Search

The study of flooding events resulting from bank over-flooding and levee breaching is of large interest for both society and environment, because flood waves, resulting from levee failure, might cause loss of lives and destruction of properties and ecosystems. Understanding the subsoil mechanics and the fluid-solid interplay allows the stability condition estimate of dams, embankments and slopes and the development of early warning alarm systems. Changes in soil and hydraulic parameters are usually monitored by geotechnical and geophysical investigations that also provide the basic assumptions for developing hydraulic models. Nowadays, remote sensing approaches, including satellite techniques, are mainly used for flooding simulation studies. Indeed, remote sensing observations, such as discharge, flood area extent and water stage, have been used for retrieving flood hydrology information and modeling, calibrating and validating hydrodynamic models, improving model structures and developing data assimilation models. Although all these studies have contributed significantly to the recent advances, uncertainty in observations, as well as in model parameters and prediction, represents a critical aspect for using remote sensing data in the flooding defence. Compared to past and current methods for monitoring the fluvial levee failure, we propose a new procedure that provides a wide and fast alert system. The proposed methodological path is based on presumed relationships between ground level deformation and hydrological and surface soil properties, due to physical mechanisms and exhibited by geodetic and hydrological time series. The procedure is accomplished first through multi-methodological comparative analyses applied to geodetic, hydrological and soil-properties patterns, then through the mapping of the river zones prone to failure. Since the input consists of time series satellite-derived data, the geospatial Artificial Intelligence is applied for extracting knowledge from spatial big data and for increasing the performance of data computing. In particular, machine learning is initially developed for selecting the relevant geographical areas (i.e. rivers, levees and riverbanks) from large geo-referential datasets. Then, since the spatial-distributed points are also time-dependent, the trends of different datasets are compared point by point by selected analytical techniques. Finally, in accordance with the acquired knowledge from previous steps, the system extracts information on the correlation indexes in order to make sense of patterns in space and time and to identify hierarchic orders for the realization of hazard maps. The proposed method is “wide” because, unlike other direct surveys, it is able to monitor large spatial areas since it is based on satellite-derived data. It is also “fast” because it is based on the Earth’s surface observation and is not connected with Earth’s inland investigations (such as the geotechnical and geophysical ones) or with forecasting models (e.g. hydraulic and flooding simulations). Due to these peculiarities, the method can support flood protection studies and can be used for driving the localization of river portions prone to failure, where focusing detailed geotechnical and geophysical surveys.

Defining land subsidence causes is not an easy task, because ground lowering is a complex phenomenon due to the contribution of different physical processes related to natural contest and to anthropic actions. Indeed, such processes, which are characterized by a specific origin and may act in different spatial and temporal intervals, can overlap giving rise to a single surface land deformation, observable through conventional and innovative monitoring techniques (i.e. high-precision levelling, InSAR and GNSS). Of course, discriminating the individual causes is fundamental for reducing environmental and social harms, especially in deltas and coastal areas, where land sinking, coupled with climatic effects, can induce massive flooding. The present work concerns an application of a multi-component and multi-source approach, recently proposed by some of the authors for studying land subsidence in deltas. Such a methodology is aimed at understanding the processes causing both periodic and permanent components of the vertical land movement and at retrieving more accurate subsidence rates. It consists of three steps, respectively involving: a component recognition phase, based on statistical and spectral analyses of geodetic time series; a source (or physical process) selection phase, based on the comparison with data of different nature; a source validation step, where the selected sources are validated through physically-based models. The proposed procedure has been applied to the permanent component of subsidence in the Po Delta (northern Italy), an area historically affected by land subsidence and influenced by climatic changes, where continuous GNSS data and differential InSAR-derived time series were simultaneously acquired from 2012 to 2017. In particular, the exponential relation found between the mean SAR-derived LOS velocity and the thickness of the Late Holocene prograding deposits, pointed out the key role of the sedimentary compaction process with respect to the spatial distribution of the subsidence rates and confirmed the importance, already highlighted by other authors, of the consolidation of the shallower strata. In order to validate the consolidation process and to quantify also the deeper contributions of tectonics- and isostasy-depending mechanisms, 2D geological models have been constructed along two west-east sections across the central part of the Delta. Finally, the computed subsidence rates have been compared with the geodetic velocities estimated in Taglio di Po and Porto Tolle villages (Rovigo, northern Italy), clarifying the contribution of each geological mechanism to the observed delta subsidence.

In the last twenty years, a growing interest is noticed in quantifying non-volcanic degassing, which could represent a significant input of CO2 into the atmosphere. Large emissions of non-volcanic carbon dioxide usually take place in seismically active zones, where the existence of a positive spatial correlation between gas discharges and extensional tectonic regimes has been confirmed by seismic data. Extensional stress plays a key role in creating pathways for the rising of gases at micro- and macro-scales, increasing the rock permeability and connecting the deep crust to the earth surface. Geoelectrical investigations, which are very sensitive to permeability changes, provide accurate volumetric reconstructions of the physical properties of the rocks and, therefore, are fundamental not only for the definition of the seismic-active zone geometry, but also for understanding the processes that govern the flow of fluids along the damage zone. In this framework, we present the results of an integrated approach where geoelectrical and passive seismic data are used to construct a 3D geological model, whose simulated temporal evolution allowed the estimation of CO2 flux along an active fault in the area of Matese Ridge (Southern Apennines, Italy). By varying the geometry of the source system and the permeability values of the damage zone, characteristic times for the upward migration of CO2 through a thick layer of silts and clays have been estimated and CO2 fluxes comparable with the observed values in the investigated area have been predicted. These findings are promising for gas hazard, as they suggest that numerical simulations of different CO2 degassing scenarios could forecast possible critical variations in the amount of CO2 emitted near the fault.