Quantitative evidence that compaction of Holocene sediments drives the present land subsidence of the Po Delta, Italy

Deltas are highly dynamic coastal systems that over the last few decades have generally experienced a substantial area loss caused by trapping of river sediments in upland drainage basins as well as land subsidence due to natural and anthropogenic causes. A major example is the Po Delta in the Mediterranean in northeastern Italy. This area has experienced as much as 3 m of land subsidence from the 1930s to the 1970s primarily because of the extraction of gas‐bearing waters. However, present subsidence rates are largely unknown and the ground settlement is supposedly controlled by natural long‐term deep processes. We have combined radar Interferometric Point Target Analysis (IPTA) with previous geomorphological investigations on aerial/satellite images and seismic surveys, and geochronological data from core samples and geomechanical in situ tests, to assess the current sinking of the delta and to understand the processes controlling the vertical movement. The high density of the measurable point targets (more than 15,000) allows characterization of the spatial variation in the vertical land motions (VLM), ranging from −1 to −15 mm/yr. We find that subsidence rates are significantly correlated with the age of highly compressible Holocene deposits that compose the shallowest 30–40 m of the sedimentary sequence. A typical log‐type consolidation equation applicable at the scale of the entire delta has been obtained. We conclude that the consolidation of late Holocene sediments is the major cause of the present land subsidence in the Po River delta. This finding has significant impact on the understanding of many other modern deltas that were formed in the lower Holocene epoch. Key Points Compaction of Holocene strata drives the present land subsidence of deltas The Po Delta is presently characterized by a natural subsidence up to 15 mm/yr A delta‐scale consolidation law is inferred by SAR and geochronological data