Umberto Fratino

Assessing Pressure Changes in an On-Demand Water Distribution System on Drip Irrigation Performance—Case Study in Italy

The hydrant pressure head in an on-demand water distribution system can be subject to high fluctuation depending on the discharge flowing inside the pipes, with consequent impacts on the performance of on-farm irrigation systems. In this work, an Italian water distribution system was analyzed using the AKLA model at upstream discharges of 1,200 and 600 L⋅ s−1 to estimate the range of hydrant pressure variation. A computer model was developed, calibrated, and used to evaluate the performance of a drip irrigation system by relating the on-farm network with the hydrant characteristic curve at a certain operating status. The flow regulator within the hydrant played an important role in stabilizing the performance of the network at hydrant pressures higher than 27 m. At lower hydrant pressures, to apply the same amount of water, irrigation time must be extended by 17 and 95% for pressure heads of 20 and 12 m, respectively. These approaches described have great utility to ensure adequate irrigation management w...

A data fusion algorithm based on the Kalman filter to estimate leaf area index evolution in durum wheat by using field measurements and MODIS surface reflectance data

ABSTRACT The use of leaf area index (LAI) is essential in ecosystem and agronomic studies since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from passive remotely sensed data although estimated results were often affected by noise and measurement uncertainties. In this article, we propose a Kalman filter algorithm in order to estimate the time evolution of LAI by combining field-measured and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. The scalar equation of the dynamic LAI model (state transition model) was derived by the field-measured data while the MODIS red, near-infrared and shortwave infrared reflectance data were used to implement the observation model. The reflectance data were linked to LAI by using the reduced simple ratio. The method was tested in an experimental field located in the north-western part of the Apulia region (Italy). The results showed a good agreement between the LAI estimated through the algorithm and the LAI derived from field data, with a coefficient of determination (R2) of 0.96 and a corresponding root mean square error of 0.124.

A Bayesian vulnerability assessment tool for drinking water mains under extreme events

Drinking water security is a life safety issue as an adequate supply of safe water is essential for economic, social and sanitary reasons. Damage to any element of a water system, as well as corruption of resource quality, may have significant effects on the population it serves and on all other dependent resources and activities. As well as an analysis of the reliability of water distribution systems in ordinary conditions, it is also crucial to assess system vulnerability in the event of natural disasters and of malicious or accidental anthropogenic acts. The present work summarizes the initial results of research activities that are underway with the intention of developing a vulnerability assessment methodology for drinking water infrastructures subject to hazardous events. The main aim of the work was therefore to provide decision makers with an effective operational tool which could support them mainly to increase risk awareness and preparedness and, possibly, to ease emergency management. The proposed tool is based on Bayesian Belief Networks (BBN), a probabilistic methodology which has demonstrated outstanding potential to integrate a range of sources of knowledge, a great flexibility and the ability to handle in a mathematically sound way uncertainty due to data scarcity and/or limited knowledge of the system to be managed. The tool was implemented to analyze the vulnerability of two of the most important water supply systems in the Apulia region (southern Italy) which have been damaged in the past by natural hazards. As well as being useful for testing and improving the predictive capabilities of the methodology and for possibly modifying its structure and features, the case studies have also helped to underline its strengths and weaknesses. Particularly, the experiences carried out demonstrated how the use of BBN was consistent with the lack of data reliability, quality and accessibility which are typical of complex infrastructures, such as the water distribution networks. The potential applications and future developments of the proposed tool have been also discussed accordingly.

The CSI-based RICH detector array for the identification of high momentum particles in ALICE

Abstract After ten years of R&D activities, an array of seven proximity focusing RICH modules is being built to identify π - K in the range 1 p c and K-p in the range 1.5 p 2 , represents the largest scale application of MWPCs with high Quantum Efficiency (QE) CsI segmented photo-cathodes for the Cherenkov photon conversion. An overview of the RICH layout, the technique of photocathode production, the front-end (FE) and readout (R/O) electronics and finally the detector control system (DCS), are presented.

Comparing the MLC and JavaNNS Approaches in Classifying Multi-Temporal LANDSAT Satellite Imagery over an Ephemeral River Area

This paper analyzes two pixel-based classification approaches to support the analysis of land cover transformations based on multitemporal LANDSAT sensor data covering a time space of about 24 years. The research activity presented in this paper was carried out using Lama San Giorgio (Bari, Italy) catchment area as a study case, being this area prone to flooding as proved by its geological and hydrological characteristics and by the significant number of floods occurred in the past. Land cover classes were defined in accordance with on the CN method with the aim of characterizing land use based on attitude to generate runoff. Two different classifiers, i.e. Maximum Likelihood Classifier (MLC) and Java Neural Network Simulator (JavaNNS) models, were compared. The Artificial Neural Networks (ANN) approach was found to be the most reliable and efficient when lacking ground reference data and a priori knowledge on input data distribution.

An object-based method for mapping ephemeral river areas from WorldView-2 satellite data

Continuous monitoring of river basins has become a significant requirement of our times. Due to increasing water scarcity and unprecedented flood calamities, assessing existing water resources and gathering timely information on water increase are nowadays essential to develop suitable strategies in water resources management. Hydrological models are being studied to increase hydrological process understanding and to support decision making in this field. River basin management models typically operate on wide territories and, given the complexity of most river basins, they are based on semi-empirical lumped parameterizations of hydrological processes. To overcome the uncertainties inherent in such models and achieve acceptable model performance, calibration techniques are indispensable. Remote sensing and satellite-based data with high temporal resolution have the potential to fill such critical information gaps. With its nine spectral bands and very high resolutions (spectral and radiometric) WorldView-2 satellite sensor (WV-2) can provide new insights in the on-going debate comparing object-oriented and spectral-based classifications for the highest accuracy. This paper proposes an efficient object-based method for land cover mapping from Worldview-2 imagery in order to assess its potentiality in acquiring detailed basic information on an ephemeral river area (Lama di Castellaneta, Taranto, Italy), to support further studies in the field of hydrological processes modeling. The approach suggested was evaluated by estimating classification accuracy.

Head loss coefficient of orifice plate energy dissipator

The authors have presented a numerical and experimental study on the hydraulic behaviour of a single-hole orifice, finding that the loss coefficient and the recirculation length are influenced by the contraction ratio β = d/D and the relative orifice thickness α. This is interesting, because nowadays not all the aspects of orifice hydraulics are clarified, particularly the effects of orifice geometry on the efficiency and cavitation behaviour. Fratino (2000) and Malavasi et al. (2010) experimentally studied the hydraulic performance of perforated plates, both for singleand multi-hole orifices, finding a relevant effect on the loss coefficient due to the plate geometry, related to the relative plate thickness l̄ = s/dh as the ratio between plate thickness and hole diameter. For thick-edged orifices (l̄ > 0.015), and fully-developed approach flow of Reynolds number R = VD/ν > 105, where V = mean approach flow velocity, D = pipe diameter, ν = kinematic viscosity, Idelchik (1994) proposed for the loss coefficient ξ

Nappe flow over horizontal stepped chutes

Stepped chutes have been frequently used since the 1980s in order to take advantage of their dissipation efficiency, aeration characteristics and relative ease of construction. Two basic flow regimes exist for stepped chutes: nappe flow and skimming flow, with the nappe flow having received less attention in comparison to the skimming flow regime. This work suggests a simple iterative method, valid for sub-to-supercritical approach flow conditions, for predicting the basic nappe flow features on a horizontal multi-step structure. The proposed calculation procedure, if applied along the stepped chute, allows us to calculate the flow depth at each step brink and at each nappe impact point, the thickness of the water cushion beneath the nappe, the jet impact length and the energy dissipation rate. For validating the proposed analytical approach, a comparison between experimental and calculated data is also presented.

Emergency Management of Drinking Water Infrastructures Based on a Bayesian Decision Support System

The present work summarizes the theoretical development process and the preliminary results of a research activity oriented to the definition of a Decision Support System (DSS) to be used for managing drinking water systems exposed to different hazard classes. The core of such DSS is a probabilistic vulnerability assessment tool based on Bayesian Belief Networks, mainly developed integrating expert knowledge and literature information. This vulnerability assessment tool proved able to define a reliable map of vulnerability levels for complex and interconnected infrastructures, thus helping decision-makers in the selection of the optimal strategies to respond to emergencies. The DSS is based also on the implementation of hydraulic models, both for gravity and pressurized water mains, which should provide information regarding the changes in the hydraulic behavior of the network due to a specific event or an action. A case study is described, confirming the potentialities of the proposed tool.

Status of the HMPID CsI-RICH project for ALICE at the CERN/LHC

The ALICE (A Large Ion Collider Experiment) high momentum particle identification (HMPID) detector, presently under construction, consists of seven identical proximity focusing ring imaging Cherenkov (RICH) counters exploiting large area CsI photocathodes for Cherenkov light imaging. With a total area of 11 m/sup 2/, it represents the largest CsI-RICH system ever used in High Energy Physics. The detector layout, assembly and quality checks will be presented, with particular emphasis on CsI photocathodes mass production. A validation procedure has been established combining the results of the photocathode response mapping obtained in a dedicated VUV scanner with test beam data. The long-term stability has also been studied by irradiation with a Sr-90 source of a final size CsI photocathode inside a detector prototype.The ALICE (A Large Ion Collider Experiment) high momentum particle identification (HMPID) detector, presently under construction, consists of seven identical proximity focusing ring imaging Cherenkov (RICH) counters exploiting large area CsI photocathodes for Cherenkov light imaging. With a total area of 11 m/sup 2/, it represents the largest CsI-RICH system ever used in High Energy Physics. The detector layout, assembly and quality checks will be presented, with particular emphasis on CsI photocathodes mass production. A validation procedure has been established combining the results of the photocathode response mapping obtained in a dedicated VUV scanner with test beam data. The long-term stability has also been studied by irradiation with a Sr-90 source of a final size CsI photocathode inside a detector prototype.