Evidences of relationships between statistics of rainfall extremes and mean annual precipitation: an application for design-storm estimation in northern central Italy

International audienceSeveral hydrological analyses need to be founded on a reliable estimate of the design storm, which is the expected rainfall depth corresponding to a given duration and probability of occurrence, usually expressed in terms of return period. The annual series of precipitation maxima for storm duration ranging from 15 min to 1 day are observed at a dense network of raingauges sited in northern central Italy are statistically analyzed using an approach based on L-moments. The study investigates the statistical properties of rainfall extremes and identifies important relationships between these properties and the mean annual precipitation (MAP). On the basis of these relationships, we develop a regional model for estimating the rainfall depth for a given storm duration and recurrence interval in any location of the study region. The reliability of the regional model is assessed through Monte Carlo simulations. The results are relevant given that the proposed model is able to reproduce the statistical properties of rainfall extremes observed for the study region

Relationships between statistics of rainfall extremes and mean annual precipitation: an application for design-storm estimation in northern central Italy

Several hydrological analyses need to be founded on a reliable estimate of the design storm, which is the expected rainfall depth corresponding to a given duration and probability of occurrence, usually expressed in terms of return period. The annual series of precipitation maxima for storm duration ranging from 15 min to 1 day, observed at a dense network of raingauges sited in northern central Italy, are analyzed using an approach based on L-moments. The analysis investigates the statistical properties of rainfall extremes and detects significant relationships between these properties and the mean annual precipitation (MAP). On the basis of these relationships, we developed a regional model for estimating the rainfall depth for a given storm duration and recurrence interval in any location of the study region. The applicability of the regional model was assessed through Monte Carlo simulations. The uncertainty of the model for ungauged sites was quantified through an extensive cross-validation

Multiple hazards and risk perceptions over time: the availability heuristic in Italy and Sweden under COVID-19

The severe impact of global crises, such as COVID-19 and climate change, is plausibly reshaping the way in which people perceive risks. In this paper, we examine and compare how global crises and local disasters influence public perceptions of multiple hazards in Italy and Sweden. To this end, we integrate information about the occurrence of hazardous events with the results of two nationwide surveys. These included more than 4000 participants and were conducted in two different phases of the COVID-19 pandemic corresponding to low (August 2020) and high (November 2020) levels of infection rates. We found that, in both countries, people are more worried about risks related to experienced events. This is in line with the cognitive process known as the availability heuristic: individuals assess the risk associated with a given hazard based on how easily it comes to their mind. Epidemics, for example, are perceived as less likely and more impactful in Italy compared to Sweden. This outcome can be explained by cross-country differences in the impact of, as well as governmental responses to, COVID-19. Notwithstanding the ongoing pandemic, people in both Italy and Sweden are highly concerned about climate change, and they rank it as the most likely threat

Comparison of genetic algorithms used to evolve specialisation in groups of robots

This paper investigates the role of genetic algorithms in determining which kind of specialisation emerges in decentralised simulated teams of robots controlled by evolved neural networks. As shown in previous works, different tasks may be better solved by robots specialized in a particular manner. However it was not clarified how much the genetic algorithm used might drive the evolution of one kind of specialisation or another: this is the goal of this paper. The study is conducted by evolving teams of robots that have to solve two different tasks that are better accomplished by using different types of specialisation (innate versus situated). Results suggest that the type of genetic algorithm employed plays a major role in determining how robots specialize and in most of the cases the algorithms used tend to always yield the same specialization. Only one of the algorithms tested led to the emergence of the most suitable kind of specialisation for each one of the two tasks

Reconstruction and Analysis of the Po River Inundation of 1951

Flood inundation models have become essential tools in flood risk management, being used also in the analysis of historical flood events, which is often needed for a reliable assessment of the potential flood hazard. This study aims at reconstructing the 1951 inundation of the Polesine Region, Italy. The 1951 flooding was a mayor natural catastrophe that caused a large inundated area (1080 km2) and produced devastating social consequences. The reconstruction of the 1951 hydraulic conditions is based on partial knowledge of discharges and water stages at the Pontelagoscuro gauging station (downstream of the 1951 levee breach) using extrapolation of the rating curves beyond the measurement range. This is, even today, something open to uncertainty. We applied a decoupled hybrid approach to the hydraulic modeling: a 1D model is used to simulate the flow into the river and to compute the flow through the levee breach; this result is then adopted as the inflow condition for a 2D model application on the inundated area. A good agreement between the patterns of the observed and reconstructed inundation areas was found, and the timing of the inundation was also found to be close to the information derived from the historical chronicles. The results of the flood inundation modelling exercise provide two practical insight: (i) obstacles in the floodplains increased the flooded area by 40% and prolonged the time to reach the sea from 5 to 15 days, (ii) the peak discharge of the event was overestimated by up to 20

Socio-hydrological spaces in the Jamuna River floodplain in Bangladesh

Socio-hydrology aims to understand the dynamics and co-evolution of coupled human–water systems, with research consisting of generic models as well as specific case studies. In this paper, we propose a concept to help bridge the gap between these two types of socio-hydrological studies: socio-hydrological spaces (SHSs). A socio-hydrological space is a geographical area in a landscape. Its particular combination of hydrological and social features gives rise to the emergence of distinct interactions and dynamics (patterns) between society and water. Socio-hydrological research on human–flood interactions has found two generic responses, fight or adapt. Distilling the patterns resulting from these responses in case studies provides a promising way to relate contextual specificities to the generic patterns described by conceptual models. Through the use of SHSs, different cases can be compared globally without aspiring to capturing them in a formal model. We illustrate the use of SHS for the Jamuna floodplain, Bangladesh. We use narratives and experiences of local experts and inhabitants to empirically describe and delimit SHS. We corroborated the resulting classification through the statistical analysis of primary data collected for the purpose (household surveys and focus group discussions) and secondary data (statistics, maps etc.). Our example of the use of SHSs shows that the concept draws attention to how historical patterns in the co-evolution of social behaviour, natural processes and technological interventions give rise to different landscapes, different styles of living and different ways of organising livelihoods. This provides a texture to the more generic patterns generated by socio-hydrological models, promising to make the resulting analysis more directly useful for decision makers. We propose that the usefulness of this concept in other floodplains, and for other socio-hydrological systems than floodplains, should be explored.</p

Analysis and performance assessment of the use of ammonia-based nano additive for lean combustion

In recent years, considerable progress has been made in exploring new applications of fuel additives to reduce emissions. Reduction of total nitrogen oxide (NOx) emissions can be achieved by decreasing the flame temperature by using fuel emulsified with water and/or using ammonia-based nano additives such as urea. The use of water as part of the hydrocarbon fuel is also one of the prospective directions in the development of new types of fuel systems. For the preparation of emulsified fuel, it is desirable to achieve greater stability of the emulsified fuel with minimum expenditure of chemicals and energy, so that the emulsified fuel can be used for a longer period. The paper analyzed the influence of nano-dispersed urea particles, water, and surfactant (Span 80/ Tween 80) on the combustion stability and emission characteristics of aviation fuel. The experimental campaign was conducted on a test stand (a 300kW liquid vortex combustor of 300 kW) consisting of a cylindrical combustion chamber with four optical windows and equipped with high-precision pressure sensors, thermocouples, and an exhaust gas analyzer for acquiring emissions. The experimental campaign was conducted at a constant fuel/air ratio (Φ). One of the main focus is related to the stability of the emulsion. Chemiluminescence imaging was performed to characterize the effects of the additive on flame emissions. In addition, a statistical and spectral analysis was performed using the pressure sensor for instability analysis. Exhaust gas analysis was performed both with the additive described above and without additive for a constant Φ condition. The analysis was performed for NOx, carbon monoxide (CO) and carbon dioxide (CO2) and oxygen (O 2)

An entropy method for floodplain monitoring network design

In recent years an increasing number of flood-related fatalities has highlighted the necessity of improving flood risk management to reduce human and economic losses. In this framework, monitoring of flood-prone areas is a key factor for building a resilient environment. In this paper a method for designing a floodplain monitoring network is presented. A redundant network of cheap wireless sensors (GridStix) measuring water depth is considered over a reach of the River Dee (UK), with sensors placed both in the channel and in the floodplain. Through a Three Objective Optimization Problem (TOOP) the best layouts of sensors are evaluated, minimizing their redundancy, maximizing their joint information content and maximizing the accuracy of the observations. A simple raster-based inundation model (LISFLOOD-FP) is used to generate a synthetic GridStix data set of water stages. The Digital Elevation Model (DEM) that is used for hydraulic model building is the globally and freely available SRTM DEM