Abstract
A superconducting gravimeter has observed with high accuracy (to within a few nm s−2) and high frequency (1 Hz) the temporal variations in the earth’s gravity field near Moxa, Germany, since 1999. Hourly gravity residuals are obtained by time averaging and correcting for earth tides, polar motion, barometric pressure variations, and instrumental drift. These gravity residuals are significantly affected by hydrological processes (interception, infiltration, surface runoff, and subsurface redistribution) in the vicinity of the observatory. In this study time series analysis and distributed hydrological modeling techniques are applied to understand the effect of these hydrological processes on observed gravity residuals. It is shown that the short-term response of gravity residuals to medium- to high-rainfall events can be efficiently modeled by means of a linear transfer function. This transfer function exhibits an oscillatory behavior that indicates fast redistribution of stored water in the upper layers (interception store, root zone) of the catchment surrounding the instrument. The relation between groundwater storage and gravity residuals is less clear and varies according to the season. High positive correlation between groundwater and gravity exists during winter months when the freezing of the upper soil layers immobilizes water stored in the unsaturated zone of the catchment. To further explore the spatiotemporal dynamics of the relevant hydrological processes and their relation to observed gravity residuals, a GIS-based distributed hydrological model is applied for the Silberleite catchment. Driven by observed atmospheric forcings (precipitation and potential evapotranspiration), the model allows the authors to compute the variation of water storage in three different layers: the interception store, the snow cover store, and the soil moisture store. These water storage dynamics are then converted to predicted gravity variation at the location of the superconducting gravimeter and compared to observed gravity residuals. During most of the investigated period (January 2000 to January 2004) predictions are in good agreement with the observed patterns of gravity dynamics. However, during some winter months the distributed hydrological model fails to explain the observations, which supports the authors’ conclusion that groundwater variability dominates the hydrological gravity signal in the winter. More hydrogeological research is needed to include groundwater dynamics in the hydrological model.
Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation