Premi di Laurea e Dottorato

Il Dipartimento di Ingegneria Civile, Edile e Ambientale (DICEA) dell’Università degli Studi di Napoli Federico II ha recentemente bandito 45 borse di studio per giovani studenti neolaureati e dottori di ricerca che si sono particolarmente distinti per il loro percorso di studi tra quelli laureati negli anni accademici 2017/2018 e 2018/2019 in uno dei seguenti Corsi di Studio Magistrale o Specialistico: Ingegneria dei Sistemi Idraulici e di Trasporto; Ingegneria per l’Ambiente e il Territorio; Ingegneria Edile; Ingegnera Edile-Architettura. Le commissioni di dipartimento hanno selezionato 40 studenti dei corsi di laurea magistrale per il brillante percorso di studi e 5 dottori di ricerca per i risultati di ricerca conseguiti.

Desideriamo menzionare ciascuno dei premiati per questo prestigioso riconoscimento e fargli i migliori auguri per il loro futuro professionale. I premiati, per le lauree magistrali, sono stati: Luigi AUDINO, Angelo AVINO, Ciro BUONOCORE, Gianluigi CIASULLO, Francesco D’AULISIO GARIGLIOTA, Federica DE CHIARA, Luigi DE SIMONE, Salvatore DELLE CAVE, Nicola DI COSTANZO, Marco GIUGLIANO, Flavia GUARRACINO, Sara IANNITTI, Francesco LANZILLO, Carlo Maria MALAFRONTE, Federico MINELLI, Raffaele NAPOLITANO, Cristina ORETO, Roberta PALMIERO, Dina PIRONE, Fabiana RUSSO, Antonio SIESTO, Floriana SIMONELLI, Sara TUOZZO, Luigi VALENTINO, Francesca ANGELLOTTO, Luigi AUDINO, Francesco D’AUSILIO GARIGLIOTTA, Luigi DE SIMONE, Salvatore DELLE CAVE, Valeria GUERRIERO, Nunzia IANUARIO, Francesco LANZILLO, Federico MINELLI, Ennio MOLISSE, Raffaele NATALE, Francesca Pia NIESPOLO, Danila Nicole PAGLIUCA, Maria Rella RICCARDI, Allegra ROMANO, Lucia RUSSO. I Premi di Dottorato sono stati assegnati a: Marilisa BOTTE, Fiore TINESSA, Vincenzo LUONGO, Gerardo CAROPPI e Maria Rosa TREMITERRA.

Giornate di Idrologia Webinar

13 novembre 2020 – 10:00 -13:30

Le scienze idrologiche e le strategie per la mitigazione dei rischi e la tutela dei sistemi naturali https://meet.google.com/cpy-kmrx-xcu

  • 10:00 – 10:20 – Impatto delle forzanti idrologiche nella sicurezza alimentare  – Cristina Rulli 
  • 10:20 – 10:40 –  Le sfide del monitoraggio idrologico e avanzamenti tecnologiciLuca Brocca 
  • 10:40 – 11:00 – Modellazione e previsione idrologica degli eventi estremiGiuseppe Formetta
  • 11:00 – 11:20 – Idrologia e dinamiche degli ecosistemi naturaliGianluca Botter 
  • 11:20 – 13:30  – Discussione

27 novembre 2020 – 10:00 -13:30

Il contributo dell’idrologia tecnica per una società più resiliente ai fenomeni naturali –https://meet.google.com/qms-jtbj-zjo 

10:00 – 10:20 – I sistemi di allertamento nazionali e regionali: questioni aperte  – Fausto Guzzetti (Dirigente Dipartimento Protezione Civile)

10:20 – 10:40 – Impatti e pressioni legati alla gestione delle risorse idriche sui corpi idrici naturali – Maurizio Giugni (Commissario straordinario per la depurazione delle acque)

10:40 – 11:00 – Revisione e aggiornamento delle mappe della pericolosità e del rischio di alluvione e nuovi piani di gestione – Barbara Lastoria (Responsabile Sezione Attuazione Direttiva Acque e Alluvioni ISPRA)

11:00 – 11:20 –  Pratiche di gestione delle risorse idrichePaolo Botti (Direttore del Servizio tutela e gestione delle risorse idriche, vigilanza sui servizi idrici e gestione della siccità – Regione Sardegna)

11 dicembre 2020 – 10:00 -13:30

Il ruolo dell’idrologo nella pratica professionale   – https://meet.google.com/vdu-vtfm-hct 

  • Saluti Presidente SII 
  • Nomina del Socio Onorario 
  • Presentazione Premio Florisa Melone
  • Attività YHS
  • Tavola Rotonda sul ruolo dell’idrologo nella pratica professionale Partecipanti: SII, AdbPO, CNI, CINID, ISPRA, GII, ARPAE-ER 

Istruzioni su http://www.sii-ihs.it/eventi.php?open=158&pb=home

Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation

River streamflow monitoring is currently facing a transformation due to the emerging of new innovative technologies. Fixed and mobile measuring systems are capable of quantifying surface flow velocities and discharges, relying on video acquisitions. This camera-gauging framework is sensitive to what the camera can “observe” but also to field circumstances such as challenging weather conditions, river background transparency, transiting seeding characteristics, among others. This short communication paper introduces the novel idea of optimising image velocimetry techniques selecting the most informative sequence of frames within the available video. The selection of the optimal frame window is based on two reasonable criteria: i) the maximisation of the number of frames, subject to ii) the minimisation of the recently introduced dimensionless seeding distribution index (SDI). SDI combines seeding characteristics such as seeding density and spatial clustering of tracers, which are used as a proxy to enhance the reliability of image velocimetry techniques. Two field case studies were considered as a proof-of-concept of the proposed framework, on which seeding metrics were estimated and averaged in time to select the proper application window. The selected frames were analysed using LSPIV to estimate the surface flow velocities and river discharge. Results highlighted that the proposed framework might lead to a significant error reduction. In particular, the computed discharge errors, at the optimal portion of the footage, were about 0.40% and 0.12% for each case study, respectively. These values were lower than those obtained, considering all frames available.

How to cite: Pizarro, A., S. F. Dal Sasso, S. Manfreda, Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation, Hydrological Processes, (doi: 10.1002/hyp.13919 ), 2020.

Seeding Distribution Index (SDI)

The code for the estimation of the Seeding Distribution Index (#SDI) is now available online on OSF. The SDI can help in the identification of the optimal frame window for image-velocimetry applications.
See osf.io/8egqw/

References

Pizarro, A., Dal Sasso, S. F., Manfreda, S., & Perks, M. T. (2020, September 17). Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow. https://doi.org/10.17605/OSF.IO/8EGQW

Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation, Hydrological Processes, (in press), 2020.

Pizarro, A., S.F. Dal Sasso, M. Perks and S. Manfreda, Spatial distribution of tracers for optical sensing of stream surface flow, Hydrology and Earth System Sciences, (in press) 2020.

A Geostatistical Approach to Map Near-Surface Soil Moisture Through Hyperspatial Resolution Thermal Inertia

Thermal inertia has been applied to map soil water content exploiting remote sensing data in the short and long wave regions of the electromagnetic spectrum. Over the last years, optical and thermal cameras were sufficiently miniaturized to be loaded onboard of unmanned aerial systems (UASs), which provide unprecedented potentials to derive hyperspatial resolution thermal inertia for soil water content mapping. In this study, we apply a simplification of thermal inertia, the apparent thermal inertia (ATI), over pixels where underlying thermal inertia hypotheses are fulfilled (unshaded bare soil). Then, a kriging algorithm is used to spatialize the ATI to get a soil water content map. The proposed method was applied to an experimental area of the Alento River catchment, in southern Italy. Daytime radiometric optical multispectral and day and nighttime radiometric thermal images were acquired via a UAS, while in situ soil water content was measured through the thermo-gravimetric and time domain reflectometry (TDR) methods. The determination coefficient between ATI and soil water content measured over unshaded bare soil was 0.67 for the gravimetric method and 0.73 for the TDR. After interpolation, the correlation slightly decreased due to the introduction of measurements on vegetated or shadowed positions (r² = 0.59 for gravimetric method; r² = 0.65 for TDR). The proposed method shows promising results to map the soil water content even over vegetated or shadowed areas by exploiting hyperspatial resolution data and geostatistical analysis.

How to cite: Paruta, A., P. Nasta, G. Ciraolo, F. Capodici, S. Manfreda, N. Romano, E. Bendor, Y. Zeng, A. Maltese, S. F. Dal Sasso and R. Zhuang, A geostatistical approach to map near-surface soil moisture through hyper-spatial resolution thermal inertia, IEEE Transactions on Geoscience and Remote Sensing, (doi: 10.1109/TGRS.2020.3019200) 2020. [pdf]

Predictive modelling of envelope flood extents using geomorphic and climatic‐hydrologic catchment characteristics

A topographic index (flood descriptor) that combines the scaling of bankfull depth with morphology was shown to describe the tendency of an area to be flooded. However, this approach depends on the quality and availability of flood maps and assumes that outcomes can be directly extrapolated and downscaled. This work attempts to relax these problems and answer two questions: 1) Can functional relationships be established between a flood descriptor and geomorphic and climatic‐hydrologic catchment characteristics? 2) If so, can they be used for low‐complexity predictive modelling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high‐resolution flood modelling results as training benchmarks, and on catchment characteristics. Elementary catchments of four river basins in Europe (Thames, Weser, Rhine and Danube) serve as training dataset, while those of the Rhône river basin in Europe serve as testing dataset. Two return periods are considered, the 10‐ and 10,000‐year. Prediction of envelope flood extents and flood‐prone areas show that both models achieve high hit rates with respect to testing benchmarks. Average values were found to be above 60% and 80% for the 10‐ and the 10,000‐year return periods, respectively. In spite of a moderate to high false discovery rate, the critical success index value was also found to be moderate to high. It is shown that by relating classification outcomes to catchment characteristics the prediction of envelope flood extents may be achieved for a given region, including ungauged basins.

ow to cite: Tavares da Costa, R., S. Zanardo, S. Bagli, A. G. J. Hilberts, S. Manfreda, C. Samela, and A. Castellarin, Predictive modelling of envelope flood extents using geomorphic and climatic-hydrologic catchment characteristics, Water Resources Research, (doi: 10.1029/2019WR026453), 2020.

Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides Occurrence

Rainfall-triggered shallow landslide events have caused losses of human lives and millions of euros in damage to property in all parts of the world. The need to prevent such hazards combined with the difficulty of describing the geomorphological processes over regional scales led to the adoption of empirical rainfall thresholds derived from records of rainfall events triggering landslides. These rainfall intensity thresholds are generally computed, assuming that all events are not influenced by antecedent soil moisture conditions. Nevertheless, it is expected that antecedent soil moisture conditions may provide critical support for the correct definition of the triggering conditions. Therefore, we explored the role of antecedent soil moisture on critical rainfall intensity-duration thresholds to evaluate the possibility of modifying or improving traditional approaches. The study was carried out using 326 landslide events that occurred in the last 18 years in the Basilicata region (southern Italy). Besides the ordinary data (i.e., rainstorm intensity and duration), we also derived the antecedent soil moisture conditions using a parsimonious hydrological model. These data have been used to derive the rainfall intensity thresholds conditional on the antecedent saturation of soil quantifying the impact of such parameters on rainfall thresholds.

Geographical distribution of the weather stations and landslide events for the study area. The graph in the inset shows the monthly distribution of landslides in Basilicata from 2001 to 2018.

How to cite: Lazzari, M., M. Piccarreta, R. L. Ray and S. Manfreda, Modelling antecedent soil moisture to constrain rainfall thresholds for shallow landslides occurrence, Landslides edited by Dr. Ram Ray, IntechOpen, pp. 1-331, (10.5772/intechopen.92730) 2020. [Link]

Towards harmonisation of image velocimetry techniques for river surface velocity observations

Since the turn of the 21st century, image-based velocimetry techniques have become an increasingly popular approach for determining open-channel flow in a range of hydrological settings across Europe and beyond. Simultaneously, a range of large-scale image velocimetry algorithms have been developed that are equipped with differing image pre-processing and analytical capabilities. Yet in operational hydrometry, these techniques are utilised by few competent authorities. Therefore, imagery collected for image velocimetry analysis (along with reference data) is required both to enable inter-comparisons between these differing approaches and to test their overall efficacy. Through benchmarking exercises, it will be possible to assess which approaches are best suited for a range of fluvial settings, and to focus future software developments. Here we collate and describe datasets acquired from seven countries across Europe and North America, consisting of videos that have been subjected to a range of pre-processing and image velocimetry analyses (Perks et al.2020https://doi.org/10.4121/uuid:014d56f7-06dd-49ad-a48c-2282ab10428e). Reference data are available for 12 of the 13 case studies presented, enabling these data to be used for reference and accuracy assessment.

How to cite: Perks, M. T., Dal Sasso, S. F., Hauet, A., Jamieson, E., Le Coz, J., Pearce, S., Peña-Haro, S., Pizarro, A., Strelnikova, D., Tauro, F., Bomhof, J., Grimaldi, S., Goulet, A., Hortobágyi, B., Jodeau, M., Käfer, S., Ljubičić, R., Maddock, I., Mayr, P., Paulus, G., Pénard, L., Sinclair, L., and Manfreda, S.: Towards harmonisation of image velocimetry techniques for river surface velocity observations, Earth Syst. Sci. Data, 12, 1545–1559, https://doi.org/10.5194/essd-12-1545-2020, 2020. [pdf]

Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania

Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability.

How to cite: Albano, R.; Samela, C.; Crăciun, I.; Manfreda, S.; Adamowski, J.; Sole, A.; Sivertun, Å.; Ozunu, A. Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania. Water 202012, 1834.

Special Issue “Global Gridded Soil Information Based on Machine Learning”

Dear Colleagues,


Recent technological advances in both remote sensing and soil mapping approaches and progress in establishing harmonized soil profile datasets have opened up the potential to derive global gridded soil information. This has been possible because worldwide researchers have gained a growing experience in building standardized soil profile datasets with measured physical, chemical data and taxonomical information; filling data gaps; using Earth observation data for soil mapping; optimizing soil sampling strategy; processing big data; applying machine learning algorithms; and assessing uncertainty; which support the preparation of global soil maps with increasing accuracy and spatiotemporal resolution.

Data-intensive computing solutions to process and analyze the exploding amount of environmental information are continuously updated. Machine learning algorithms are among the most frequently used tools for data preprocessing and describing the complex relationship between soil properties and environmental covariates with the ability to assess the uncertainty of the predictions. One of the greatest challenges in deriving global gridded soil information is to make the most of the predictive power of machine learning algorithms with the continuously increasing amount of environmental information. This Special Issue is dedicated to machine learning-based methods in:

  • proximal and digital global mapping of soil properties (e.g., basic, hydraulic, thermal, functional, ecosystem services);
  • computing systems/algorithms/approaches using Earth observation data to derive global gridded soil datasets;
  • preprocessing Earth observation data to feed into global soil mapping;
  • data-intensive computing methods for incorporating Earth observation data for predictive soil mapping;
  • optimizing temporal resolution to globally track the changes of soil properties,
  • uncertainty assessment of the derived gridded soil information;
  • specifying algorithms to local soil specificities in, e.g., proximal soil mapping;
  • the engagement of remote sensing data with digital soil mapping;
  • downscaling of large-scale soil feature;
  • other related topics.

Review contributions on the abovementioned topics are welcomed as well.Dr. Brigitta Szabó (Tóth)
Prof.Dr. Eyal Ben-Dor
Dr. Yijian Zeng
Prof.Dr. Salvatore Manfreda
Dr. Madlene Nussbaum
Guest Editors

Keywords

  • Global gridded soil information
  • Predictive soil mapping
  • Uncertainty assessment
  • Spectral data
  • Parallel distributive platforms
  • Machine learning
  • Digital soil mapping