Reference : Hydrological effects on gravity at the station of Walferdange, Luxembourg
Dissertations and theses : Doctoral thesis
Physical, chemical, mathematical & earth Sciences : Physics
http://hdl.handle.net/10993/15450
Hydrological effects on gravity at the station of Walferdange, Luxembourg
English
Lampitelli, Carmine [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Physics and Materials Science Research Unit >]
10-Jun-2010
University of Luxembourg, ​Luxembourg, ​​Luxembourg
Docteur en Physique
Francis, Olivier mailto
[en] Hydrology Superconducting Gravimeter ; Gravity Permeability ; Groundwater ; Precipitation
[en] In this study, we look at the physical relationship between water storage variations driven by local precipitation events and local gravity changes at Walferdange, in the Grand Duchy of Luxembourg. A synthesis of the different approaches that can be found in recent literature is proposed. Then, a new and simple scheme is provided to remove the effects of precipitation events in the gravity observations,
For the gravity observations, we use the data collected by the superconducting gravimeter CT040 (SG) located in the Walferdange Underground Laboratory for Geodynamics (WULG), which provides high resolution relative gravity measurements from December 2003.
In addition, a statistical analysis is presented to determine the correlation between the gravity signal variations registered by the SG and the water level of the nearby flowing Alzette River. The idea is that the gravity variation due to the precipitation should appear before the change in river level. Contrarily to the simple precipitation data, the gravity observation should also contain information on the degree of soil saturation, which depends on processes having a seasonal variability, as evapotranspiration, and on hydrogeological parameters like hydraulic conductivity. Understanding the relationship and temporal dependence between the observed precipitation and the gravity changes might improve the capacity to predict extreme events like flooding.
http://hdl.handle.net/10993/15450

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