Anthropogenic and climatic controls on surface water across the contiguous United States

Anthropogenic activities and climatic processes heavily influence surface water resources by causing their progressive depletion, which in turn affects both societies and the environment. Therefore, there is an urgent need to understand the contribution of human and climatic dynamics on the variation of surface water availability. Here, this investigation is performed on the contiguous United States (CONUS) using remotely-sensed data. Three anthropogenic (i.e., urban area, population, and irrigation) and two climatic factors (i.e., precipitation and temperature) were selected as potential drivers of changes in surface water extent and the overlap between the increase or decrease in these drivers and the variation of surface water was examined. Most of the river basins experienced a surface water gain due to precipitation increase (eastern CONUS), and a reduction of irrigated land (western CONUS). River basins of the arid southwestern region and some river basins of the northeastern area encountered a surface water loss, essentially induced by population growth, along with a precipitation deficit and a general expansion of irrigated land. To further inspect the role of population growth and urbanization on surface water loss, the spatial interaction between human settlements and surface water depletion was examined by evaluating the frequency of surface water loss as a function of distance from urban areas. The decline of the observed frequency was successfully reproduced with an exponential distance-decay model, proving that surface water losses are more concentrated in the proximity of cities. Climatic conditions influenced this pattern, with more widely distributed losses in arid regions compared to temperate and continental areas. The results presented in this Thesis provide an improved understanding of the effects of anthropogenic and climatic dynamics on surface water availability, which could be integrated in the definition of sustainable strategies for urbanization, water management, and surface water restoration.

Quality assessment of a landslide inventory map and its application to land‐use planning. A case study in the Northern Apennines (Emilia‐Romagna region, Italy)

Landslide hazard and risk are growing as a consequence of climate change and demographic pressure. Land‐use planning represents a powerful tool to manage this socio‐economic problem and build sustainable and landslide resilient communities. Landslide inventory maps are a cornerstone of land‐use planning and, consequently, their quality assessment represents a burning issue. This work aimed to define the quality parameters of a landslide inventory and assess its spatial and temporal accuracy with regard to its possible applications to land‐use planning. In this sense, I proceeded according to a two‐steps approach. An overall assessment of the accuracy of data geographic positioning was performed on four case study sites located in the Italian Northern Apennines. The quantification of the overall spatial and temporal accuracy, instead, focused on the Dorgola Valley (Province of Reggio Emilia). The assessment of spatial accuracy involved a comparison between remotely sensed and field survey data, as well as an innovative fuzzylike analysis of a multi‐temporal landslide inventory map. Conversely, long‐ and short‐term landslide temporal persistence was appraised over a period of 60 years with the aid of 18 remotely sensed image sets. These results were eventually compared with the current Territorial Plan for Provincial Coordination (PTCP) of the Province of Reggio Emilia. The outcome of this work suggested that geomorphologically detected and mapped landslides are a significant approximation of a more complex reality. In order to convey to the end‐users this intrinsic uncertainty, a new form of cartographic representation is needed. In this sense, a fuzzy raster landslide map may be an option. With regard to land‐use planning, landslide inventory maps, if appropriately updated, confirmed to be essential decision‐support tools. This research, however, proved that their spatial and temporal uncertainty discourages any direct use as zoning maps, especially when zoning itself is associated to statutory or advisory regulations.

Analysis of the long-term subsidence in the urban area of Bologna

Land subsidence in urban areas represents a widespread geological hazard and a pressing challenge for modern society. This research focuses on the subsidence process affecting the city of Bologna (Italy). Since the 1960s, Bologna has experienced ground deformation due to aquifers overexploitation that peaked during the 1970s with rates of 10 cm/year. Despite a general reduction in these rates over the subsequent decades, thanks to groundwater regulations policies, recent data underscore a substantial subsidence resurgence. To reconstruct the subsurface stratigraphic architecture of Bologna’s urban area and generate a 3D geological model, a multidisciplinary approach centred on a stratigraphic analysis relying on the lithofacies criterion was adopted. The convergence of the analyses within this framework resulted in partitioning the study area into three geological domains exhibiting unique morphological features and depositional stacking patterns. Subsequently, since long-term data are crucial for a comprehensive understanding of ongoing subsidence, a methodology was developed to generate cumulative ground displacement time series and maps by integrating ground-based and remotely-sensed measurements. While the reconstructed long-term subsidence field consistently aligns with the primary geological variations summarised in the 3D model, the generated cumulative displacement curves systematically match pluriannual trends observed in groundwater level and pumping monitoring data. Lastly, to evaluate the expression of the observed relationships from a geotechnical perspective, a series of one-dimensional subsidence calculations were conducted considering the mechanical properties of the investigated deposits and piezometric data. These analyses provided valuable insight into the overall mechanical behaviour of the existing soils, as well as the post-pumping groundwater level and pore pressure distributions, consistent with field data. The methodological approach employed enables a comprehensive analysis of land subsidence in urban areas, allowing the exploration of individual factors governing the deformation process and their interactions, even within complex stratigraphic and hydrogeological environments such as Bologna’s urban area.

Dynamic services in mobile ad hoc networks

The increasing diffusion of wireless-enabled portable devices is pushing toward the design of novel service scenarios, promoting temporary and opportunistic interactions in infrastructure-less environments. Mobile Ad Hoc Networks (MANET) are the general model of these higly dynamic networks that can be specialized, depending on application cases, in more specific and refined models such as Vehicular Ad Hoc Networks and Wireless Sensor Networks. Two interesting deployment cases are of increasing relevance: resource diffusion among users equipped with portable devices, such as laptops, smart phones or PDAs in crowded areas (termed dense MANET) and dissemination/indexing of monitoring information collected in Vehicular Sensor Networks. The extreme dynamicity of these scenarios calls for novel distributed protocols and services facilitating application development. To this aim we have designed middleware solutions supporting these challenging tasks. REDMAN manages, retrieves, and disseminates replicas of software resources in dense MANET; it implements novel lightweight protocols to maintain a desired replication degree despite participants mobility, and efficiently perform resource retrieval. REDMAN exploits the high-density assumption to achieve scalability and limited network overhead. Sensed data gathering and distributed indexing in Vehicular Networks raise similar issues: we propose a specific middleware support, called MobEyes, exploiting node mobility to opportunistically diffuse data summaries among neighbor vehicles. MobEyes creates a low-cost opportunistic distributed index to query the distributed storage and to determine the location of needed information. Extensive validation and testing of REDMAN and MobEyes prove the effectiveness of our original solutions in limiting communication overhead while maintaining the required accuracy of replication degree and indexing completeness, and demonstrates the feasibility of the middleware approach.

Pervasive Sensing in Future Networks

Pervasive Sensing is a recent research trend that aims at providing widespread computing and sensing capabilities to enable the creation of smart environments that can sense, process, and act by considering input coming from both people and devices. The capabilities necessary for Pervasive Sensing are nowadays available on a plethora of devices, from embedded devices to PCs and smartphones. The wide availability of new devices and the large amount of data they can access enable a wide range of novel services in different areas, spanning from simple data collection systems to socially-aware collaborative filtering. However, the strong heterogeneity and unreliability of devices and sensors poses significant challenges. So far, existing works on Pervasive Sensing have focused only on limited portions of the whole stack of available devices and data that they can use, to propose and develop mainly vertical solutions. The push from academia and industry for this kind of services shows that time is mature for a more general support framework for Pervasive Sensing solutions able to enhance frail architectures, promote a well balanced usage of resources on different devices, and enable the widest possible access to sensed data, while ensuring a minimal energy consumption on battery-operated devices. This thesis focuses on pervasive sensing systems to extract design guidelines as foundation of a comprehensive reference model for multi-tier Pervasive Sensing applications. The validity of the proposed model is tested in five different scenarios that present peculiar and different requirements, and different hardware and sensors. The ease of mapping from the proposed logical model to the real implementations and the positive performance result campaigns prove the quality of the proposed approach and offer a reliable reference model, together with a direction for the design and deployment of future Pervasive Sensing applications.