Can GPS-Derived Surface Loading Bridge a GRACE Mission Gap?

Rietbroek, Roelof; Fritsche, Mathias; Dahle, Christoph; Brunnabend, Sandra-Esther; Behnisch, Madlen; Kusche, Jürgen; Flechtner, Frank; Schröter, Jens; Dietrich, Reinhard

doi:10.1007/s10712-013-9276-5

Cited by 35 publications

(23 citation statements)

References 35 publications

(43 reference statements)

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“…The model performance was generally high (66%) in the northeastern region compared to the central and western areas (39%). Additional statistical approaches include, but are not limited to: (1) the use of the GPS-derived low-degree surface loading variations to predict the seasonal harmonic variations mapped by GRACE [52]; (2) the application of a statistical model that relates Alaska glacier mass variability to variations in monthly snowfall and temperature fields [53];…”

Section: Introductionmentioning

confidence: 99%

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

et al. 2019

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The GRACE-derived terrestrial water storage (TWS GRACE ) provides measurements of the mass exchange and transport between continents, oceans, and ice sheets. In this study, a statistical approach was used to forecast TWS GRACE data using 10 major African watersheds as test sites. The forecasted TWS GRACE was then used to predict drought events in the examined African watersheds. Using a nonlinear autoregressive with exogenous input (NARX) model, relationships were derived between TWS GRACE data and the controlling and/or related variables (rainfall, temperature, evapotranspiration, and Normalized Difference Vegetation Index). The performance of the model was found to be "very good" (Nash-Sutcliffe (NSE) > 0.75; scaled root mean square error (R*) < 0.5) for 60% of the investigated watersheds, "good" (NSE > 0.65; R* < 0.6) for 10%, and "satisfactory" (NSE > 0.50; R* < 0.7) for the remaining 30% of the watersheds. During the forecasted period, no drought events were predicted over the Niger basin, the termination of the latest (March-October 2015) drought event was observed over the Zambezi basin, and the onset of a drought event (January-March 2016) over the Lake Chad basin was correctly predicted. Adopted methodologies generate continuous and uninterrupted TWS GRACE records, provide predictive tools to address environmental and hydrological problems, and help bridge the current gap between GRACE missions.

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Section: Introductionmentioning

confidence: 99%

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

et al. 2019

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“…However, this high resolution information is limited to the life-time of the GRACE satellites (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) and of the GRACE-FO (Follow On) [5] mission that was launched in May 2018, but suffered a failure of the main instrument processing unit between July and October 2018. To date, no other single satellite mission or proxy is able to bridge the gap between GRACE and GRACE-FO with comparable quality (compare, e.g., [6,7] or [8]). We therefore present a combination on the normal equation level of alternative satellite gravimetric data considering several missions.…”

Section: Introductionmentioning

confidence: 99%

SLR, GRACE and Swarm Gravity Field Determination and Combination

et al. 2019

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Satellite gravimetry allows for determining large scale mass transport in the system Earth and to quantify ice mass change in polar regions. We provide, evaluate and compare a long time-series of monthly gravity field solutions derived either by satellite laser ranging (SLR) to geodetic satellites, by GPS and K-band observations of the GRACE mission, or by GPS observations of the three Swarm satellites. While GRACE provides gravity signal at the highest spatial resolution, SLR sheds light on mass transport in polar regions at larger scales also in the pre- and post-GRACE era. To bridge the gap between GRACE and GRACE Follow-On, we also derive monthly gravity fields using Swarm data and perform a combination with SLR. To correctly take all correlations into account, this combination is performed on the normal equation level. Validating the Swarm/SLR combination against GRACE during the overlapping period January 2015 to June 2016, the best fit is achieved when down-weighting Swarm compared to the weights determined by variance component estimation. While between 2014 and 2017 SLR alone slightly overestimates mass loss in Greenland compared to GRACE, the combined gravity fields match significantly better in the overlapping time period and the RMS of the differences is reduced by almost 100 Gt. After 2017, both SLR and Swarm indicate moderate mass gain in Greenland.

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“…GRACE observations have been extensively used to model hydrological loading in geodetic position time series at global and regional scales. A number of studies have emphasized the significance of GRACE‐derived hydrological loading signals in global GPS networks (Döll et al, ; Horwath et al, ; King et al, ; Kusche & Schrama, ; Rietbroek et al, ; Tesmer et al, ; Tregoning et al, ; Yan et al, ). Consistency between GPS and GRACE‐derived deformation has been observed in large drainage basins where seasonal changes are significant, including Africa (Birhanu & Bendick, ; Nahmani et al, ), the Amazon River Basin, and South America (Davis et al, ; Fu et al, ), Europe (Valty et al, ; van Dam et al, ), the Himalayan region (Chanard et al, ; Fu et al, ; Fu & Freymueller, ; Liu et al, ; Steckler et al, ), Southern Alaska (Fu et al, ), the Western United States (Argus et al, ; Fu et al, ), Australia (Han, ), and near the Greenland Ice Sheet (Liu et al, ).…”

Section: Study Areamentioning

confidence: 99%

A New Hybrid Method for Estimating Hydrologically Induced Vertical Deformation From GRACE and a Hydrological Model: An Example From Central North America

Karegar

Dixon

Kusche

et al. 2018

J Adv Model Earth Syst

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Hydrologically induced deformation of Earth's surface can be measured with high precision geodetic techniques, which in turn can be used to study the underlying hydrologic process. For geodetic study of other Earth processes such as tectonic and volcanic deformation, or coastal subsidence and its relation to relative sea level rise and flood risk, hydrological loading may be a source of systematic error, requiring accurate correction. Accurate estimation of the hydrologic loading deformation may require consideration of local as well as regional loading effects. We present a new hybrid approach to this problem, providing a mathematical basis for combining local (near field) and regional to global (far field) loading data with different accuracies and spatial resolutions. We use a high-resolution hydrological model (WGHM) for the near field and GRACE data for the far field. The near field is defined as a spherical cap and its contribution is calculated using numerical evaluation of Green's functions. The far field covers the entire Earth, excluding only the near-field cap. The far-field contribution is calculated using a modified spherical harmonic approach. We test our method with a large GPS data set from central North America. Our new hybrid approach improves fits to GPS-measured vertical displacements, with 25% and 35% average improvement relative to GRACE-only or WGHM-only spherical harmonic solutions. Our hybrid approach can be applied to a wide variety of environmental surface loading problems.

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Can GPS-Derived Surface Loading Bridge a GRACE Mission Gap?

Cited by 35 publications

References 35 publications

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

SLR, GRACE and Swarm Gravity Field Determination and Combination

A New Hybrid Method for Estimating Hydrologically Induced Vertical Deformation From GRACE and a Hydrological Model: An Example From Central North America

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