into two independent components by ICA, the second IC shows obvious variations in the beginning of each year, the same characters are also seen in the atmosphere press variations. Then we compare the two timeseries and demonstrated that atmosphere high frequency pressure mass loading is one of the main contributors to the CME.

How to cite: Tan, W., Chen, J., and Qu, W.: Applying the ICA method to extract the potential signals in GNSS timeseries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6329, https://doi.org/10.5194/egusphere-egu2020-6329, 2020.

Since nearly 3 decades, high-precision satellite altimetry allows us to precisely measure the mean sea level evolution at global and regional scales. In terms of global mean, sea level is rising at a mean rate of 3.2 mm/yr. The altimetry record is also suggesting that the global mean sea level rise is accelerating. However, the exact value of the acceleration and even its mere existence are still debated. Determination of the global warming-related sea level rate and acceleration are somewhat hindered by the interannual signal caused by natural climate variability. During the recent years, several studies have shown that at interannual time scale, the global mean sea level is mostly due to ENSO-driven land water storage variations. But thermal expansion fluctuations may also contribute. Thus, to isolate the global warming signal in the global mean sea level, we need to remove the ENSO-related interannual variability. For that purpose we use the Water Gap Global Hydrological model developed by the University of Frankfurt for land water storage as well as GRACE space gravimetry data on land and empirical models based on ENSO indices. We also extract the ENSO-related signal in thermal expansion. After removing the total interannual variability signal due to both mass and steric components, we compute the evolution with time of the ‘residual’ rate of sea level rise over successive 5-year moving windows, as well as the associated acceleration. Using time series of thermal expansion and ice sheet mass balances, we also estimate the respective contributions of each component to the global mean sea level acceleration.

How to cite: Moreira, L., Cazenave, A., Cáceres, D., Palanisamy, H., and Dieng, H.: The rate and acceleration of the global mean sea level revisited, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4643, https://doi.org/10.5194/egusphere-egu2020-4643, 2020.

The Gravity Recovery and Climate Experiment (GRACE) satellite mission has profoundly advanced our knowledge of contemporary sea level change. Owing to the coarse spatial resolution and leakage issue across the land-ocean boundary, it is challenged for GRACE to detect mass changes over a region smaller than its spatial resolution, especially a semi-enclosed basin that is adjacent to land with significant mass variation. In this contribution, we find that GRACE is capable of recovering mass increase in the Bohai Sea, which is adjacent to the North China Plain that has been experiencing significant groundwater depletion. This water mass increase, only amounting to 0.45 Gt/yr, is demonstrated by a reconstruction that is implemented with multisource data, including altimeter observations, steric estimates, and hydrology model. The reconstructed mass signal rejects the detection of sediment accumulation by GRACE, but it does not exclude the possibility that sediment accumulation may occur at local scale. Compared with the “true” mass increase, the mass increase observed by GRACE spherical harmonic coefficients (SHCs) is seriously compromised (i.e., signal magnitudes are substantially reduced) due to leakage issue. Our reconstruction results exemplify that elaborate data-processing is necessary for specific cases. On the other hand, the recently released mascons, which are resolved with constraints and require no further processing, suggest improved seasonal cycles in the Bohai Sea that are in agreement with altimeter observations. However, the rates derived from the mascons cannot properly represent the real ocean mass increase for the Bohai Sea, because the mascons underestimate the rates or contain some artificial effect. Nevertheless, the mascons provide new insights into regional sea level change relative to the traditional SHCs.

How to cite: Mu, D. and Xu, T.: GRACE observing a small scale ocean mass increase in the Bohai Sea , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-193, https://doi.org/10.5194/egusphere-egu2020-193, 2020.

The Atlantic Meridional Overturning Circulation (AMOC) plays a key role in our global climate system and is the main mechanism of northward heat transport for a warm climate in Northern Europe. Despite its crucial role, the AMOC is only scarcely observed, as observations covering all of the Atlantic Ocean for extended time are difficult to obtain. Satellite gravimetry offers key advantages compared to existing in-situ data sources by providing ocean bottom pressure anomalies with global coverage, thus allowing the monitoring of the AMOC in the complete Atlantic Ocean basin. The Gravity Recovery And Climate Experiment (GRACE) satellite mission and its successor GRACE Follow-On have provided a nearly continuous time series of monthly gravity field snapshots since 2002. In contrast to in-situ measurements of ocean bottom pressure, which suffer from inherent drift problems, the temporally stable satellite observations allow investigations of the long-term AMOC behavior.

Preliminary studies have shown that monitoring changes in the AMOC is possible with observations from GRACE and GRACE Follow-On, however, it is pushing the limits of the current data products in resolution and accuracy. To fully exploit the information content in the gravity observations, we implemented a processing chain tailored to the Atlantic Ocean basin. Compared to existing approaches, we perform signal separation, that is the reduction of continental hydrology and glacial isostatic adjustment, on the satellite sensor data level. This has the key advantage that all background models are treated the same, thus are spectrally coherent. Geocenter motion is estimated in combination with an ocean model, as is the state-of-the-art for GRACE/GRACE-FO processing. Ocean bottom pressure anomalies are then computed through least squares collocation, which allows for point distributions tailored to the bathymetry. This consistently processed data record is then used to gauge the performance of satellite gravimetry for monitoring the AMOC.

How to cite: Kvas, A., Bentel, K., Behzadpour, S., and Mayer-Gürr, T.: Monitoring the AMOC with GRACE/GRACE-FO - How far can we push the spatial resolution?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3468, https://doi.org/10.5194/egusphere-egu2020-3468, 2020.

It is already well known that intra-seasonal oscillations in the Earth’s global temperature are driven by ENSO (El Niño Southern Oscillation) events. ENSO signal is also present in length of day and global sea level rise, because during El Niño the increase of the length of day and global sea level rise can be noticed. To detect common oscillations in length of day, global sea level rise, global temperature data and ENSO indices the wavelet-based semblance filtering method was used. This method, however, seeks the signals with a good phase agreement of oscillations in two time series thus, no phase agreement results in very small amplitudes of the common signals. The spectra-temporal semblance functions allow detecting the similarity of two time series in spectral bands in which the amplitudes and phases of the oscillations are consistent with each other. The amplitudes of oscillations in the considered data vary in time and in order to detect the signals with similar amplitude variations between pairs of time series the normalized Morlet wavelet transform (NMWT) and the combination of the Fourier transform bandpass filter with the Hilbert transform (FTBPF+HT) were used. These two methods enable computation of the instantaneous amplitudes and phases of oscillations in two real-valued time series. In order to detect oscillations with similar amplitude variations in two time series correlation coefficients between the amplitude variations as a function of oscillation frequencies were computed.

How to cite: Kosek, W.: Application of spectra-temporal analysis methods to detect common signals in length of day, global sea level rise, global temperature data, and ENSO indices, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8196, https://doi.org/10.5194/egusphere-egu2020-8196, 2020.

Surface geology and seismic tomography show that the properties of Earth’s internal structure vary laterally. Lateral heterogeneity has been demonstrated to have considerable effect on the observables of Glacial Isostatic Adjustment (GIA) such as surface deformation, geoid and sea-level change. A number of models have been developed to consider a complex viscous structure of the Earth by implementing 3D viscosity for linear or nonlinear creep laws. However, there are only few studies addressing lateral heterogeneity in the (an-)elastic structure.

Due to the increased accuracy of global observation systems like GNSS and an integrated interpretation of earth system processes, the demand for improved global deformation models for instantaneous to annual loading is rising. To analyse the effect of lateral heterogeneity on a global scale, we extend the spectral–finite element method suggested by Martinec for a viscoelastic body to compute the deformations and gravitational potential changes of an elastic spherical self-gravitating Earth. The effect of 3D elastic structure is studied by varying the elastic moduli in the crust and mantle. We present a sensitivity study in order to quantify its effect on solid-earth deformations on a regional to global scale.

How to cite: Huang, P., Tanaka, Y., Klemann, V., Martinec, Z., and Thomas, M.: Surface deformations of a 3D elastic self-gravitating Earth, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7733, https://doi.org/10.5194/egusphere-egu2020-7733, 2020.

Glacial Isostatic Adjustment (GIA) is the visco-elastic response of the Solid Earth to changes in the ice sheet load during past glacial cycles. GIA produces vertical land motion and mantle mass redistribution, both of which are important to include when studying surface deformations, sea level rise, present day mass changes from satellite data and changes in the geoid. Estimates of GIA are typically obtained from forward numerical models that are driven by varying assumptions about Earth rheology and ice load history, leading to a range of GIA estimates. As a consequence, many studies are trying to move away from forward modelling and co-estimate GIA from contemporary observations. We present a novel theoretical framework that uses GPS vertical land motion and GRACE data to provide a data-driven estimate of GIA. Assuming that all other significant processes are correctly identified and accounted for, we show that GRACE and GPS data can successfully be used together to isolate GIA. We compare our results to outputs from various GIA forward models.

How to cite: Ziegler, Y., Vishwakarma, B. D., Royston, S., Brady, A., Chuter, S., Westaway, R., and Bamber, J.: A novel theoretical framework to estimate GIA from GPS and GRACE, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19815, https://doi.org/10.5194/egusphere-egu2020-19815, 2020.

The two most widespread geodynamic signals in Iceland are caused by glacio isostatic adjustment (GIA; up to 4.5 cm/yr vertical motion) and tectonic plate spreading (approximately 1.9 cm/yr horizontal motion). GPS measurements of crustal deformation started in Iceland in 1986 and annually tens to hundreds of benchmarks are re-measured. Many of these surveys are on local scales, but the ISNET campaigns in 1993, 2004, and 2016 are the only island-wide efforts. Continuous GPS (cGPS) measurements started in 1995 and now over 100 cGPS stations are running. The cGPS allows for excellent quantification of seasonal variations in position with amplitude up to several cm closest to the glaciers, driven mainly by seasonal snowload. Frequent observations also help to observe temporal changes in uplift rates and correlate to glacier mass balance. In recent years InSAR has been applied to obtain both local signals (e.g., due to glacial surges) and island-wide estimates of GIA and plate motion. However, InSAR does not work under the glaciers where we expect the largest uplift. Regular GPS measurements at several nunataks on Vatnajökull started in 2008 and provide the only intra-glacier GIA observations in Iceland. Going further backwards in time is a challenge and relies on local levelling where relative uplift rates can be compared to current relative uplift rates to infer the temporal evolution.

During 1993-2004 the average observed uplift rates reached at most around 2 cm/yr and were likely at its lowest in the early 1990s, lower than during 1959-1991. During 2004-2010 the uplift rates increased on average by 70% compared to the previous time period. A thin layer of ash from the 2010 Eyjafjallajökull eruption enhanced the melting rates and is clearly seen as enhanced uplift rates during 2010-2012. Until 2014 the uplift rates remained high. In 2014 the average uplift rates lowered by around 20%. Comparable changes are observed in the horizontal deformation field. Overall, recent changes in GIA broadly follow changes in climate and mass balance. The first part of the 90s was cold and glaciers in Iceland were overall in equilibrium or gaining a bit of mass. After 1995 the glaciers started losing considerable mass every year. From 2011 the mass loss decreased; in 2015 there was a net mass gain, and in 2017 and 2018 the mass balance was close to equilibrium. The highly variable deformation rates call for a re-evaluation of the current GIA models, working towards a time-dependent response that can be applied to regional deformation studies.

How to cite: Geirsson, H., Valsson, G., Ófeigsson, B. G., Sturkell, E., Arnadottir, T., LaFemina, P. C., Hreinsdottir, S., Drouin, V., Schmidt, P., Lund, B., and Palsson, F.: Significant temporal changes in glacio isostatic adjustment in Iceland during the 1950s to present, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17390, https://doi.org/10.5194/egusphere-egu2020-17390, 2020.

Satellite Laser Ranging (SLR) data of the geodynamic satellite Lageos-1 (LAser GEOdynamics Satellite) for the period January 2000 - June 2013 are processed and analysed through sequential estimation to obtain multiyear solution for global geodetic parameters - coordinates and velocities of 37 stations located on the main tectonic plates. The analysis is carried out with the Satellite Laser Ranging Processor (SLRP) software, version 4.3, developed in the Department Geodesy of the National Institute of Geophysics, Geodesy and Geography at Bulgarian Academy of Sciences. The software consists of two main programs – orbit determination and parameter estimation modules. Total number of 202 447 measurements are processed and analyzed by monthly batches. Arc dependent parameters, geogravitational parameter - GM, Earth Orientation Parameters (pole coordinates and length of the day - LOD), along track and solar radiation pressure coefficients are obtained from monthly solutions. The weighted root mean squares of the monthly station coordinates solution are between 2 and 16 mm. The analysis of monthly GM time series reveal value of the secular trend Ġ/G = -3.31. 10^-13yr^-1. The results obtained contribute to the monitoring of recent tectonics of the major continental plates and global geodynamic parameters.

How to cite: Dimitrov, N., Georgiev, I., and Ivanov, A.: Global geodetic parameters obtained from 14 years Lageos 1 Satellite Laser Ranging, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15816, https://doi.org/10.5194/egusphere-egu2020-15816, 2020.

We studied front-type heavy rain and typhoon-type heavy rain in 2019 in Japan, using tropospheric delay data from the dense Global Satellite Navigation System (GNSS) network GEONET. In 2019, based on data from Japan Meteorological Agency (JMA), that front type heavy rain occurred on 26-29 August 2019, and typhoon type heavy rain occurred on 10-13 October 2019.

In this study, we analyzed the behavior of water vapor during heavy rainfall, using tropospheric parameters obtained from a database at the University of Nevada, Reno (UNR). Data sets, including delays in gradient vectors in the troposphere (G), as well as delays in the zenith troposphere (ZTD), are estimated every 5 minutes. Initially, we interpolated G to get grid points. We removed the hydrostatic delay from ZTD to get zenith wet delay (ZWD). In the inversion scheme, we use G at all GEONET stations and ZWD data at low altitude GEONET stations (<100 m) as input. Then we assume that the spatial change in ZWD is proportional to G (Gx = H δZWD /δx, where H is the height of the water vapor scale) and the estimated height of sea-level ZWD at grid points throughout Japan.

We try to justify our working hypothesis that heavy rains occur when the convergence of G and ZWD sea levels is high by analyzing the hourly water vapor distribution on all days in August 2019 and October 2019. We found that both values ​​show a maximum in the period studied when two events heavy rain occurred, i.e., August 27, 2019, and October 12, 2019. Furthermore, we studied the analysis of high time resolution (every 5 minutes) on heavy rain days. The results show that the convergence of G and ZWD sea level rises before rain occurs, and ZWD shows a rapid decline once heavy rain begins.

How to cite: Arief, S. and Heki, K.: A new approach that utilizes the GNSS atmospheric delay gradient to monitor front-type heavy rain and typhoon-type heavy rain in 2019 in Japan., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2646, https://doi.org/10.5194/egusphere-egu2020-2646, 2020.

Changes in terrestrial water storage as observed by the satellite gravity mission GRACE represent a new and completely independent data set for constraining the net flux deficit of precipitation (P), evapotranspiration (E), and lateral runoff (R) in atmospheric reanalyses.

In this study we use daily GRACE gravity field changes to investigate high-frequency hydro-meteorological fluxes over the continents. Band-pass filtered water fluxes are derived from GRACE water storage time series by first applying a numerical differentiation filter and subsequent high-pass filtering to isolate fluxes at periods between 5 and 30 days.

We can show that on these time scales GRACE is able to identify quality differences between different reanalyses, e.g. the improvements in the latest reanalysis ERA5 of the European Centre for Medium-Range Weather Forecasts (ECWMF) over its direct predecessor ERA-Interim. We will therefore use GRACE as an evaluation tool to compare hydro-meteorological fluxes in various global atmospheric reanalyses, such as ERA5(-Land), ERA-Interim, Merra2, JRA-55, or NCEP.

How to cite: Wöhnke, V., Eicker, A., Jensen, L., Kvas, A., Mayer-Gürr, T., and Dobslaw, H.: Evaluating short-term hydro-meteorological fluxes in global atmospheric reanalyses using daily GRACE data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4502, https://doi.org/10.5194/egusphere-egu2020-4502, 2020.

Quantifying individual contributors to global and regional mean sea level along with corresponding uncertainties is crucial for future projections. However, the contribution of terrestrial hydrology seems to be the least known, but is particularly important, since in addition to the climate-driven changes human activities (such as groundwater pumping, irrigation, deforestation) have a large impact on global sea level changes. Under the common assumption that atmospheric water storage change is negligible, (total) terrestrial water storage anomalies (TWSA) represents a proxy for the hydrologic contribution. Generally, TWSA can be derived using models, observations or a combination of both. Each of the methods has its pros and cons.

In this study, we estimate the contribution of terrestrial hydrological cycle changes to global mean sea level along with corresponding uncertainties for 2003 - 2016 based on land TWSA time series derived (i) from WaterGAP Global Hydrological Model WGHM that also simulates anthropogenic effects and provides a partitioning of TWSA into global river discharge and evapotranspiration minus precipitation, (ii) satellite gravimetry data from GRACE, and (iii) from a joint inversion using GRACE and altimetry data. To realistically describe uncertainties in forcing data, model parameters, initial water states, and errors in the model structure, an ensemble of 30 runs is generated and analyzed. Because of well-known large inter-annual and decadal hydrological variations, we estimate time-varying trends using a Kalman filter framework in addition to the usually estimated linear trends. This approach provides more reliable trend and corresponding uncertainty estimates. Moreover, it naturally enables detecting any changes in rates, which is acceleration.

How to cite: Mielke, C., Engels, O., Uebbing, B., Gerdener, H., Börger, L., Schulze, K., Döll, P., and Kusche, J.: How uncertain is the hydrological contribution to global sea level based on hydrological modelling compared to observational data?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5802, https://doi.org/10.5194/egusphere-egu2020-5802, 2020.

The Indo-Gangetic Plain, feeding more than 9 billion people, are facing serious water scarcity due to expanding populations and development in agriculture and industry. Rainfall concentrated in monsoon season, about 70% of precipitation falls between June and September, causes the imbalance between water supply and demand. A large amount of groundwater is extracted for irrigation during dry season, causes the groundwater to decline. Increasing glacier meltwater under the ongoing warming of global climate from upstream high mountainous also modulates the variation of terrestrial water storage (TWS) in this region. Thus, estimating and evaluating anthropogenic water depletion are beneficial to water resources protection and management in the Indo-Gangetic Plain.

Here, we propose a method to remove the influence of climate variability and obtain human-driven TWS variability. Atmosphere-driven TWS variability is estimated by a relationship between change in TWS (GRACE data) and precipitation and temperature, which has been confirmed that these two variables (precipitation and temperature) already explain a substantial fraction of continental-scale run off dynamics in previous studies. Glacier melting recharge from upstream high mountainous is calculated by the proportion with the temperature.

Results show that the rate of anthropogenic depletion of water in Indus Plain increased from -5.5 km³/yr to -25.0 km³/yr during 2003 - 2011 due to the deficient precipitation, and remained stable from 2011 to 2016 at the rate of ~-26.0 km³/yr with increasing precipitation and enhancing glacier meltwater recharge. The rate of anthropogenic depletion of water in Ganges Plain (including the Brahmaputra River) slowed from -37.7 km³/yr to -12.0 km³/yr during 2003 -2011due to the increased glacier meltwater recharge, which reduced the pressure of irrigation water in northwest of the Plain. However, with the increasing temperature since 2014, The rate of anthropogenic depletion of water increased to -20.0 km³/yr in 2016.

How to cite: Liu, B., Zou, X., and Li, J.: Anthropogenic water depletion in the Indo-Gangetic Plain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6125, https://doi.org/10.5194/egusphere-egu2020-6125, 2020.

To better understand global freshwater resources, we combine the state-of-the-art global hydrological model WGHM with Total Water Storage Anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission in an ensemble-based calibration and data assimilation (CDA) framework. However, when dealing with GRACE data, their limited horizontal resolution represents a major challenge. Filtering and/or ’destriping’ is the usual approach for suppressing GRACE-specific spatial noise, which causes spatial leakage and in turn attenuation of signal and reduction of spatial resolution. In GlobalCDA project, we derive altimetry-based storage variations along with corresponding uncertainties of surface water bodies, such as lakes and reservoirs, that feature significantly higher spatial resolution compared to GRACE-based TWSA. These can, additionally, be incorporated into the CDA framework.

In this study, we investigate several possibilities on how to use the additional remote sensing observations within the CDA over the Mississippi basin for the time span 2003 - 2016. For this, we run the CDA (i) using GRACE-based TWSA only, (ii) removing altimetry-based storage variations of surface water bodies from GRACE-TWSA, (iii) removing and restoring altimetry-based storage variations for GRACE-TWSA, and (iv) directly using altimetry-based storage variations. New observation operators are constructed for (ii) and (iv). The results are validated against independent discharge observations.

How to cite: Engels, O., Schulze, K., Kusche, J., Deggim, S., Eicker, A., Mayr, S., Klein, I., Ellenbeck, L., Dettmering, D., Schwatke, C., Elmi, O., Tourian, M., and Döll, P.: Analyzing different ways of assimilating volume change estimates for surface water bodies into a hydrological model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7196, https://doi.org/10.5194/egusphere-egu2020-7196, 2020.

Global-mean sea level rises (GMSLR) by 3.1-3.5 mm a^-1 (1993-2017) and of which about 50 % can be attributed to changes in global-mean ocean mass due to hydrological variations, mass changes of land glaciers, and mass changes of the major ice sheets in Greenland and Antarctica. The ice-sheet contributions account for more than the half of the contemporary ocean mass change and can be observed with time-variable gravimetry by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO). In addition, geometric surface changes due to the volume change of ice sheets is also observed by polar altimetry missions. Of particular importance here is the signal of glacial isostatic adjustment (GIA) which is superimposed with ice mass change.

Conventionally, the gravimetry and ice-altimetry observations are processed independently. For ocean applications, a global fingerprint inversion (Rietbroek et al., 2016) allows to estimate individual mass and steric contributors to the sea-level budget by combining GRACE and ocean-altimetry data in a joint approach. To improve the estimates of the ice-sheet contributions to GMSLR, we present first results from additionally incorporating independent ice-altimetry data over Greenland and Antarctica into the fingerprint inversion. We examine the sensitivity of the sea-level contributions to the additional ice-altimetry data (from ERS-2, Envisat, ICESat, CryoSat-2 missions) and provide validation against independent estimates. In our standard runs, GIA is accounted for as an a-priori correction during the inversion. However, we demonstrate the potential and limitations of a regional inverse approach in which GIA is separated from ice mass change over Antarctica using GRACE and ice altimetry. In our future work, we aim to parametrise and co-estimate GIA within the global inversion framework.

How to cite: Willen, M. O., Uebbing, B., Horwath, M., Kusche, J., Rietbroek, R., Strößenreuther, U., and Schröder, L.: Improving the representation of ice-sheet mass changes in the global inversion for sea-level contributions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7999, https://doi.org/10.5194/egusphere-egu2020-7999, 2020.

Gulf of Cadiz from the Strait of Gibraltar to the Western Coast of the Iberian Peninsula is a natural hazard risky region due to the existence  of several active faults related to the Eurasian and Nubian Plate interaction. The 1755 Lisbon Earthquake was remarkable example. With the epicenter located SW of Cape San Viente, and a Richter scale magnitude around 8.3, the earthquake triggered a devastating tsunami hitting the Portuguese, Moroccan and Southern Spain coasts, resulting in thousands of casualties. More recently, in 1969 a 7.8 magnitude earthquake with its epicenter located in the same region, originated another tsunami but smaller than the previous one, resulting nineteen casualties.

To prevent natural hazards like these, the Astronomy, Geodesy and Cartography Laboratory at the Universidad de Cadiz, is drawing and implementing early warning systems, trying to detect and evaluate tectonic activity in near real time at the Gulf of Cadiz. The system includes the GNSS network SPINA  receivers together with MEMS acelerometers, meteo equipments, and ancillary instrumentation for data adquistion, monitoring, quality control and results display at a dedicated control center.

How to cite: Garate, J., Ramirez Zelaya, J., Rosado, B., Berrocoso, M., de Gil, A., Fernandez Ros, A., Prates, G., and Peci, L. M.: Developing geodetic tools for early warning and monitoring of tectonic activity at the Gulf of Cadiz, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8162, https://doi.org/10.5194/egusphere-egu2020-8162, 2020.

Temporal variations in the total ocean mass representing the barystatic part of present-day global mean sea-level rise can be unambiguously inferred from time-series of global gravity fields as provided by the GRACE and GRACE-FO missions. A spatial integration over all ocean regions, however, largely underestimates present-day rates as long as the effects of spatial leakage along the coasts of in particular Antarctica, Greenland, and the various islands of the Canadian Archipelago are not properly considered.

Based on the recent release 06 of monthly gravity fields processed at GFZ, we quantify (and subsequently correct) the contribution of spatial leakage to the post-processed mass anomalies of continental water storage and ocean bottom pressure. Utilising the sea level equation allows to predict spatially variable ocean mass trends out of the (leakage-corrected) terrestrial mass distributions from GRACE and GRACE-FO. Consistent results for the global mean barystatic sea-level rise are obtained also from spatial integrations over ocean masks with different coastal buffer zones ranging from 400 to 1000 km, thereby confirming the robustness of our method. Residual month-to-month variations in ocean bottom pressure are indicative for errors in the monthly-mean estimates of the applied de-aliasing model AOD1B RL06 and will be thus contrasted against very recent MPIOM experiments considered for AOD1B RL07. The in this way improved leakage correction will be implemented in future GravIS versions (http://gravis.gfz-potsdam.de).

How to cite: Klemann, V., Dobslaw, H., Bagge, M., Dill, R., Thomas, M., Dahle, C., and Flechtner, F.: Mitigating Spatial Leakage in Monthly GRACE/GRACE-FO Gravity Fields for the Separation of Barystatic Sea-Level Variations and Residual Ocean Circulation Effects, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7652, https://doi.org/10.5194/egusphere-egu2020-7652, 2020.

Satellite gravimetry observations from GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On are widely used to study the co-seismic and post-seismic deformations caused by large earthquakes. Temporal gravity changes from GRACE provide good constraints to investigate the fault slips of large earthquakes especially for oceanic areas. However, reliable retrieval of seismic signals is still challenging due to large uncertainties and limited spatial and temporal resolutions of GRACE observations. To extract the co- and post-seismic signals from GRACE, the time series fitting method based on least squares is commonly used. In the time series fitting, the earthquake occurrence time parameter (t0) is usually set at the mid-month point, since most available GRACE time-variable data are monthly solutions. Nevertheless, a lot of large earthquakes did not occur exactly at mid-month. By simulative tests, we demonstrate that the commonly used mid-month approximation for the fitting parameter t0 can cause noticeable bias for the seismic signal extraction. The several-days deviation in the parameter t0 leads to obvious difference for the time series fitting of seismic signals, since the post-seismic changes are rapid and significant within a short period after the earthquake. With the case study of the 2004 Mw9.1 Sumatra-Andaman earthquake (which occurred on December 26), we indicate that the bias due to the commonly used mid-month t0 approximation reaches above 10 percent amplitude of the extracted co-seismic signals. Thus the exact date for the fitting parameter t0 should be used for more reliable separation of the co- and post-seismic signals from GRACE observations.

How to cite: Li, J., Chen, J., Wang, S.-Y., Tang, L., and Hu, X.: On the separation of co- and post-seismic signals due to large earthquakes from GRACE observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12944, https://doi.org/10.5194/egusphere-egu2020-12944, 2020.

High correlations between length-of-day (LOD) and climate variables (sea-surface temperature, surface air temperature) have been pointed out in numerous studies (e.g., Lambeck and Cazenave 1976, Dickey et al. 2011, Marcus 2016) in the recent years at both decadal and multidecadal time scales. Moreover, the multidecal LOD variations (that reach several milliseconds) have been shown to have their origin in variations in the core angular momentum and are associated with variations of the Earth magnetic field now modeled back to the middle of the 19th century. Though the climate variations unlikely arise from the core, some authors suggested that they could result from modulation of incoming cosmic ray flux by Earth's magnetic field through cloud formation. In this study, we propose to check correlations between LOD, Earth dipolar magnetic field, and climate variables as taken from a century reanalysis (gridded air temperature and cloud coverage from NCEP 20th Century Reanalysis V2 and V3) in order to (i) confirm results of previous studies about a possible causality between geomagnetism, LOD, and climate, and (ii) locate the hot spots where the link between geomagnetism and cloud formation could be significant.

How to cite: Lambert, S.: The Intriguing Relation Between Earth's Rotation, Geomagnetic Field, and Climate at Multidecadal Time Scales: Insights from NCEP 20th Century Reanalysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10437, https://doi.org/10.5194/egusphere-egu2020-10437, 2020.

Originating from econometrics, the concept of Granger causality (GC) has been widely used in a variety of fields, including climate sciences, to infer directional dependencies between stochastic variables.  Going one step further than the simple detection of lag-correlations, GC evaluates the directed interaction of a variable Y on a variable X by quantifying the improvement of prediction of future values of X when past values of Y are considered or omitted. Although not prescribed initially as such, GC is routinely computed from an estimated vector autoregressive model of the data of interest X, with and without the exogenous variable Y. However, such a modelling is somewhat restrictive and not suitable for filtered, sampled and noisy time series which may contain a moving-average component, impairing at the same time the quality of the GC estimator. Conversely, state-space representation offers a much more general framework for linear time series modelling.

In this study, we use Granger causality in the framework of a state-space modelling of time series to infer the presence of causal influences of the sea surface temperature (SST) and the 500hPa geopotential height on the Terrestrial Water Storage Anomaly (TWSA) over Australia[PS1] . A first and critical step is to reduce the high-dimension of the spatio-temporal data to a size compatible with classical state-space modelling algorithms. To do that we extract a limited number of leading modes of variability from the geophysical fields. Next, the state-space models of the extracted modes are identified using subspace-based methods. Then, the Granger causality of every mode of SST (resp. 500hPa geopotential height) on TWSA is estimated. Finally, we discuss the capability of the presented method to detect real directional dependencies in the light of current knowledge on Australia’s rainfall climatology and compare it to the results obtained with the classical vector autoregressive models.

How to cite: Douch, K., Saemian, P., and Sneeuw, N.: Investigating Granger causality with state-space representation of time series: a case study of the total water storage anomaly over Australia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10684, https://doi.org/10.5194/egusphere-egu2020-10684, 2020.

The Gravity Recovery and Climate Experiment (GRACE) measurements have provided global observations of total water storage (TWS) changes at monthly intervals for almost 20 years. They are useful for estimating changes in groundwater storage (GWS) after extracting other water storage components like soil water or snow water.

In this study, we analyse the GWS variations of two main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ wells measurements, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programme’s Coupled Model Intercomparison Project Phase 5) climate data. The research is conducted for the period between September 2006 and October 2015.

Here, TWS is taken directly from GRACE measurements and also computed from all considered models. GWS is obtained by subtracting the modelled sum of soil moisture and snow water from the GRACE-based TWS. The resultant GWS series are validated by comparing with appropriately calibrated in-situ wells measurements. For each GWS time series, the trends, spectra, amplitudes, and seasonal components were computed and analysed. The results suggest that in Poland there has been generally no major GWS depletion. The results can contribute toward selection of an appropriate model that, in combination with GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate in-situ groundwater storage measurements.

How to cite: Nastula, J., Śliwińska, J., Rzepecka, Z., and Birylo, M.: Analysis of groundwater storage changes in main Polish river basins using GRACE observations, in-situ data, and hydrological and climate models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13554, https://doi.org/10.5194/egusphere-egu2020-13554, 2020.

 DORIS system aims to provide precise orbit determination of low earth orbit satellites, and the dual-frequencies on S1=2036.25 MHz and U2=401.25 MHz were used on DORIS signals. The ionosphere TEC retrieval on the signal path is possible based on DORIS dual-frequency observations.

Analysis results show that DORIS pseudo-ranges had noise with several kilometers level, hence only the carrier-phase observations could be utilized on TEC retrieval. Moreover, as the DORIS ground stations were thousands kilometers separated with each other, station differential cannot be guaranteed and the data preprocessing can only be done base on the un-difference observations before the TEC could be precisely determined.

In this research, a polynomial function was applied to model the DORIS phase observations, and minimal detectable biases (MDB) of less than one cycle wavelength was used as the index on the cycle-slip detection. And then the geometry free combination of S1 and U2 phase measurements were calculated for each DORIS LEO satellite passing arc. Finally, the unknown ambiguities bias on S1 and U2 geometry free observables were shifted to coincide with STEC calculated from the IGS GIM products.

Both the Jason-2 & 3 based DORIS observations were used for the validation, several simulated +5 and -1 cycle-slip events on both DORIS observation could be clearly detected and correctly repaired. And the calculated STEC on one satellite passing arc from the LEO satellite to station show well agreement with IGS STEC on continent area, and the differences on ocean areas could be used to prove that the IGS GIM products were less precise on those areas.

How to cite: Zhang, S., Li, W., Yin, F., and Gou, H.: Data pre-processing for ionosphere TEC retrieval based on DORIS observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21943, https://doi.org/10.5194/egusphere-egu2020-21943, 2020.

The Dynamic ocean Topography (DT) describes the deviation of the true ocean surface from a hypothetical equilibrium state ocean at rest forced by gravity alone. With the geostrophic surface currents obtained from its gradients the DT is an essential parameter for describing the ocean dynamics. Observation-based global temporal Mean geodetic DTs (MDTs) are obtained from the difference of altimetric Mean Sea Surface (MSS) and the geoid height, that equipotential surface of gravity closest to the ocean surface.

The geoid is provided either as a satellite-only, or a combined model including in addition gravity anomalies derived from satellite altimetry and ground data. In recent years the focus was on satellite-only models, produced from new space-born observations obtained from the Gravity Recovery and Climate Experiment (GRACE) and Gravity field and Ocean Circulation Explorer (GOCE) satellite missions. Moreover, combined geoid models are only cautiously used for MDT calculation, since it is still in question to what extent the gravity information obtained from altimetry is distorted by the MDT information included therein and how this translates into errors of the MDT.

Here we want to concentrate on MDT models based on recent combined geoid models. An assessment is provided based on comparisons to near-surface drifter data from the Global Drifter Program (GDP). Besides providing a general, global assessment, we focus on signal content on small scales, addressing mainly two questions: 1) Do MDTs obtained from combined geoid models contain signal for scales smaller than resolvable by the
satellite-only models? 2) Is there a maximum resolution beyond which no signal is detectable?

Until recently, these questions couldn't be answered since low resolution MDTs usually contained strong wavy-structured errors and thus needed a strong spatial filtering thereby killing the smallest scales resolved in the MDT. These errors, which worsen with lower resolution, are caused by Gibbs effects provoked by imperfections in bringing the high resolution ocean-only MSS models into spectral consistency with the much lower resolved global geoid model. A new methodology, however, improves the necessary globalization of the MSS. After subtraction of the geoid model, subsequent cutting-off the signal beyond a specific spherical harmonic degree and order (d/o) results in an MDT without any Gibbs effects, also for low resolution models.

To answer the questions posed above applying the new methodology, the scale-dependent signal in MDTs for different geoid models is presented for a list of cut off d/os. To minimize the influence of noise on the results we concentrate on strong signal Western Boundary Currents like the Gulf Stream and the Kuroshio. For the Gulf Stream results of a high resolution hydrodynamic model are available and presented as an independent method to estimate the scale dependent signal.

How to cite: Siegismund, F., Oikonomidou, X., and Zingerle, P.: Small-Scale Signal in Mean Dynamic Topographies Applying Combined Geoid Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3512, https://doi.org/10.5194/egusphere-egu2020-3512, 2020.

The fluctuation in vegetation is affected by water availability, while on the same hand vegetation also influences regional water balance. A better understanding of the relationship between variation in vegetation state and water storage change would help explain the complicated interactions between vegetation dynamics and regional water balance. We use total water storage change from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission GRACE Follow-On (GRACE-FO) and Normalised Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR). First, we bring the two datasets to a comparable resolution and then we aggregate the two datasets over the 37 sub-catchments of the Ganga basin. The Pearson correlation coefficient was very high (R > 0.5) for 35 of the 37 sub-catchments when the full signals were used, indicating that the seasonality signals have a high correlation. Once the seasonal signal was removed, the Pearson correlation coefficient became insignificant. We will look into the causes of the lack of correlation between the two residual signals and also perform an autocorrelation analysis to identify the lag between the two variables.

How to cite: Mankar, V., Jhaj, A. S., Jain, S., and Devaraju, B.: What is the relationship between water storage change and NDVI?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-931, https://doi.org/10.5194/egusphere-egu2020-931, 2020.

GRACE and GRACE Follow-On (FO) Level 2 data provide quasi-monthly, band-limited estimates of Stokes (geopotential, spherical harmonic) coefficients mostly reflecting surface mass variability due to non-tidal atmosphere, ocean, and continental hydrology.    
Although space gravimetry does not directly provide CM-related degree-1 Stokes coefficients, GRACE data have been successfully used over the years to complement time series of station positions from global space-geodetic (SG) network when inverting for Center-of-Mass to Center-of-Network (CM-CN) displacements (Wu et al, 2006).

Surficial mass variability observed through GRACE/GRACE-FO can be conveniently converted into load-induced (ENU) deformations at SG observing sites by adopting a spectral (i.e. load Love-number based) formalism and assuming Earth’s response is fully elastic and isotropic. GRACE-derived elastic displacements at observing sites would represent, if accurate, band-limited (degree 2 to 96, or higher if Mascon solutions are adopted) load-induced deformations that can be removed from SG-derived station displacements  in order to more accurately recover degree-1 surface deformation signature (and therefore geocenter motion). 

In this study, we adopt GRACE JPL Mascon RL06 data in conjunction with Preliminary Reference Earth Model-derived load Love numbers to infer elastic displacement at SG sites and remove them from SLR inherently geocentric time series of station positions.
In so doing, the residual SLR station displacements, consistently expressed in a geocentric frame, would in principle reflect a degree-1 deformation signature that can be recovered via either surface deformation (Chanard et al, 2018) or translational approach.

We will compare the SLR/GRACE (CM-CN) determined in this study to standard estimates of geocenter motion such as ILRS’s and JTRF2014’s estimated via translational approach and spectrally inverted solutions (CM-CF).

References
Chanard K et al, (2018). JGR-Sol Ea doi:10.1002/2017JB015245 
Wu X et al, (2006). JGR-Sol Ea doi:10.1029/2005JB004100. 

How to cite: Abbondanza, C., Chin, T. M., Gross, R. S., Heflin, M. B., Parker, J. W., Soja, B. S., Wiese, D. N., and Wu, X.: Geocenter Motion from a Combination of GRACE Mascon and SLR Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10369, https://doi.org/10.5194/egusphere-egu2020-10369, 2020.