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The coupled effects of climate change and land sinking make deltas and coastal areas prone to inundation and flooding, meaning that reliable estimation of land subsidence is becoming crucial. Commonly, land subsidence is monitored by accurate continuous and discrete measurements collected by terrestrial and space geodetic techniques, such as Global Navigation Satellite System (GNSS), Interferometry Synthetic Aperture Radar (InSAR), and high precision leveling. In particular, GNSS, which includes the Global Positioning System (GPS), provides geospatial positioning with global coverage, then used for deriving local displacements through time. These site-positioning time series usually exhibit a linear trend plus seasonal oscillations of annual and semi-annual periods. Although the periodic components observed in the geodetic signal affect the velocity estimate, studies dealing with the prediction and prevention of risks associated with subsidence focus mainly on the permanent component. Periodic components are simply removed from the original dataset by statistical analyses not based on the underlying physical mechanisms. Here, we propose a systematic approach for detecting the physical mechanisms that better explain the permanent and periodic components of subsidence observed in the geodetic time series. It consists of three steps involving a component recognition phase, based on statistical and spectral analyses of geodetic time series, a source selection phase, based on their comparison with data of different nature (e.g., geological, hydro-meteorological, hydrogeological records), and a source validation step, where the selected sources are validated through physically-based models. The application of the proposed procedure to the Codigoro area (Po River Delta, Northern Italy), historically affected by land subsidence, allowed for an accurate estimation of the subsidence rate over the period 2009–2017. Significant differences turn out in the retrieved subsidence velocities by using or not periodic trends obtained by physically based models.

Multi-Component and Multi-Source Approach for Studying Land Subsidence in Deltas