In hydrological studies, it is very important to properly analyze the relationship among the different components of the water cycle, due to the complex feedback mechanisms typical of this system. The analysis of available time series is hence a fundamental step, which has to be performed before any modeling activity. Moreover, time series analysis can shed light over the spatial and temporal dynamics of correlated hydrological and climatological processes. In this work, we focus on three tools applied for time series analysis: homogeneity tests, wavelet analysis and copula analysis. Homogeneity tests allow to identify a first important kind of variability in the time series, which is not due to climate nor seasonal variability. Testing for inhomogeneities is therefore an important step that should be always performed on a time series before using it for any application. The homogenization of snow depth data, in particular, is a challenging task. Up to now, it has been performed analyzing available metadata, which often present contradictions and are rarely complete. In this work, we present a procedure to test the homogeneity of snow depth time series based on the Standard Normal Homogeneity Test (SNHT). The performance of the SNHT for the detection of inhomogeneities in snow depth data is further investigated with a comparison experiment, in which a dataset of snow depth time series relative to Austrian stations has been analyzed with both the SNHT and the HOMOP algorithm. The intercomparison study indicates that the two algorithms show comparable performance. The wavelet transform analysis allows to obtain a different kind of information about the variability of a time series. In fact, it determines the different frequency content of a signal in different time intervals. Moreover, the wavelet coherence analysis allows to identify periods where two time series are correlated and their phase shift. We apply the wavelet transform to a dataset of snow depth time series of stations distributed in the Adige catchment and on a dataset of 16 discharge time series located in the Adige and in the Inn catchments. The same datasets are used to perform a wavelet coherence analysis considering the Mediterranean Oscillation Index (MOI) and the North Atlantic Oscillation Index (NAOI). This analysis highlights a difference in the behavior of the snow time series collected below and above 1650 m a.s.l.. We also observe a difference between low and high elevation sites in the amount of mean seasonal snow depth and snow cover duration. More interestingly, snow time series collected at different elevations respond differently to temperature and more in general to climate changes. The wavelet analysis allows us also to distinguish between gauging stations belonging to different catchments, while the wavelet coherence analysis revealed non-stationary correlations with the MOI and NAOI, indicating a very complex relation between the measured quantities and climatic indexes. Finally the application of copulas allows modeling the marginal of each variable and their dependence structure independently. We apply this technique to two relevant cases. First we study snow related variables in relation with temperature, the NAOI and the MOI, which we already investigated with the wavelet coherence analysis. Then we model flood events registered at two stations of the Inn river: Wasserburg and Passau. This last analysis is performed with the goal of predicting future flood events and derive construction parameters for retention basins. We test three different combinations of variables (direct peak discharge-direct volume, direct peak discharge-direct volume-rising time-base flow, direct peak discharge-direct volume-rising time-moving threshold) describing the flood events and compare the results. The consistency in the results indicates that the proposed methodology is robust and reliable. This study shows the importance of approaching the analysis to hydrological time series from several points of view: quality of the data, variability of the time series and relation between different variables. Moreover, it shows that integrating the use of various time series analysis methods can greatly improve our understanding of the system behavior.

The fate and transport of solutes introduced into a watershed and sampled at the catchment outlet depends on many environmental, chemical and hydro-climatological forces.Moreover, if the solutes are micro and emerging pollutants (i.e. pharmaceuticals), which are non-regulated contaminants not routinely monitored but often-detected in fresh waters, the description of the transport sources and routes becomes an interesting and challenging topic to investigate and describe, especially in conjunction with the well-known travel time transport approach at the catchment-scale. In fact, with the travel time approach to pharmaceuticals represents a framework that allows dealing in a unitary and simple way the main two mitigation mechanisms controlling them, which are dilution and biological decay. Moreover, possible consequences on the health of humans and of aquatic organisms have become issue of increasing concern by the scientific community worldwide. The topics have been extensively studied in the last decades, with some recent benchmark contributions. Nevertheless, there is still room for further development for emerging contaminant models and there is still the necessity of complementing the applications with measured data. This doctoral thesis aimed at contributing with new insights into the multi-faceted aspects of solute transport at catchment-scale, proposing novel solutions, with applications to real-world case studies and including a detailed description of the major aspects that influence the water quality dynamics in rivers. The thesis is divided into three interconnected and chronological subsequent parts. In the first part, a detailed description of three large European river basins are presented (i.e. Adige, Ebro and Sava), believing that an accurate analysis of existing information is therefore useful and necessary to identify stressors that may act in synergy and to design new field campaigns. In addition, a detailed data analysis of the main water quality variables is presented: advanced statistical analyses (i.e. Spearman rank correlation, Principal Component Analysis, andMann-Kendall trend tests) were applied to long-term time series of water quality data both in the Adige River Basin and in the Ebro and Sava catchments, aiming at providing an integrated and comparative analysis of recent trends, in order to investigate the relationships between water quality parameters and the main factors controlling them (i.e. climate change, streamflow, land use, population) in the Mediterranean region. These catchments are included into the EU project “Globaqua ”, dealing with the analysis of the combined effect of several stressors on the freshwater ecosystems inMediterranean rivers. In fact, little attention has been paid to linkages between long-term trends in climate, streamflow and water quality in European basins; nevertheless, such analysis can represent, complementary to a deep knowledge of the investigated systems, a reliable tool for decision makers in river basin planning by providing a reliable estimate of the impacts on the aquatic ecosystem of the studied basins. In the second part, sampling campaigns performed in our study basin, the Adige catchment, are presented in detail. Special attention is also given to emerging pollutants, whose study on the occurrence patterns and spatiotemporal variability in the Adige River Basin has been conducted in conjunction with population patterns and touristic fluxes. In the third and last part, novel theoretical solutions of the well-known advection-dispersion-reaction (ADR) equation are presented. The theory was developed for both general water quality variables and pharmaceuticals, evidencing differences and analysing the main factors that influence water quality dynamics. An application is also proposed to the Adige catchment.

The impact of changing climate on the hydrological cycle in Alpine regions has attracted in the last decades a wealth of attention by the scientific community and decision makers. Indeed, the implications of changes in the intensity and in the temporal and spatial patterns of precipitation, temperature and other climatic forcing have been widely observed accompanied with an increased frequency of drought and flood events, and a general degradation of water quality and health of aquatic ecosystems. Accordingly, in the present thesis, the effect of changes in hydro-climatic variables on the hydrological cycle is investigated over a range of temporal and spatial scales. In particular, the research moves along two main directions: 1) changes in historical time series of streamflow, precipitation and temperature, recorded in the Adige River Basin (i.e., Northeastern Italy), are analyzed with a water balance approach and compared to those of other large European river basins (i.e., Ebro and Sava) in order to quantify alterations of the main hydrological fluxes due to climate change and water uses and to disentangle their reciprocal effects; 2) a framework for evaluating the hydrological coherence of available gridded meteorological datasets, including one developed in the first part of the thesis, is introduced and tested. Regarding the first line of research, hydro-climatic and water quality variables of some important European river basins have been analyzed in order to quantify the main alterations of streamflow and to understand the most important factors controlling them. Particular attention is drawn to the Adige River Basin (an Alpine catchment located in the North-East of Italy), for which in depth studies, data measures and analyses have been performed. At this purpose, advanced techniques, besides novel approaches, have been applied. In particular, statistical methods (i.e., Mann-Kendall trend tests, Senâ€TMs slope estimates, multivariate data analyses and Kriging algorithms) have been used to assess the water budgets and the variations in time and space of the aforementioned variables. Disentangling climatic and human impacts on the hydrological fluxes is a difficult task and it has not been fully explored yet, since concurring drivers of hydrological alterations (e.g., climate and land use changes, hydropower and agricultural developments and increasing population) are intimately intertwined one to each other and combined in a complex nonlinear manner. At this purpose, spatial and temporal patterns of change in the hydrological cycle of the Adige River Basin have been identified by comparing annual and seasonal water budgets performed in four representative sub-basins (sized from 207 to 9,852 km2) characterized by different climatic and water uses conditions. A significant downward trend of streamflow is found in the lower part of the Adige since the â€TM70s , which can be attributed to the intense development of irrigated agriculture in the drainage area of the Noce River (one of the main tributaries of the Adige River). Conversely, headwater catchments showed a significant positive trend in streamflow due to a shift in the seasonal distribution of precipitation. These results suggest that climate change is the main driver only in headwater basins, while water uses overcome its effect along the main stream and the lower end of the tributaries. Therefore, a comparative analysis of recent trends in hydro-climatic parameters in three climatologically different European watersheds (i.e., the Adige, Ebro and Sava River Basins) has been performed. The main results suggest that the highest risk of increasing water scarcity refers to the Ebro, whereas the Adige shows better resilience to a changing climate. In the second part, this thesis deals with the uncertainty associated with climate datasets, that typically represents the largest part of the total uncertainty in hydrological modeling and, more in general, in climate change impact studies. In particular, this thesis describes a new framework for assessing the coherence of gridded meteorological datasets with streamflow observations (i.e., HyCoT - Hydrological Coherence Test). Application to the Adige catchment reveals that using inverse hydrological modeling allows testing the accuracy of gridded temperature and precipitation datasets and it may represent a tool for excluding those that are inconsistent with the hydrological response.

An elastic-visco-plastic thermomechanical model for cold forming of ceramic powders and subsequent sintering is introduced and based on micromechanical modelling of the compaction process of granulates. Micromechanics is shown to yield an upper-bound estimate to the compaction curve of a granular material, which compares well with other models and finite element simulations. The parameters of the thermomechanical model are determined on the basis of available data and dilatometer experiments. Finally, after computer implementation, validation of the model is performed with a specially designed ceramic piece showing zones of different density. The mechanical model is found to accurately describe forming and sintering of stoneware ceramics and can therefore be used to analyze and optimize industrial processes involving compaction of powders and subsequent firing of the greens.

The research “Future motorway. Design strategies for next generation infrastructure”, in its path, deals with a double important and urgent issue: the need to consider mobility infrastructures as landscape devices and the definition of a new paradigm for the motorways of the future. The main objective of the thesis is the definition of a planning strategy for the infrastructures of the future, starting from the TechnoEcoSystem concept. It is based around a double hypothesis: one theoretical, the other experimental. The first observes the definition of TechnoEcoSystem (Naveh, Lieberman,1990) from the ecology of the landscape and transfers it to the project/transformation process of the motorways. The second one identifies one of the prototypes of the Motorway TechnoEcoSystem into the service areas. As a whole, the work combines theoretical and experimental aspects, within a path of design process that through qualitative and quantitative observations, defines the 4.0 motorway through a holistic view of the system.

A second-gradient elastic material has been identified as the equivalent homogeneous material of an hexagonal lattice made up of three different orders of linear elastic bars (hinged at each junction). In particular, the material equivalent to the lattice exhibits: (i.) non-locality, (ii.) non-centrosymmetry, and (iii.) anisotropy (even if the hexagonal geometry leads to isotropy at first-order). A Cauchy elastic equivalent solid is only recovered in the limit of vanishing length of the lattice’s bars. The identification of the second-gradient elastic material is complemented by analyses of positive definiteness and symmetry of the constitutive operators. Solutions of specific mechanical problems in which the lattice response is compared to the corresponding response of an equivalent boundary value problem for the homogeneous second-gradient elastic material are presented. These comparisons show the efficacy of the proposed identification procedure.

Cells physical properties and functions like adhesion, migration and division are all regulated by an interplay between mechanical and biochemical processes occurring within and across the cell membrane. It is however known that mechanical forces spread through the cytoskeletal elements and reach equilibrium with characteristic times at least one order of magnitude smaller than the ones typically governing propagation of biochemical signals and biological phenomena like polymerization/depolymerization of protein microfilaments or even cell duplication and differentiation. This somehow allows to study as uncoupled many biochemo- mechanical events although they appear simultaneously and as concatenated. In this work, the complex machinery of the cell is hence deprived of its biochemical processes with the aim to bring out the crucial role that mechanics plays in regulating the cell as a whole as well as in terms of some interactions occurring at the interface with the extra-cellular matrix. In this sense, the single-cell is here described as a mechanical unit, endowed with an internal micro-architecture –the cytoskeleton– able to sense extra-cellular physical stimuli and to react to them through coordinated structural remodelling and stress redistribution that obey specific equilibrium principles. By coupling discrete and continuum theoretical models, cell mechanics is investigated from different perspectives, thus deriving the cell overall elastic response as the macroscopic projection of micro-structural kinematics involving subcellular constituents. Finally, some optimal arrangements of adherent cells in response to substrate-mediated elastic interactions with external loads are explored and compared with experimental evidences from the literature.

During my PhD activities, I studied the introduction of carbon-based nanofillers in materials at different scales, while focusing primarily on fibres and fibrillar materials. Several production techniques were exploited. Little is known about the interaction of graphene with electrospun polymeric fibres. Manufacturing composite fibres is complex since fillers have lateral sizes nearing that of the embedding fibre. Indeed, graphene has a direct effect in both the assembly of the electrospun composite fibres and their mechanical performance. Moreover, the tensile behaviour of hollow micrometric electrospun fibres was compared with macroscopic hollow structures such as drinking straws. The acquired insights helped to explain the toughening mechanisms at the micro-scale and develop a model capable of predicting the stress-strain response of such structures. Among natural materials, wood has the most relevant structural applications even at large scales. Its main structural component is cellulose that has a high resistance and a low light absorption. Several structural modifications of wood derived materials were recently investigated in order to enhance the mechanical and optical properties of cellulose. These enhancements can take place after the internal structure is chemically modified with the removal of lignin and after a structural densification. Potentially, any type of wood-like materials, such as giant reed (that is a fast-growing and invasive species), can be turned into a strong structural composite. Such modifications lead to an open and interconnected internal structure that is the ideal scaffold for nanoparticle intercalation. Graphene oxide and silicon carbide nanoparticles were intercalated into densified reed. They produced an even stiffer, stronger and tougher composite compared to the best up-to-date process available. Moreover, its capabilities to resist fire and water-absorption were tested. Finally, the previous process was further developed on wood to achieve a combination of improved transparency and electrical conductivity. Graphene and carbon nanotubes were introduced into the structure of wood to foster conductivity and explore the viability of its application as a self-strain sensor.

The increasing impacts of climate changes on water related sectors are leading the scientists’ attentions to the development of comprehensive models, allowing better descriptions of the water and solute transport processes. "Getting the right answers for the right reasons", in terms of hydrological response, is one of the main goals of most of the recent literature. Semi-distributed hydrological models, based on the partition of basins in hydrological response units (HRUs) to be connected, eventually, to describe a whole catchment, proved to be robust in the reproduction of observed catchment dynamics. ’Embedded reservoirs’ are often used for each HRU, to allow a consistent representation of the processes. In this work, a new semi-distributed model for runoff and evapotranspiration is presented: five different reservoirs are inter-connected in order to capture the dynamics of snow, canopy, surface flow, root-zone and groundwater compartments. The knowledge of the mass of water and solute stored and released through different outputs (e.g. discharge, evapotranspiration) allows the analysis of the hydrological travel times and solute transport in catchments. The latter have been studied extensively, with some recent benchmark contributions in the last decade. However, the literature remains obscured by different terminologies and notations, as well as model assumptions are not fully explained. The thesis presents a detailed description of a new theoretical approach that reworks the theory from the point of view of the hydrological storages and fluxes involved.Major aspects of the new theory are the ’age-ranked’ definition of the hydrological variables, the explicit treatment of evaporative fluxes and of their influence on the transport, the analysis of the outflows partitioning coefficients and the explicit formulation of the ’age-ranked’ equations for solutes.Moreover, the work presents concepts in a new systematic and clarified way, helping the application of the theory. To give substance to the theory, a small catchment in the prealpine area was chosen as an example and the results illustrated. The new semi-distributed model for runoff and evapotranspiration and the travel time theory were implemented and integrated in the semi-distributed hydrological system JGrass-NewAge. Thanks to the environmentalmodeling framework OMS3, each part of the hydrological cycle is implemented as a component that can be selected, adopted, and connected at run-time to obtain a user-customized hydrologicalmodel. The system is flexible, expandable and applicable in a variety of modeling solutions. In this work, the model code underwent to an extensive revision: new components were added (coupled storages water budget, travel times components); old components were enhanced (Kriging, shortwave, longwave, evapotranspiration, rain-snow separation, SWE and melting components); documentation was standardized and deployed. Since the Thesis regards in wide sense the building of a collaborative system, a discussion of some general purpose tools that were implemented or improved for supporting the present research is also presented. They include the description and the verification of a software component dealing with the long-wave radiation budget and another component dealing with an implementation of some Kriging procedure.

Recent epidemiological studies have reinforced the link between short and long-term exposure to air pollutants and adverse effects on public health especially over the weaker part of the population, like children and older adults. The creation of simple tools to locate sensible areas as well as of dedicated Spatial Decision Support System (SDSS) to improve the management of pollution risk areas system is strongly advised. The aim of this work is to develop a SDSS methodology, based on easy to find data and usable by decision makers, to assess and reduce the impact of air pollutants in a urban context. To achieve this goals I tested the exploitability of a set of low-cost sensors for outdoor air quality monitoring, I characterized the urban micro-environments and the spatial variability of air pollutants using remote sensing compared to field data and eventually I developed a SDSS to improve the public health designing and comparing different scenarios. The city centre of Edinburgh has been used as study case for the purposed methodology. To test the reliability and applicability of low cost sensors as proxies for remote sensed data, we conducted a measurements campaign to compare the observed data between an official measurements station (OMS) in Trento (Italy) and electrochemical and thick film sensors respectively of Carbon Monoxide (CO) and Ozone ($O_3$). Due to data quality and availability we decided to characterize the urban micro-environments of Edinburgh (Scotland, UK) in eight main classes (water, grass, vegetation, road, car, bus, buildings and shadow) combining the Geographic Object-Based Image Analysis (GEOBIA) with Machine Learning algorithms to process the high resolution (0.25m x 0.25m) RGB aerial ortho-rectified images. This land-use characterization combined with other geographical informations, like the classification of the roads and the urban morphology, were compared with 37 Nitrogen Dioxide (NO2) concentration data, collected using passive tubes during a six week campaign of measurements conducted by the school of Chemistry of the University of Edinburgh. I developed a new open-source GIS python library (PyGRASS), integrated in the stable release of GRASS GIS, to speed-up the prototyping phase and to create and test new GIS tools and methodologies. Different studies on SDSS were carried out to implement procedures and models. Based on these models and data all the factors (land-use, roads and geo-morphological features) were ranked to identify which are driving forces for urban air quality and to help decision makers to develop new policies. The sensor tested in Trento revealed an evident drift in measurement residues for CO, furthermore the measurements were also quite sensitive to external factors such as temperature and humidity. Since these sensors required frequent recalibration in order to obtain reliable results, their use was not as low-cost as expected. The characterization of urban land-use in Edinburgh with GEOBIA and machine learning provided an overall accuracy of 93.71\% with a Cohen's k of 0.916 using a train/test dataset of 9301 objects. The $NO_2$ data confirm the assumption that air concentration is strongly dependent on geographical position and it is strongly influenced by the position of the pollutant's source. Using the results of the tests and remote sensing analysis, I developed an SDSS. Starting from the current situation, I designed three scenarios to assess the effect that different policies and actions could have on improving air quality at on the local and district level. The outcomes of this work can be used to define and compare different scenarios and develop effective policies to reduce the impact of air pollutants in an urban context using simple and easy to find data. The GIS-based tool can help to identify critical areas before deploying sensors and splitting the study area in homogeneous micro-environments clusters. The model is easy to expand following different procedures.