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Regression Analysis of Subsidence in the Como Basin (Northern Italy): New Insights on Natural and Anthropic Drivers from InSAR Data.

Authors :
Nappo, Nicoletta
Ferrario, Maria Francesca
Livio, Franz
Michetti, Alessandro Maria
Source :
Remote Sensing. Sep2020, Vol. 12 Issue 18, p2931. 1p.
Publication Year :
2020

Abstract

Natural and anthropogenic subsidence such as that in the Como urban area (northern Italy) can cause significant damage to structures and infrastructure, and expose the city's lakefront to an increasing risk of inundation from Lake Como. This phenomenon affecting the Como basin has been studied by several researchers, and the major drivers of subsidence are known. However, the availability of historical InSAR data allowed us to reconsider the relationship between subsidence predisposing factors (i.e., the thicknesses of reworked and compressible layers, overburden stress, and the piezometric level) and ground surface displacements with higher precision over the entire basin. Benefiting from the deep knowledge of the hydromechanical setting of the Como basin and the availability of InSAR measurements from 1992 to 2010, in this paper we model subsidence-related movements using linear and nonlinear regression methods in order to determine the combination of natural and anthropic factors that have caused subsidence in the Como basin over the past decades. The results of this study highlight peculiar patterns of subsidence that suggest the influence of two further causes, namely tectonic control of the sedimentary architecture and diversion of local streams, which have never been considered before. This analysis aims to assess the spatial distribution of subsidence through InSAR analysis in order to enhance the knowledge and understanding of the phenomenon in the Como urban area. The interferometric data could be used to better plan urban risk management strategies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
12
Issue :
18
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
146537675
Full Text :
https://doi.org/10.3390/rs12182931