An integral approach using InSAR and data assimilation to disentangle and quantify multi-depth driven subsidence causes in the Ravenna coastland, Northern Italy

Land subsidence in the Ravenna area (Italy) was a hydrogeological hazard until the end of last century. Although subsidence reduced during the last decades, the area is still experiencing vertical displacements. Understanding their drivers is challenging. Land subsidence magnitude and distribution must be interpreted with a combination of geological factors and human activities. This study integrates various datasets, subsidence observations, and subsidence models to evaluate the contributions of three main causes: building related, shallow subsurface processes and deep subsurface processes. The model result was optimized using Interferometric Synthetic Aperture Radar. The highest subsidence rates, of over 10 mm/year, were found at locations where multiple causes have an effect. The results of building-related subsidence indicate that subsidence rates associated with industrial buildings are twice as high as for residential buildings. This difference is even more pronounced in lagoonal and reclaimed areas. Shallow causes, associated with overburden weight on tidal deposits and drainage of reclaimed land, cause significant subsidence along the coast. Deep causes, by offshore gas extraction, contribute to subsidence along parts of the coast, with a decreasing trend over time. Other factors, such as low-lying farmland drainage, (historical) groundwater extraction and compaction of Quaternary deposits are not specifically addressed because of their small contribution to the total subsidence during the time period considered. This study underscores the importance of a comprehensive approach that considers the interplay between geomorphology and geology, industrialization, urbanization, and fluid extraction. Geotechnical assessments and improved subsidence models, incorporating localized data on buildings and subsurface fluid withdrawals, are crucial for developing effective mitigation strategies. © 2025 The Authors