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

Abstract

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.

Monitoring of Land Subsidence in the Po River Delta (Northern Italy) Using Geodetic Networks

“…Several authors (e.g., [3,[28][29][30]) investigated rates and extension of land subsidence in both the Po plain and PRD area using InSAR data. Other authors (e.g., [31][32][33][34][35][36][37][38]) used GNSS observations acquired using Non-Permanent Sites (NPS) and continuous stations to estimate the land subsidence rate in river deltas, showing that despite its benefit, these methods are strongly limited to the number of points that it is possible to measure.…”

Analysis of the Periodic Component of Vertical Land Motion in the Po Delta (Northern Italy) by GNSS and Hydrological Data

“…The periodic component analysis of the VLM in the Po Delta is achieved by using the available geodetic, hydro-meteorological, hydrogeological and climate datasets (Figure 1b), and by applying a methodological approach recently proposed for evaluating subsidence in delta and coastal areas [24].…”

“…The current work aims to properly model ground level oscillations in the central part of the Po Delta (northern Italy) through a multi-step procedure [24], which is able to identify the sources responsible for the observed land deformations starting from the analysis of satellite data and comparing such information with other available datasets (e.g., hydro-meteorological, hydrogeological and climate time series). The study area was historically affected by significant land subsidence (e.g., [25]) and, in recent decades, has been covered by accurate GNSS monitoring data and InSAR data (e.g., [26][27][28]).…”

Studying a Subsiding Urbanized Area from a Multidisciplinary Perspective: The Inner Sector of the Sarno Plain (Southern Apennines, Italy)

“…GNSS and DInSAR are two techniques of satellite geodesy, which have been widely used in recent years to measure crustal deformation associated with a number of natural phenomena, such as earthquakes, volcanism, landslides, and subsidence (e.g., [51][52][53][54][55][56][57][58]). GNSS can provide millimetre-level accuracy in horizontal directions and centimetre-level accuracy in the vertical direction [59] with unprecedented time resolution, but only at selected points, even if in dense networks.…”