88 research outputs found
Influenza della densità della rete di pluviografi sulla modellazione idrologica delle piene. Analisi statistica attraverso campi di pioggia sintetici su bacini di differente dimensione e confronto con un caso reale
No full textAl fine di creare uno scenario di controllo non condizionato da errori di misura e di modellazione, è stata assunta come precipitazione di riferimento, un campo di precipitazione ad alta risoluzione spaziale e temporale (1.5 km x 1.5 km x 5 minuti, di durata pari a 80 anni) ottenuto attraverso un processo di downscaling, mentre come portata di riferimento è stato assunto il corrispondente idrogramma ottenuto attraverso due modelli afflussi-deflussi. Al fine di studiare la sensibilità della risposta idrologica in relazione alla densità della rete pluviografica, è stato supposto che la serie storica di pioggia estratta da una singola cella (1.5 km x 1.5 km) sia equivalente alla registrazione effettuata da un potenziale pluviografo fittizio, ed è stato considerato un numero variabile di potenziali pluviografi, distribuiti casualmente fra i punti del grigliato. Quindi per estensioni della rete variabili da 1 a 30 pluviografi (per ciascun bacino considerato), sono stati selezionati in modo casuale 100 combinazioni spaziali indipendenti di pluviografi, ottenendo altrettanti scenari di campi di precipitazione, che utilizzati come input dei due modelli afflussi-deflussi hanno consentito di realizzare 100 corrispondenti scenari di portata.
La qualità dei risultati è stata valutata confrontando gli scenari di portata (ottenuti attraverso un numero limitato di pluviografi) con la portata di riferimento (ottenuta attraverso l’intero campo di precipitazione ad alta risoluzione) applicando differenti metriche. Un’analisi critica dei vantaggi dell’utilizzo della modellazione distribuita rispetto a quella a parametri concentrati è stata eseguita considerando: variabilità delle performance dei modelli in relazione al numero di pluviografi utilizzati; relazione tra le performance dei modelli e l’entità dell’evento meteorico; numero minimo di pluviografi necessari per ottenere una risposta idrologica soddisfacente di ciascun modello e relazione con l’entità dell’evento; dipendenza rispetto alla dimensione del bacino.
Osservando la figura 1 possono essere illustrati alcuni dei risultati ottenuti. In particolare si nota come il numero minimo di pluviografi necessario a raggiungere un valore soddisfacente dell’efficienza di Nash Sutcliffe sia fortemente dipendente dall’entità dell’evento, questo comportamento è più accentuato per il modello a parametri concentrati. Con entrambi i modelli le simulazioni risultano migliori, a parità di pluviografi, per gli eventi di maggiore entità. Il modello a parametri concentrati presenta inoltre una marcata dipendenza con la dimensione del bacino, mentre il modello distribuito non ne risulta significativamente influenzato. In conclusione è importante notare che anche in bacini di piccole dimensioni sono necessari non meno di 5 pluviografi affinché il 50% degli eventi possa essere modellato con un’efficienza pari o maggiore di 0.5.
Infine è stato studiato un caso reale di 2 piccoli bacini provvisti di serie sufficientemente lunghe di osservazioni di precipitazione ad alta risoluzione (5 minuti) e di corrispondenti misure di portata. In particolare sono state valutate le performance dei due modelli afflussi-deflussi, confrontando la portata osservata con gli idrogrammi prodotti considerando le possibili combinazioni di pluviografi esistenti. Sebbene l’analisi sia limitata ad un numero massimo di tre pluviografi, risultano confermati i risultati ottenuti attraverso lo scenario sintetico sopra descritto
Influence of rain gauge network density on flood model prediction: a statistical investigation using synthetic rainfall fields on basins of different size and a comparison with a real case
No full textThe accuracy of simulation of the catchment hydrologic response is strongly affected by the reliable representation of the spatial rainfall pattern. The present work investigates the role of rainfall sampling and network density on the performance of a lumped and a distributed rainfall-runoff model in predicting extreme floods. The analysis is conducted on a suite of 12 basins of different size ranging from 15 to 1793 km2 , located in Sardinia, Italy.
In order to create a reference framework uncorrupted by errors of measure and of modelling, we assume as reference precipitation an high resolution rainfall field (1.5 km x 1.5 km x 5 min, 80 years long) derived through a downscaling procedure, and as reference discharge the corresponding hydrograph obtained by the two rainfall-runoff models. In order to investigate the sensitivity of the hydrological response to the rain gauge network density we assume that a rainfall series from a single cell (1.5 km x 1.5 km) is equivalent to a potential fictitious rain gauge record and consequently we activate a number of potential gauges ranging from 1 up to 30 (for each considered basin). Then for each fixed network size, we randomly select 100 independent spatial combinations of rain gauge positions providing the rainfall pattern scenarios which are used as input of the two rainfall-runoff models to produce an ensemble of 100 corresponding discharge scenarios.
Performances are evaluated by comparing the discharge scenarios (obtained by a limited number of potential rain gauges) with the reference discharge (obtained by the entire high resolution rainfall fields) and applying different metrics. A critical analysis of the advantage of using distributed vs lumped modelling is performed considering: model performance variability related to the number of rain gauges; model performance dependence on event magnitude; minimum number of rain gauges for a satisfying model performance and its relationship with event magnitude; dependence on the basin size.
Finally we analyse the real case of 2 small basins with a good record of hydrometric and high temporal resolution (5 min) pluviometric observations. Specifically we evaluate the performances by comparing the observed runoff with the hydrographic response produced by the rainfall-runoff models and considering as active a different number of rain gauges. Although this analysis is limited to a maximum of three rain gauges, it confirms the results obtained in the synthetic framework described above
The efficiency of the top mega yacht builders across the world: a financial ratio-based data envelopment analysis
No full textThis research provides an application of a non-parametric analytic technique (Data Envelopment Analysis, DEA) in measuring the performance of the mega yacht sector. It analyses the efficiency of the top mega yacht companies across the world in 2005-2013 by offering a model useful for comparing inefficient shipbuilders with the efficient ones. This paper adopts an output-oriented version of DEA based on financial ratios where inputs are not utilised. In order to handle missing data, we test and compare two different techniques: the deletion one and the multiple linear regression analysis (MLRA). We find that DEA can be a complement or alternative tool to ratio analysis to evaluate corporates’ performance. We also find that the most efficient shipbuilders are those based in the most prosperous countries. Finally, the MLRA efficiency scores are more reliable and consistent with the firms’ annual reports and financial ratios
Implementing the street-turn strategy by an optimization model
No full textThe street-turn option represents a major strategy for the profitability of shipping
companies supplying container-based transportation. This option consists in
the distribution of trucks delivering loaded containers to import customers, the
subsequent allocation of empty containers to export customers and the final
dispatch of loaded containers to departure ports. However, the determination of
truck routes is a time-consuming activity for shipping companies, because
available information can suddenly change while they are making their decisions.
In this paper we aim to propose a decision support tool to quickly determine
truck routes and implement the street-turn strategy. This tool is based on an
optimization model determining the allocation of empty containers between
customers and defining truck routes in a post-optimization phase. We compare
routes resulting from the proposed model to the decisions of a real shipping
company. Early results indicate that this approach represents a promising support
for shipping companies in dealing with street-turns. It can significantly reduce
distances travelled by trucks and times requested to determine routes
A meteo-hydrological forecasting chain: performance of the downscaling and rainfall-runoff steps in a small catchment
No full textForecasting ground effects of severe meteorological events with an adequate lead time is fundamental for civil protection scopes and is therefore an important challenge for the scientific community. The paper focuses on the performance of some steps of a meteo-hydrological forecasting chain that can be applied in small watersheds to assess hydrological risk deriving by an intense storm predicted at the large meteorological scale. The proposed procedure integrates large-scale rainfall fields, as those produced by numerical weather prediction (NWP) models, with statistical rainfall downscaling and hydrological modelling. More in details, assuming a large scale rain rate as the input of the process, the forecasting chain produces an ensemble of hydrographs that are post-processed in order to give a probabilistic representation of mean streamflow maxima for different time windows. The outcome of this procedure can be thus applied to assess the risk that some critical streamflow thresholds may be exceeded. The procedure has been tested on more than one thousand recorded events in the Araxisi catchment in Sardinia, Italy. Results and performances are presented and discussed
Artichoke deep learning detection network for site-specific agrochemicals UAS spraying
No full textAbstract
Input optimization is a distinguishing characteristic of Precision Agriculture approaches, helping reduce the environmental impact and costs and increase vegetable production quality. Thanks to the high automation evolution of Unmanned Aerial Systems (UAS), a new approach derived from their combination with Deep Learning techniques is leading to significant improvements in agricultural management practices. The study aims at artichoke plants detection and georeferencing as a first step for an on-the-fly, real time, UAS spraying system, and use the gathered information to monitor crop development through a multi-temporal approach. A commercial UAS, equipped with an RGB sensor, acquired images of the artichoke field located in Sardinia (Italy) during the 2021–2022 season in different crop growth stages. The FPN (Feature Pyramid Network), trained and compared with the YOLOv5 (You Only Look Once) network, showed a high detection level with an average F1 score of around 90%, and satisfactory off-line performances on the Nvidia Jetson Nano board. YOLOv5 achieved the best overall result. The FPN recorded a lower recall, which is desirable to achieve a minimum number of detection errors and limit the leakage of agrochemicals on false-positive targets. The multi-temporal approach influenced detection performances, with an inverse response of precision and recall metrics. The growing index trend showed a distinct value in October, peaking at the beginning of December as expected. The proposed approach contributes to designing future automatic and reliable site-specific UAS agrochemicals application and the classification of management zones
Integrating UAVs and Canopy Height Models in Vineyard Management: A Time-Space Approach
No full textThe present study illustrates an operational approach estimating individual and aggregate vineyards’ canopy volume estimation through three years Tree-Row-Volume (TRV) measurements and remotely sensed imagery acquired with unmanned aerial vehicle (UAV) Red-Green-Blue (RGB) digital camera, processed with MATLAB scripts, and validated through ArcGIS tools. The TRV methodology was applied by sampling a different number of rows and plants (per row) each year with the aim of evaluating reliability and accuracy of this technique compared with a remote approach. The empirical results indicate that the estimated tree-row-volumes derived from a UAV Canopy Height Model (CHM) are up to 50% different from those measured on the field using the routinary technique of TRV in 2019. The difference is even much higher in the two 2016 dates. These empirical findings outline the importance of data integration among techniques that mix proximal and remote sensing in routine vineyards’ agronomic practices, helping to reduce management costs and increase the environmental sustainability of traditional cultivation systems
On the nature of rainfall intermittency as revealed by different metrics and sampling approaches
No full textA general consensus on the concept of rainfall intermittency has not yet been reached, and intermittency is often attributed to different aspects of rainfall variability, including the fragmentation of the rainfall support (i.e., the alternation of wet and dry intervals) and the strength of intensity fluctuations and bursts. To explore these different aspects, a systematic analysis of rainfall intermittency properties in the time domain is presented using high-resolution (1-min) data recorded by a network of 201 tipping-bucket gauges covering the entire island of Sardinia (Italy). Four techniques, including spectral and scale invariance analysis, and computation of clustering and intermittency exponents, are applied to quantify the contribution of the alternation of dry and wet intervals (i.e., the rainfall support fragmentation), and the fluctuations of intensity amplitudes, to the overall intermittency of the rainfall process. The presence of three ranges of scaling regimes between 1 min to ~ 45 days is first demonstrated. In accordance with past studies, these regimes can be associated with a range dominated by single storms, a regime typical of frontal systems, and a transition zone. The positions of the breaking points separating these regimes change with the applied technique, suggesting that different tools explain different aspects of rainfall variability. Results indicate that the intermittency properties of rainfall support are fairly similar across the island, while metrics related to rainfall intensity fluctuations are characterized by significant spatial variability, implying that the local climate has a significant effect on the amplitude of rainfall fluctuations and minimal influence on the process of rainfall occurrence. In addition, for each analysis tool, evidence is shown of spatial patterns of the scaling exponents computed in the range of frontal systems. These patterns resemble the main pluviometric regimes observed on the island and, thus, can be associated with the corresponding synoptic circulation patterns. Last but not least, we demonstrate how the methodology adopted to sample the rainfall signal from the records of the tipping instants can significantly affect the intermittency analysis, especially at smaller scales. The multifractal scale invariance analysis is the only tool that is insensitive to the sampling approach. Results of this work may be useful to improve the calibration of stochastic algorithms used to downscale coarse rainfall predictions of climate and weather forecasting models, as well as the parameterization of intensity-duration-frequency curves, adopted for land planning and design of civil infrastructures
Transect-scale imaging of root zone electrical conductivity by inversion of multiple-height EMI measurements under different salinity conditions
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