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J L Davis - One of the best experts on this subject based on the ideXlab platform.
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land water storage within the congo basin inferred from grace satellite Gravity Data
Geophysical Research Letters, 2006Co-Authors: John W Crowley, Jerry X Mitrovica, R C Bailey, Mark E Tamisiea, J L DavisAbstract:[1] GRACE satellite Gravity Data is used to estimate terrestrial (surface plus ground) water storage within the Congo Basin in Africa for the period of April, 2002–May, 2006. These estimates exhibit significant seasonal (30 ± 6 mm of equivalent water thickness) and long-term trends, the latter yielding a total loss of ∼280 km3 of water over the 50-month span of Data. We also combine GRACE and precipitation Data sets (CMAP, TRMM) to explore the relative contributions of the source term to the seasonal hydrological balance within the Congo Basin. We find that the seasonal water storage tends to saturate for anomalies greater than 30–40 mm of equivalent water thickness. Furthermore, precipitation contributed roughly three times the peak water storage after anomalously rainy seasons, in early 2003 and 2005, implying a ∼60–70% loss from runoff and evapotranspiration. Finally, a comparison of residual land water storage (monthly estimates minus best-fitting trends) in the Congo and Amazon Basins shows an anti-correlation, in agreement with the “see-saw” variability inferred by others from runoff Data.
Zhiyong Wang - One of the best experts on this subject based on the ideXlab platform.
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increased water storage in north america and scandinavia from grace Gravity Data
Nature Geoscience, 2013Co-Authors: Hansheng Wang, Lulu Jia, Holger Steffen, Liming Jiang, Houtse Hsu, Longwei Xiang, Zhiyong WangAbstract:Changes in continental water storage have been difficult to constrain from space-borne Gravity Data in regions experiencing both ice melting and glacial isostatic adjustment. Separation of the hydrologic and isostatic signals reveals increases in water storage in both North America and Scandinavia over the past decade.
Peter Vajda - One of the best experts on this subject based on the ideXlab platform.
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Spatial and Spectral Analysis of Refined Gravity Data for Modelling the Crust–Mantle Interface and Mantle-Lithosphere Structure
Surveys in Geophysics, 2012Co-Authors: Robert Tenzer, Pavel Novák, Vladislav Gladkikh, Peter VajdaAbstract:We analyse spatial and spectral characteristics of various refined Gravity Data used for modelling and gravimetric interpretation of the crust–mantle interface and the mantle-lithosphere structure. Depending on the purpose of the study, refined Gravity Data have either a strong or weak correlation with the Moho depths (Moho geometry). The compilation of the refined Gravity Data is purely based on available information on the crustal density structure obtained from seismic surveys without adopting any isostatic hypothesis. We demonstrate that the crust-stripped relative-to-mantle Gravity Data have a weak correlation with the CRUST2.0 Moho depths of about 0.02. Since gravitational signals due to the crustal density structure and the Moho geometry are subtracted from Gravity field, these refined Gravity Data comprise mainly the information on the mantle lithosphere and sub-lithospheric mantle. On the other hand, the consolidated crust-stripped Gravity Data, obtained from the Gravity field after applying the crust density contrast stripping corrections, comprise mainly the gravitational signal of the Moho geometry, although they also contain the gravitational signal due to anomalous mass density structures within the mantle. In the absence of global models of the mantle structure, the best possible option of computing refined Gravity Data, suitable for the recovery/refinement of the Moho interface, is to subtract the complete crust-corrected Gravity Data from the consolidated crust-stripped Gravity Data. These refined Gravity Data, that is, the homogenous crust Gravity Data, have a strong absolute correlation of about 0.99 with the CRUST2.0 Moho depths due to removing a gravitational signal of inhomogeneous density structures within the crust and mantle. Results of the spectral signal decomposition and the subsequent correlation analysis reveal that the correlation of the homogenous crust Gravity Data with the Moho depths is larger than 0.9 over the investigated harmonic spectrum up to harmonic degree 90. The crust-stripped relative-to-mantle Gravity Data correlate substantially with the Moho depths above harmonic degree 50 where the correlation exceeds 0.5.
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spatial and spectral analysis of refined Gravity Data for modelling the crust mantle interface and mantle lithosphere structure
Surveys in Geophysics, 2012Co-Authors: Robert Tenzer, Pavel Novák, Vladislav Gladkikh, Peter VajdaAbstract:We analyse spatial and spectral characteristics of various refined Gravity Data used for modelling and gravimetric interpretation of the crust–mantle interface and the mantle-lithosphere structure. Depending on the purpose of the study, refined Gravity Data have either a strong or weak correlation with the Moho depths (Moho geometry). The compilation of the refined Gravity Data is purely based on available information on the crustal density structure obtained from seismic surveys without adopting any isostatic hypothesis. We demonstrate that the crust-stripped relative-to-mantle Gravity Data have a weak correlation with the CRUST2.0 Moho depths of about 0.02. Since gravitational signals due to the crustal density structure and the Moho geometry are subtracted from Gravity field, these refined Gravity Data comprise mainly the information on the mantle lithosphere and sub-lithospheric mantle. On the other hand, the consolidated crust-stripped Gravity Data, obtained from the Gravity field after applying the crust density contrast stripping corrections, comprise mainly the gravitational signal of the Moho geometry, although they also contain the gravitational signal due to anomalous mass density structures within the mantle. In the absence of global models of the mantle structure, the best possible option of computing refined Gravity Data, suitable for the recovery/refinement of the Moho interface, is to subtract the complete crust-corrected Gravity Data from the consolidated crust-stripped Gravity Data. These refined Gravity Data, that is, the homogenous crust Gravity Data, have a strong absolute correlation of about 0.99 with the CRUST2.0 Moho depths due to removing a gravitational signal of inhomogeneous density structures within the crust and mantle. Results of the spectral signal decomposition and the subsequent correlation analysis reveal that the correlation of the homogenous crust Gravity Data with the Moho depths is larger than 0.9 over the investigated harmonic spectrum up to harmonic degree 90. The crust-stripped relative-to-mantle Gravity Data correlate substantially with the Moho depths above harmonic degree 50 where the correlation exceeds 0.5.
Robert Tenzer - One of the best experts on this subject based on the ideXlab platform.
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how to calculate bouguer Gravity Data in planetary studies
Surveys in Geophysics, 2019Co-Authors: Robert Tenzer, Ismael Foroughi, Christian Hirt, Pavel Novák, Martin PitoňakAbstract:In terrestrial studies, Bouguer Gravity Data is routinely computed by adopting various numerical schemes, starting from the most basic concept of approximating the actual topography by an infinite Bouguer plate, through adding a planar terrain correction to account for a local/regional terrain geometry, to more advanced schemes that involve the computation of the topographic Gravity correction by taking into consideration a gravitational contribution of the whole topography while adopting a spherical (or ellipsoidal) approximation. Moreover, the topographic density information has significantly improved the Gravity forward modeling and interpretations, especially in polar regions (by accounting for a density contrast of polar glaciers) and in regions characterized by a complex geological structure. Whereas in geodetic studies (such as a gravimetric geoid modeling) only the gravitational contribution of topographic masses above the geoid is computed and subsequently removed from observed (free-air) Gravity Data, geophysical studies focusing on interpreting the Earth’s inner structure usually require the application of additional stripping Gravity corrections that account for known anomalous density structures in order to reveal an unknown (and sought) density structure or density interface. In planetary studies, numerical schemes applied to compile Bouguer Gravity maps might differ from terrestrial studies due to two reasons. While in terrestrial studies the topography is defined by physical heights above the geoid surface (i.e., the geoid-referenced topography), in planetary studies the topography is commonly described by geometric heights above the geometric reference surface (i.e., the geometric-referenced topography). Moreover, large parts of a planetary surface have negative heights. This obviously has implications on the computation of the topographic Gravity correction and consequently Bouguer Gravity Data because in this case the application of this correction not only removes the gravitational contribution of a topographic mass surplus, but also compensates for a topographic mass deficit. In this study, we examine numerically possible options of computing the topographic Gravity correction and consequently the Bouguer Gravity Data in planetary applications. In agreement with a theoretical definition of the Bouguer Gravity correction, the Bouguer Gravity maps compiled based on adopting the geoid-referenced topography are the most relevant. In this case, the application of the topographic Gravity correction removes only the gravitational contribution of the topography. Alternative options based on using geometric heights, on the other hand, subtract an additional gravitational signal, spatially closely correlated with the geoidal undulations, that is often attributed to deep mantle density heterogeneities, mantle plumes or other phenomena that are not directly related to a topographic density distribution.
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Spatial and Spectral Analysis of Refined Gravity Data for Modelling the Crust–Mantle Interface and Mantle-Lithosphere Structure
Surveys in Geophysics, 2012Co-Authors: Robert Tenzer, Pavel Novák, Vladislav Gladkikh, Peter VajdaAbstract:We analyse spatial and spectral characteristics of various refined Gravity Data used for modelling and gravimetric interpretation of the crust–mantle interface and the mantle-lithosphere structure. Depending on the purpose of the study, refined Gravity Data have either a strong or weak correlation with the Moho depths (Moho geometry). The compilation of the refined Gravity Data is purely based on available information on the crustal density structure obtained from seismic surveys without adopting any isostatic hypothesis. We demonstrate that the crust-stripped relative-to-mantle Gravity Data have a weak correlation with the CRUST2.0 Moho depths of about 0.02. Since gravitational signals due to the crustal density structure and the Moho geometry are subtracted from Gravity field, these refined Gravity Data comprise mainly the information on the mantle lithosphere and sub-lithospheric mantle. On the other hand, the consolidated crust-stripped Gravity Data, obtained from the Gravity field after applying the crust density contrast stripping corrections, comprise mainly the gravitational signal of the Moho geometry, although they also contain the gravitational signal due to anomalous mass density structures within the mantle. In the absence of global models of the mantle structure, the best possible option of computing refined Gravity Data, suitable for the recovery/refinement of the Moho interface, is to subtract the complete crust-corrected Gravity Data from the consolidated crust-stripped Gravity Data. These refined Gravity Data, that is, the homogenous crust Gravity Data, have a strong absolute correlation of about 0.99 with the CRUST2.0 Moho depths due to removing a gravitational signal of inhomogeneous density structures within the crust and mantle. Results of the spectral signal decomposition and the subsequent correlation analysis reveal that the correlation of the homogenous crust Gravity Data with the Moho depths is larger than 0.9 over the investigated harmonic spectrum up to harmonic degree 90. The crust-stripped relative-to-mantle Gravity Data correlate substantially with the Moho depths above harmonic degree 50 where the correlation exceeds 0.5.
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spatial and spectral analysis of refined Gravity Data for modelling the crust mantle interface and mantle lithosphere structure
Surveys in Geophysics, 2012Co-Authors: Robert Tenzer, Pavel Novák, Vladislav Gladkikh, Peter VajdaAbstract:We analyse spatial and spectral characteristics of various refined Gravity Data used for modelling and gravimetric interpretation of the crust–mantle interface and the mantle-lithosphere structure. Depending on the purpose of the study, refined Gravity Data have either a strong or weak correlation with the Moho depths (Moho geometry). The compilation of the refined Gravity Data is purely based on available information on the crustal density structure obtained from seismic surveys without adopting any isostatic hypothesis. We demonstrate that the crust-stripped relative-to-mantle Gravity Data have a weak correlation with the CRUST2.0 Moho depths of about 0.02. Since gravitational signals due to the crustal density structure and the Moho geometry are subtracted from Gravity field, these refined Gravity Data comprise mainly the information on the mantle lithosphere and sub-lithospheric mantle. On the other hand, the consolidated crust-stripped Gravity Data, obtained from the Gravity field after applying the crust density contrast stripping corrections, comprise mainly the gravitational signal of the Moho geometry, although they also contain the gravitational signal due to anomalous mass density structures within the mantle. In the absence of global models of the mantle structure, the best possible option of computing refined Gravity Data, suitable for the recovery/refinement of the Moho interface, is to subtract the complete crust-corrected Gravity Data from the consolidated crust-stripped Gravity Data. These refined Gravity Data, that is, the homogenous crust Gravity Data, have a strong absolute correlation of about 0.99 with the CRUST2.0 Moho depths due to removing a gravitational signal of inhomogeneous density structures within the crust and mantle. Results of the spectral signal decomposition and the subsequent correlation analysis reveal that the correlation of the homogenous crust Gravity Data with the Moho depths is larger than 0.9 over the investigated harmonic spectrum up to harmonic degree 90. The crust-stripped relative-to-mantle Gravity Data correlate substantially with the Moho depths above harmonic degree 50 where the correlation exceeds 0.5.
John W Crowley - One of the best experts on this subject based on the ideXlab platform.
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land water storage within the congo basin inferred from grace satellite Gravity Data
Geophysical Research Letters, 2006Co-Authors: John W Crowley, Jerry X Mitrovica, R C Bailey, Mark E Tamisiea, J L DavisAbstract:[1] GRACE satellite Gravity Data is used to estimate terrestrial (surface plus ground) water storage within the Congo Basin in Africa for the period of April, 2002–May, 2006. These estimates exhibit significant seasonal (30 ± 6 mm of equivalent water thickness) and long-term trends, the latter yielding a total loss of ∼280 km3 of water over the 50-month span of Data. We also combine GRACE and precipitation Data sets (CMAP, TRMM) to explore the relative contributions of the source term to the seasonal hydrological balance within the Congo Basin. We find that the seasonal water storage tends to saturate for anomalies greater than 30–40 mm of equivalent water thickness. Furthermore, precipitation contributed roughly three times the peak water storage after anomalously rainy seasons, in early 2003 and 2005, implying a ∼60–70% loss from runoff and evapotranspiration. Finally, a comparison of residual land water storage (monthly estimates minus best-fitting trends) in the Congo and Amazon Basins shows an anti-correlation, in agreement with the “see-saw” variability inferred by others from runoff Data.
