Search Results - "Silvia Barbetta"

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  1. 1

    River Discharge Estimation by Using Altimetry Data and Simplified Flood Routing Modeling by Tarpanelli, Angelica, Barbetta, Silvia, Brocca, Luca, Moramarco, Tommaso

    Published in Remote sensing (Basel, Switzerland) (01-09-2013)
    “…A methodology to estimate the discharge along rivers, even poorly gauged ones, taking advantage of water level measurements derived from satellite altimetry is…”
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    Journal Article
  2. 2

    River Bathymetry Estimate and Discharge Assessment from Remote Sensing by Moramarco, Tommaso, Barbetta, Silvia, Bjerklie, David M., Fulton, John W., Tarpanelli, Angelica

    Published in Water resources research (01-08-2019)
    “…The NASA Surface Water Ocean Topography (SWOT) mission and ESA Sentinel‐1, inter‐alia, propose to monitor inland waters from satellites. The radar altimeter…”
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    Journal Article
  3. 3

    Real-time flood forecasting downstream river confluences using a Bayesian approach by Barbetta, Silvia, Coccia, Gabriele, Moramarco, Tommaso, Todini, Ezio

    Published in Journal of hydrology (Amsterdam) (01-10-2018)
    “…•The issue of forecast at gauged sites downstream river confluence is investigated.•The proposed method uses probabilistic forecast at gauged sites on…”
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    Journal Article
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  5. 5

    Significance of hydraulic complexity parameters M1 and M2 based on the laboratory and field data by Bahmanpouri, Farhad, Termini, Donatella, Barbetta, Silvia, Dionigi, Marco, Moramarco, Tommaso

    Published in Hydrology research (01-03-2023)
    “…Hydraulic complexity metrics referred to as M1 and M2 play an important role when it comes to the analysis of habitat metrics. In the present paper, the…”
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    Journal Article
  6. 6

    From Surface Flow Velocity Measurements to Discharge Assessment by the Entropy Theory by Tommaso Moramarco, Silvia Barbetta, Angelica Tarpanelli

    Published in Water (Basel) (01-02-2017)
    “…A new methodology for estimating the discharge starting from the monitoring of surface flow velocity, usurf, is proposed. The approach, based on the entropy…”
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    Journal Article
  7. 7

    Flood Hydrograph Prediction Using Machine Learning Methods by Gokmen Tayfur, Vijay P. Singh, Tommaso Moramarco, Silvia Barbetta

    Published in Water (Basel) (24-07-2018)
    “…Machine learning (soft) methods have a wide range of applications in many disciplines, including hydrology. The first application of these methods in hydrology…”
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  8. 8

    A multilayer soil approach for seepage process analysis in earthen levees by Bonaccorsi, Bianca, Moramarco, Tommaso, Noto, Leonardo Valerio, Barbetta, Silvia

    Published in Journal of flood risk management (01-09-2022)
    “…The analysis of seepage through earthen levees is fundamental to investigate the levee safety condition, since that the seepage phenomenon can often lead to…”
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  9. 9

    Flooding in the Digital Twin Earth: The Case Study of the Enza River Levee Breach in December 2017 by Angelica Tarpanelli, Bianca Bonaccorsi, Marco Sinagra, Alessio Domeneghetti, Luca Brocca, Silvia Barbetta

    Published in Water (Basel) (23-04-2023)
    “…The accurate delineation of flood hazard maps is a key element of flood risk management policy. Flood inundation models are fundamental for reproducing the…”
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    Journal Article
  10. 10

    A methodology for discharge estimation and rating curve development at ungauged river sites by Perumal, Muthiah, Moramarco, Tommaso, Sahoo, Bhabagrahi, Barbetta, Silvia

    Published in Water resources research (01-02-2007)
    “…A methodology for estimating discharges and development of rating curves at ungauged river sites is presented. The methodology employs a routing method, which…”
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    Journal Article
  11. 11

    Impact of animal burrows on earthen levee body vulnerability to seepage by Palladino, Michela R., Barbetta, Silvia, Camici, Stefania, Claps, Pierluigi, Moramarco, Tommaso

    Published in Journal of flood risk management (01-01-2020)
    “…A novel procedure for estimation of the vulnerability to seepage inducing piping processes in earthen levees affected by animal burrows is presented. The…”
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  12. 12

    Case Study: A Real-Time Flood Forecasting System with Predictive Uncertainty Estimation for the Godavari River, India by Silvia Barbetta, Gabriele Coccia, Tommaso Moramarco, Ezio Todini

    Published in Water (Basel) (01-10-2016)
    “…This work presents the application of the multi-temporal approach of the Model Conditional Processor (MCP-MT) for predictive uncertainty (PU) estimation in the…”
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    Journal Article
  13. 13

    A Self-Contained and Automated Method for Flood Hazard Maps Prediction in Urban Areas by Marco Sinagra, Carmelo Nasello, Tullio Tucciarelli, Silvia Barbetta, Christian Massari, Tommaso Moramarco

    Published in Water (Basel) (01-05-2020)
    “…Water depths and velocities predicted inside urban areas during severe storms are traditionally the final result of a chain of hydrologic and hydraulic models…”
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  14. 14

    A Muskingum-based methodology for river discharge estimation and rating curve development under significant lateral inflow conditions by Barbetta, Silvia, Moramarco, Tommaso, Perumal, Muthiah

    Published in Journal of hydrology (Amsterdam) (01-11-2017)
    “…•We present an extension of the VPMS method by involving lateral flow estimate.•The method uses equivalent prismatic sections.•The method can be used for…”
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  15. 15

    Prediction of river discharges at confluences based on Entropy theory and surface-velocity measurements by Bahmanpouri, Farhad, Barbetta, Silvia, Gualtieri, Carlo, Ianniruberto, Marco, Filizola, Naziano, Termini, Donatella, Moramarco, Tommaso

    Published in Journal of hydrology (Amsterdam) (01-03-2022)
    “…•Estimating the discharge rate in Amazon river based on Entropy approach.•Estimating Entropy-based velocity profiles from surface velocity…”
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  16. 16

    Estimating the Average River Cross‐Section Velocity by Observing Only One Surface Velocity Value and Calibrating the Entropic Parameter by Bahmanpouri, Farhad, Eltner, Anette, Barbetta, Silvia, Bertalan, László, Moramarco, Tommaso

    Published in Water resources research (01-10-2022)
    “…The current research aims to predict the velocity distribution and discharge rates in rivers based on the entropy concept using only one surface velocity…”
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    Journal Article
  17. 17

    Investigating hydrodynamics and turbulent effects in rivers for different flow conditions using spatial complexity metrics by Bahmanpouri, Farhad, Termini, Donatella, Barbetta, Silvia, Gualtieri, Carlo, Dionigi, Marco, Moramarco, Tommaso

    Published in Journal of hydrology (Amsterdam) (01-09-2024)
    “…•We investigate the hydraulic complexity metrics based on laboratory and field data.•We study the kinetic energy and coherent structures related to aquatic…”
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  18. 18

    The use of remote sensing-derived water surface data for hydraulic model calibration by Domeneghetti, Alessio, Tarpanelli, Angelica, Brocca, Luca, Barbetta, Silvia, Moramarco, Tommaso, Castellarin, Attilio, Brath, Armando

    Published in Remote sensing of environment (01-06-2014)
    “…Considering a large satellite dataset (i.e. ~16years of ERS-2 and ENVISAT observations) we investigate the reliability of remotely-sensed data for calibrating…”
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  19. 19

    The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting by Barbetta, Silvia, Coccia, Gabriele, Moramarco, Tommaso, Brocca, Luca, Todini, Ezio

    Published in Journal of hydrology (Amsterdam) (01-08-2017)
    “…•We extend the Multi-Temporal Model Conditional Processor to the multi-model case.•We introduce the four Truncated Normal Distributions approach for…”
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  20. 20

    Addressing effective real-time forecasting inflows to dams through predictive uncertainty estimate by Barbetta, Silvia, Sahoo, Bhabagrahi, Bonaccorsi, Bianca, Nanda, Trushnamayee, Chatterjee, Chandranath, Moramarco, Tommaso, Todini, Ezio

    Published in Journal of hydrology (Amsterdam) (01-05-2023)
    “…•The accuracy of five deterministic forecasts of inflows to dam is investigated.•The probabilistic forecast is estimated through the Model Conditional…”
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