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The work of Stefano Bagli and Paolo Mazzoli was partly funded by the Autonomous Province of Bolzano under an R&D grant to GECOsistema srl (L.P. 14/2006), project "IASMHyN". ...doi:10.3390/w9110848 fatcat:yklkvz3mwzdpbf2vbsj6mxgr44
While planning coastal risk management strategies, coastal managers need to assess risk across a range of spatial and temporal scales. GIS-based tools are one efficient way to support them in the decision making process through a scenarios analysis starting from social, economic and environmental information integrated into a common platform. However, this integration process requires a significant effort from a team of scientists in terms of a) identifying the appropriate scales and datadoi:10.1016/j.coastaleng.2013.11.013 fatcat:wr2erugnd5grhkpo7k2rsoechm
more »... tion for analysing social, environmental and economic issues; b) selecting and linking an appropriate set of tools to build a coupled model; c) representing key emerging (and hence challenging) research issues, such as risk perception and social resilience in the model; d) developing multi-criteria analysis to integrate social, environmental, economic impacts; and e) accounting for the expectations of the stakeholders and therefore optimizing the opportunity for them to interact with the tool development and with the final tool itself. In this spirit, this paper presents an open-source Spatial Decision Support System developed within the THESEUS Project to help decision makers to scopeg optimal strategies to minimise coastal risks. The exploratory tool allows the users to perform an integrated coastal risk assessment, to analyse the effects of different combinations of engineering, social, economic and ecologically based mitigation options, across short (2020s), medium (2050s) and long-term (2080s) scenarios, taking into account physical and non-physical drivers, such as climate change, subsidence, population and economic growth.
Topography is a critical element in the hydrological response of a drainage basin and its availability in the form of digital elevation models (DEMs) has advanced the modeling of hydrological and hydraulic processes. However, progress experienced in these fields may stall, as intrinsic characteristics of free DEMs may limit new findings, while at the same time new releases of free, high-accuracy, global digital terrain models are still uncertain. In this paper, the limiting nature of free DEMsdoi:10.3389/feart.2019.00141 fatcat:24vtcvlfjvdtbdpmjslh5dxpei
more »... s dissected in the context of hydrogeomorphology. Ten sets of terrain data are analyzed: the SRTM GL1 and GL3, HydroSHEDS, TINITALY, ASTER GDEM, EU DEM, VFP, ALOS AW3D30, MERIT and the TDX. In specific, the influence of three parameters are investigated, i.e., spatial resolution, hydrological reconditioning and vertical accuracy, on four relevant geomorphic terrain descriptors, namely the upslope contributing area, the local slope, the elevation difference and the flow path distance to the nearest stream, H and D, respectively. The Tanaro river basin in Italy is chosen as the study region and the newly released LiDAR for the Italian territory is used as benchmark to reassess vertical accuracies. In addition, the EU-Hydro photo-interpreted river network is used to compare DEM-based river networks. Most DEMs approximate well the frequency curve of elevations of the LiDAR, but this is not necessarily reflected in the representation of geomorphic features. For example, DEMs with finer spatial resolution present larger contributing areas; differences in the slope can reach 10%; between 5 m and 12 m H, none of the considered DEMs can faithfully represent the LiDAR; D presents significant variability between DEMs; and river network extraction can be problematic in flatter terrain. It is also found that the lowest mean absolute error (MAE) is given by the MERIT, 2.85 m, while the lowest root mean square error (RMSE) is given by the SRTM GL3, 4.83 m. Practical implications of choosing a DEM over another may be expected, as the limitations of any particular DEM in faithfully reproducing critical geomorphic terrain features may hinder our ability to find satisfactory answers to some pressing problems.
Modern Diesel engines are attractive for fuel economy and performances but they are suffering from increasingly strict emission standards. Therefore the investigation of the injection and combustion processes are mandatory. This paper focuses on the development of a multi-component fuel based methodology for the simulation of non-reacting and reacting high injection pressure Diesel sprays. In multi-dimensional modeling fuels are represented predominantly by single components, such as n-Dodecanedoi:10.1016/j.egypro.2014.01.093 fatcat:oz6aukrflne6lpfs37vf7iivaa
more »... for Diesel, and this is a limitation in their ability to represent real fuels which are blends of hundreds components. This study outlines a method by which the fuel composition is represented by means of a Discrete Multi-Component (DMC) model approach in order to improve the prediction of the vaporization behavior of high injection pressure Diesel sprays. A testing blend of 6 hydrocarbons is taken into account and a reduced one is developed in order to reduce the computational cost of the CFD simulations while maintaining the advantages due to a multi-component description of the mixture. The CFD methodology is developed within Star-CD commercial code while particular care is also dedicated to the prediction of the atomization and secondary breakup processes. At the nozzle exit the atomized droplets are predicted by a primary breakup approach which is able to take into account the cavitation phenomena and the turbulent effects. The atomization model is based on a simplified approach that is able to evaluate the effects of the nozzle geometry. The preliminary investigations are performed in a constant volume vessel, validating the numerical parameters against experimental data in order to correctly reproduce spray vaporization behavior. Then, to illustrate the important differences between the vaporization characteristics of a multi-component mixture compared to a mono-component one, the CFD methodology is tested investigating the in-cylinder combustion process of a 4 cylinders, Common Rail Diesel engine of current production.
Bagli), email@example.com (D. Geneletti), firstname.lastname@example.org (F. Orsi). Environmental Impact Assessment Review 31 (2011) 234-239 1 Tel. ...doi:10.1016/j.eiar.2010.10.003 fatcat:pbm7gq4hv5e7tl4pahtnxhvhq4
Abstract. The combined effect of global sea level rise and land subsidence phenomena poses a major threat to coastal settlements. Coastal flooding events are expected to grow in frequency and magnitude, increasing the potential economic losses and costs of adaptation. In Italy, a large share of the population and economic activities are located along the low-lying coastal plain of the North Adriatic coast, one of the most sensitive areas to relative sea level changes. Over the last half adoi:10.5194/nhess-22-265-2022 fatcat:33dwnjxf5nckdlgpsltr6acf5a
more »... y, this stretch of coast has experienced a significant rise in relative sea level, the main component of which was land subsidence; in the forthcoming decades, climate-induced sea level rise is expected to become the first driver of coastal inundation hazard. We propose an assessment of flood hazard and risk linked with extreme sea level scenarios, under both historical conditions and sea level rise projections in 2050 and 2100. We run a hydrodynamic inundation model on two pilot sites located along the North Adriatic coast of Emilia-Romagna: Rimini and Cesenatico. Here, we compare alternative extreme sea level scenarios accounting for the effect of planned and hypothetical seaside renovation projects against the historical baseline. We apply a flood damage model to estimate the potential economic damage linked to flood scenarios, and we calculate the change in expected annual damage according to changes in the relative sea level. Finally, damage reduction benefits are evaluated by means of cost–benefit analysis. Results suggest an overall profitability of the investigated projects over time, with increasing benefits due to increased probability of intense flooding in the near future.
<p><strong>Abstract.</strong> Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall–runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall–runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow–durationdoi:10.5194/hess-22-4633-2018 fatcat:ghj4j36475c5jiy6u6z5eazxxa
more »... es (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ≈ 0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000<span class="thinspace"></span>km<sup>2</sup>).</p>
Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall-runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall-runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow-duration curves (FDCs) aredoi:10.5194/hess-2017-589 fatcat:q4kvg2xcbnbndnam73r7wepfbe
more »... predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980&ndash;2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional NSE increases from nearly zero to ≈&thinsp;0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000&thinsp;km<sup>2</sup>).
This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir ofdoi:10.3390/atmos11121305 fatcat:krazxewqovafrlwbsu37edjh5m
more »... plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets.
The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g., cloudbursts, rainstorms, heavy rainfall, hail, heavy snow), combined with the high population density and concentration of assets, makes urban areas particularly vulnerable to pluvial flooding. Hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modeling is resource intensive and thereforedoi:10.3390/w12061514 fatcat:2m7c3iej65bdxk3px6wiynbxpq
more »... rcely suitable for performing consistent hazard assessments across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential of fast-processing DEM-based methods, such as the Hierarchical Filling-&-Spilling or Puddle-to-Puddle Dynamic Filling-&-Spilling Algorithms (abbreviated herein as HFSAs). We develop a fast-processing HFSA, named Safer_RAIN, that enables mapping of pluvial flooding in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. We present the first applications of the algorithm to two case studies in Northern Italy. Safer_RAIN output is compared against ground evidence and detailed output from a two-dimensional (2D) hydrologic and hydraulic numerical model (overall index of agreement between Safer_RAIN and 2D benchmark model: sensitivity and specificity up to 71% and 99%, respectively), highlighting potential and limitations of the proposed algorithm for identifying pluvial flood-hazard hotspots across large urban environments.
Esame finale anno 2015 Acknowledgements.fatcat:pr2diyz4jjfx7iq2tyrunqo3xu
Science and practice for an uncertain future
Local flood risk assessments require integration of many detailed data sources from the public and private sector. In the framework of the EIT Climate-KIC Demonstrator project "SaferPLACES", we explore how openly available datasets can be harnessed to both reanalyse past flood losses and estimate potential present and future flood damages. Three different cases studies were selected: Cologne in Germany, Rimini in Italy, and Pamplona in Spain. Each city has different size, economic structure anddoi:10.3311/floodrisk2020.9.1 fatcat:lcld45jxxvcgzkndyxbpskjjfe
more »... is subject to different types of flood events, namely fluvial, pluvial, and coastal. Here, we concentrate on methods for extracting exposure and predicting vulnerability for the residential and commercial sectors with openly available and crowd-sourced spatial datasets and public statistical data. Exposure is quantified at building level, covering residential and commercial assets. Further, vulnerability is calculated by Bayesian Network-based probabilistic models for residential and commercial sectors created on the basis of postdisaster household and company surveys. Finally, we use flood compensation data from a major flood in Pamplona in 2013 and analyse whether the cascade of models is able to recreate flood damages from a particular event. Flood risk estimates for Rimini are also shown to highlight the application of the project's model chain.
Marin Drinov", Sofya, 1999, s. 222. 132 Ot San Stefano…, s. 188. 133 Ot San Stefano…, s. 188. 134 Mançev, Natsionalniyat Vıpros…, s. 222. 135 Ot San Stefano…, s. 189. 136 Ot San Stefano…, s. 189. 137 Mançev ... Ot San Stefano…, s. 150. 90 Ot San Stefano…, s. 151. 91 Semov, Velikite Sili i Bılgarskata…, s. 227. ...doi:10.1501/tite_0000000462 fatcat:e65xbupnwrbsxdjgsyoztilibi
Mevcut belge Grandukalığa bağlı önemli şehirlerin bir kısmını tek tek yazmakta, buna karşılık Toskana ismini zikretmemektedir. ... Toskana Grandukalığına bağlı Livorno'nun, Osmanlı belgelerinde bir değişime uğrayarak böyle bir imla ile kaydedildiği düşünülebilir. ...doi:10.30517/cihannuma.283516 fatcat:7ter4jxkcveh3h6qdfqcmbnkii
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