Editorial Note &amp;quot;A case of plagiarism&amp;quot;

Guzzetti, Fausto

doi:10.5194/nhess-9-1-2009

Cited by 1 publication

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References 1 publication

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“…Data used as input for the hydrological model are provided by the Italian national gauge network composed of the collection of the regional networks (Molini et al 2009). Data with hourly time step are available for rainfall, air temperature, wind velocity, air humidity and solar radiation.…”

Section: Study Area and Datamentioning

confidence: 99%

Improving real-time operational streamflow simulations using discharge data to update state variables of a distributed hydrological model

et al. 2021

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Reducing errors in streamflow simulations is one of the main issues for a reliable forecast system aimed to manage floods and water resources. Data assimilation is a powerful tool to reduce model errors. Unfortunately, its use in operational chains with distributed and physically based models is a challenging issue since many methodologies require computational times that are hardly compatible with operational needs. The implemented methodology corrects modelled water level in channels and root-zone soil moisture using real-time water level gauge stations. Model's variables are corrected locally, then the updates are propagated upstream with a simple approach that accounts for sub-basins’ contributions. The overfitting issue, which arises when updating a spatially distributed model with sparse streamflow data, is hence here addressed in the context of a large-scale operational implementation working in real time thanks to the simplicity of the strategy. To test the method, a hindcast of daily simulations covering 18 months was performed on the Italian Tevere basin, and the modelling results with and without assimilation were compared. The setup was that currently in place in the operational framework in both cases. The analysis evidences a clear overall benefit of applying the proposed method even out of the assimilation time window.

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Section: Study Area and Datamentioning

confidence: 99%